So, you’re knee-deep in data, spreadsheets, and customer feedback forms, trying to make sense of it all. You’re not alone. In today’s data-driven world, understanding your customer is the name of the game. But let’s be real—traditional methods like surveys, polls and focus groups are starting to feel a bit, well, stale. And don’t even get me started on the maze of data privacy laws like GDPR and CCPA that make collecting customer info feel like navigating a legal minefield.

Enter quiz surveys. Think of them as the Swiss Army knife in your marketing toolkit. They’re versatile, engaging, and—here’s the kicker—totally above board when it comes to data privacy laws. And it’s not just big names like Sephora, Warby Parker, and Honda. Small businesses like consultants and even local car dealers are using quiz surveys these days.

Why? Because quiz surveys are not just about collecting data; they’re about engaging your customer in a way that feels natural and even fun.

We’re about to dive into how you can unlock the power of quiz surveys to boost customer engagement, segment your audience like a pro, and stay on the right side of those pesky data privacy laws.

The Rise of Quiz Surveys: Not Just Data Collection, It’s Customer Service

Forget what you’ve been told about data collection being a one-sided affair that benefits only the marketer. Survey surveys are flipping the script, turning data collection into an engaging customer experience. Think of them as your online, non-pushy salesperson who’s there to help, not just sell.

The Customer Love Affair with Quiz Surveys

Customers love these quiz surveys. Why? Because it’s not just about answering questions; it’s about receiving personalized and valuable insights in return. It’s like having a genuine conversation with a brand, one that makes you feel truly heard and catered to.

Here’s the kicker: this approach is so seamless that customers often don’t even realize they’re giving away data.

And that’s a good thing. Because how can you recommend the perfect product without asking the right questions? It’s a win-win: customers get personalized recommendations, and brands get invaluable data.

Why Quiz Surveys are Effective

So you’re intrigued by quiz surveys, but you’re probably wondering, “What’s in it for me?” Well, let me tell you, these bad boys are more than just a fun diversion. They’re a triple threat in the marketing world. Here are just four big benefits you’ll get from quiz surveys:

Audience Segmentation: Uncover Psychographics, Motovations, and Interests

Quiz surveys aren’t just about gathering basic demographic information such as age, gender, or location. They take it a step further, helping you tap into the rich vein of psychographics and preferences.

You start to understand your customers’ attitudes, values, interests, and lifestyle choices, offering a deeper insight into what truly drives their buying behavior.

This not only improves customer engagement but also refines your marketing efforts, enabling you to craft personalized campaigns that resonate with your audience.

Boosted Engagement: A Two-Way Street

Quiz surveys aren’t just data collection tools—they’re conversational catalysts. By designing your quiz survey to emulate a real conversation, you can open up a dialogue with your customers that’s both insightful and engaging.

Use looping features to tailor the journey of each customer, creating a customized experience that feels personal and authentic. The beauty of a well-crafted quiz survey is that it doesn’t just end when the customer hits ‘submit’. Each answer can guide future conversations, allowing you to delve deeper into customers’ interests and needs over time, and not only inform your marketing strategy but also foster a stronger, more meaningful relationship with your customers.

Lead Generation: The Magnet Effect

Let’s get down to brass tacks—leads are the lifeblood of any marketing strategy. But how do you collect them without being pushy? This is where quiz surveys really shine. They’re like that charismatic person at the party who everyone wants to talk to. Before you know it, you’ve got names, emails, and maybe even some phone numbers. All willingly given, of course.

Feedback Mechanism: The Two-Way Street

Most marketing tools are like a monologue, but a quiz survey is a dialogue. You’re not just collecting data; you’re giving back valuable insights or recommendations. It’s like asking your customers, “What do you think?” and then actually using that info to improve or tailor your services.

Navigating the Data Privacy Maze with Quiz Surveys

Alright, let’s talk about the razor’s edge of Data Privacy laws like GDPR, CCPA, you name it. 

Data privacy is becoming increasingly important to marketers in today’s digital landscape. Businesses are under growing pressure from customers and regulators to protect user data, which means marketers must be up-to-date with current trends in data privacy. Here are some of the key trends impacting data privacy for marketers today:

Adoption of GDPR and CCPA Regulations 

The EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have set a new standard for consumer data protection. These regulations require businesses to provide users with control over their personal information, as well as transparency regarding how it is collected and used. Marketers must become familiar with these regulations and ensure that any data collection activities comply with them.

Increased Emphasis on Consent 

Marketers should be aware of the need for explicit consent from consumers whenever collecting or using their personal information. This includes providing detail about what specific data is being collected, how it will be used, if it will be shared with third parties, and how long it will be stored before deletion.

Use of Privacy-Enhanced Technologies 

To help reduce the risks associated with collecting user data, many organizations are turning to privacy-enhanced technologies such as encryption, anonymization, pseudonymization and tokenization. These techniques can help ensure that customer data remains secure while allowing marketers to continue leveraging this valuable asset in their campaigns.

Greater Focus on Security Practices 

Alongside using privacy-enhanced technologies, businesses should also focus on implementing robust security practices throughout their organization to minimize the chances of a data breach occurring. This includes developing processes such as user access control systems and regular security audits/reviews to identify potential vulnerabilities or areas of improvement within an organization’s digital infrastructure.

How to Keep Your Quiz Surveys are Privacy Friendly

Privacy, privacy, everywhere. Consumers are extremely sensitive to how their data is used. In fact, 73% of consumers say that they will stop doing business with a company that misuses their data! Yes, this is serious, but it’s nothing you can’t handle.

Here are a few things you can do — and the good news is that your organization probably has much of this in place already.

1. Get explicit consent from users before collecting any personal data. Some of the best (and easiest ways) to get explicit consent is to have them opt-in with their email to get their results. If you’re using the quiz to help them choose a product, you can include language that tells them how you’re going to use their information.

2. Use encryption techniques to protect any sensitive information collected in a quiz. If you have SSL enabled, you have some of this covered. Depending on the kind of information you’re collecting, you may need to use more secure methods such as 2-step authentication.

3. Provide participants with a way to opt-out of their data being used for marketing or advertising purposes. You can do this by simply adding an extra check box to an opt-in form where they give you their email to get results, but opt-out of having their email used for ads.

4. Make sure all user data is stored securely and not shared with third parties without permission.

5. Provide clear notification of how long the collected data will be retained, and delete it when no longer necessary.

6. Make sure that the quiz follows any applicable international laws concerning online privacy, such as GDPR if you collect data from EU citizens.

But don’t let any of this disway you from using quiz surveys.  Most of these data and privacy protection practices are standard for businesses of all sizes.

The Pulsating Heartbeat of Data: Translating Quiz Surveys into Actionable Insights

The beauty of quiz survey data is that it’s as close as you can get to living, breathing data in the digital realm. These aren’t just cold, hard facts and figures, but dynamic snapshots of your audience’s beliefs, preferences, and desires.

By delving into the nuances of each quiz response, you’re not just analyzing data—you’re understanding people. Now, let’s discover how to decode this life-like data and transform it into actionable insights that drive your business forward.

Integrate Insights With Your CRM and Analytics Platforms

The first step in maximizing the value of your quiz survey data is integrating it with your existing CRM and analytics platforms like Salesforce, HubSpot, or Google Analytics.

Doing this allows you to create a 360-degree view of your customer by enriching profiles with quiz responses. To make this happen, identify the data points you want to sync, such as email addresses, quiz scores, or product preferences. Then, work with your IT team to establish a secure data pipeline between the quiz platform and your CRM. Once this is in place, you can trigger automated workflows, segment your audience more effectively, and even personalize future marketing campaigns based on quiz results.

Leverage Machine Learning and AI for Predictive Customer Insights

Once your data is integrated, you can take your analytics to the next level by employing machine learning and AI algorithms. These can predict future customer behaviors based on their quiz responses.

To leverage this, work with your data science team to develop predictive models. For example, you could predict which products a customer is likely to purchase next based on their quiz answers. These predictive insights can be incredibly powerful for optimizing your marketing strategies, from email campaigns to product recommendations.

Open-ended questions in your quiz can provide qualitative insights that are rich but hard to quantify. This is where sentiment analysis comes in. It uses natural language processing to gauge the mood of the respondent. To implement this, use text analytics tools that offer sentiment analysis features. Import the open-ended responses into these tools to get a sentiment score.

Understanding the sentiment behind your customers’ responses can help you tailor your messaging, improve products, or even identify potential brand advocates.

Set Up Real-Time Dashboards and Alerts for Immediate Insights

Real-time analytics dashboards offer a live view into how your quiz is performing. This enables you to make data-driven decisions on the fly, whether it’s tweaking the quiz in real-time or adjusting your marketing strategies.

(Caption: Dashboard from Bucket.io quiz funnel platform.)

To set this up, use dashboard tools that offer real-time analytics and can integrate with your quiz platform. Monitor key performance indicators like completion rates, average time spent, and lead generation metrics. With real-time insights, you can immediately see what’s working and what needs adjustment, allowing for agile decision-making that can significantly impact your ROI.

You can’t be glued to your dashboard 24/7. Setting up alerts for key metrics ensures that you’re instantly notified when something significant happens—like a sudden drop in completion rates or a spike in traffic.

Most analytics dashboards allow you to set up custom alerts. Identify the metrics that are most crucial for your quiz’s success and set up alerts for any significant changes in these numbers. By doing this, you’re always in the loop, able to react quickly to any opportunities or issues, keeping your quiz survey strategy on the path to success.

Scaling Your Quiz Surveys Across the Organization

In an enterprise setting, the power of quiz surveys extends far beyond just the marketing department. They can be a versatile tool that benefits multiple departments, adapts to international audiences, and can be easily managed with the right training. Let’s explore how to scale your quiz surveys effectively across your organization.

Deploy Quizzes Across Multiple Departments

Quiz surveys aren’t just for the marketing team. They can be a versatile tool that benefits multiple departments within your organization. For instance, HR can use them for employee engagement or skills assessment, Sales can deploy them to qualify leads more effectively, and Customer Service can use them to gather customer feedback or troubleshoot common issues.

To get started, collaborate with department heads to identify their specific needs and objectives. Once you’ve got that down, tailor your quiz surveys to meet these unique requirements. For example, HR might need a personality quiz to assess cultural fit, while Sales might require a product knowledge quiz to qualify leads.

The key here is to have a centralized quiz survey platform that allows for customization and role-based access. This ensures that each department can tailor quizzes to their needs while adhering to brand guidelines and data privacy regulations.

Localize Quizzes for Global Reach

In today’s global marketplace, your quiz surveys need to speak the language of your diverse customer base. Localizing your quiz not only means translating the content but also adapting cultural references, units of measurement, and even the tone to resonate with different audiences.

Start by identifying your key international markets and then work with native speakers or localization experts to adapt your quiz content. Make sure to also adapt any images, videos, or other media that might be included in the quiz.

Don’t forget to test the localized versions thoroughly to ensure that nothing is lost in translation. Use A/B testing to compare the performance of the localized quiz against the original, adjusting as necessary to maximize engagement and completion rates.

Train Teams for Effective Quiz Management

As you scale your quiz surveys across departments and geographies, it’s crucial to have a well-trained team that can manage them effectively. This involves not just the technical aspects of setting up a quiz but also understanding best practices for question design, data analysis, and compliance.

Start with a training program that covers the basics—how to use the quiz platform, how to set up different types of questions, and how to analyze the data. You can create a series of training modules or even a certification program to ensure that team members are well-equipped to manage quizzes.

But don’t stop at the basics. Keep your team updated with regular training sessions on new features, advanced analytics techniques, and changes in data privacy laws. This ensures that your team is always at the forefront of quiz survey best practices, ready to extract maximum value from this versatile tool.

Personalization and Automation: Streamlining Quiz Surveys for Individual Engagement

In an age where customers expect personalized experiences, quiz surveys offer a goldmine of data that can be used to tailor future interactions. But how do you go from a set of quiz responses to a personalized customer journey? And how can you automate this process for maximum efficiency and impact? Let’s dive in.

(Caption: Typical quiz survey funnel that shows the flow from traffic source to segmented, personalized campaigns. )

Personalize Customer Interactions Based on Quiz Data

The data collected from quiz surveys can be a treasure trove for personalizing future customer interactions. For example, if a customer’s quiz responses indicate a strong interest in sustainable products, you can tailor your email campaigns to highlight your eco-friendly offerings.

To implement this, integrate your quiz platform with your email marketing software. Use tagging or segmentation features to categorize quiz participants based on their answers. Then, create personalized email campaigns targeting these specific segments. The goal is to make each customer feel like your brand truly understands them, increasing both engagement and conversion rates.

Automate Marketing Processes for Efficiency

Automation is the key to scaling your personalized marketing efforts without overwhelming your team. Use the quiz data to trigger automated workflows in your marketing automation platform. For instance, if a quiz identifies a participant as a hot lead, you can automatically enroll them in a targeted drip email campaign.

To set this up, define the criteria that will trigger different workflows. Then, create the content for each automated sequence, ensuring it aligns with the insights gained from the quiz. This way, you’re not just collecting data for the sake of it; you’re using it to automate and streamline your marketing processes, making your team more efficient and your campaigns more effective.

Leverage Quiz Data for Product Development and Strategy

Beyond marketing, quiz data can also inform product development and overall business strategy. If a significant portion of quiz takers express interest in a particular feature or product category, that’s a strong signal to consider it in your development roadmap.

Compile the data into actionable reports and share them with your product development and strategy teams. The insights gained from quiz surveys can help prioritize features, identify gaps in the market, and even inspire entirely new product lines, making your business more responsive to customer needs and market trends.

Community Building: Leveraging Quiz Surveys for Brand Advocacy and Loyalty

In today’s digital landscape, building a community around your brand can be a game-changer. It’s not just about making a sale; it’s about creating lasting relationships that turn customers into brand advocates. Quiz surveys can play a pivotal role in this, serving as a unique engagement tool that fosters community, loyalty, and advocacy. Let’s explore how.

(Caption: QuestionPro’s Poll and Quiz features can help you build an engaged community)

Turn Quiz Participants into Brand Advocates

One of the most potent ways to build a community is to turn your customers into advocates who willingly promote your brand. Quiz surveys can be a stepping stone for this. For instance, you can design a quiz that identifies how well participants know your brand or products. Those who score high could be invited to become part of an exclusive brand ambassador program.

To implement this, set up automated emails that are triggered based on quiz scores. High scorers receive an invitation to join your brand ambassador program, complete with perks like exclusive discounts, early access to new products, or even commission on sales they refer. This not only rewards your most engaged customers but also turns them into vocal advocates for your brand.

Foster Loyalty Through Exclusive Content and Offers

Loyalty programs are a tried-and-true method for retaining customers, and quiz surveys can add a personalized touch. Based on quiz results, you can offer participants exclusive content or special offers tailored to their interests or needs.

For example, if your quiz identifies a segment of customers who are fitness enthusiasts, you could offer them an exclusive eBook on “10 Pro Workouts for the Busy Professional.” The key is to make the offer so relevant and valuable that it cements their loyalty to your brand.

Engage Your Community Through Social Sharing and Challenges

Social media is a powerful tool for community building, and quiz surveys can naturally encourage social sharing. Design your quiz with shareable results that participants will want to post on their social media profiles. You can even create challenges or competitions around your quiz to encourage more participation and sharing.

To get started, include social sharing buttons on the quiz results page and incentivize sharing by offering additional rewards or entries into a competition. Monitor social media for shares and engage with participants, thanking them for taking part and sharing. This not only amplifies the reach of your quiz but also fosters a sense of community and engagement among participants.

ROI: Measuring the Financial Impact

In the enterprise world, every marketing activity needs to justify its existence by showing a positive return on investment (ROI). Quiz surveys are no exception. But how do you go beyond surface-level metrics like engagement rates to actually prove that your quizzes are contributing to the bottom line? Let’s break it down.

Conduct a Cost-Benefit Analysis for Your Quizzes

The first step in understanding the ROI of your quiz surveys is to conduct a thorough cost-benefit analysis. This involves calculating the total cost of creating, deploying, and managing your quizzes and then comparing it to the benefits gained, such as leads generated, sales conversions, or customer retention rates.

Start by itemizing all the costs involved, from the quiz platform subscription fees to the man-hours spent on creating and managing the quizzes. Don’t forget to include any third-party integrations or advanced analytics tools you might be using.

Next, quantify the benefits. This could be in the form of leads generated, products sold, or even customer satisfaction scores. Convert these into monetary values wherever possible, and compare them against your total costs. The resulting figure gives you a clear ROI metric that you can use to justify the investment in quiz surveys.

Assess the Long-term Value of Quiz-Driven Engagement

While immediate ROI is important, don’t overlook the long-term value that quiz surveys can bring. They can significantly contribute to customer lifetime value (CLV) by not only attracting new customers but also increasing engagement and loyalty among existing ones.

To measure this, track how quiz participants interact with your brand over time. Are they more likely to open your emails? Do they spend more on your products? Do they refer new customers to your brand? All these actions contribute to CLV.

Use analytics tools to track these behaviors over a set period, and compare the CLV of quiz participants against a control group that didn’t take the quiz. This will give you a more holistic view of the long-term financial impact of your quiz surveys.

Future-Proofing: Adapting Quiz Surveys to Emerging Trends and Technologies

In the fast-paced world of digital marketing, what works today might be obsolete tomorrow. As a marketing or market research manager, you need to be ahead of the curve to ensure that your quiz surveys continue to deliver value. Let’s explore how to future-proof your quiz survey strategy.

Stay Updated with Industry Trends and Best Practices

The first step in future-proofing your quiz surveys is to stay abreast of industry trends and best practices. Whether it’s new types of interactive content, advances in AI and machine learning, or shifts in consumer behavior, being in the know allows you to adapt your quizzes accordingly.

Subscribe to industry newsletters, follow thought leaders on social media, and attend webinars or conferences focused on interactive content and data-driven marketing. Make it a habit to review and update your quiz strategy at least once a quarter to incorporate any new insights or technologies.

Integrate Emerging Technologies for Enhanced Engagement

As new technologies like augmented reality (AR), virtual reality (VR), or chatbots become more mainstream, consider how they can be integrated into your quiz surveys for a more engaging experience. For example, a furniture store could use AR within a quiz to allow customers to virtually place furniture items in their home before making a purchase decision.

Start by identifying which emerging technologies align with your brand and target audience. Then, work with your tech team or external vendors to pilot these features within your quizzes. Measure their impact on engagement and ROI, and refine your approach based on the data.

Prepare for Platform and Algorithm Changes

Search engines, social media platforms, and even quiz software are constantly updating their algorithms and features. These changes can have a direct impact on how your quizzes are discovered and engaged with by your audience.

To mitigate this, diversify how you distribute your quizzes. Don’t rely solely on one channel like Facebook or Google. Use a multi-channel approach that includes email, social media, paid ads, and even partnerships with other brands or influencers. This not only increases your reach but also insulates you from the impact of any single platform’s changes.

By staying updated on industry trends, integrating emerging technologies, and preparing for platform changes, you can ensure that your quiz surveys remain an effective and engaging tool for years to come. Future-proofing your strategy in this way means you’re not just reacting to the market, but proactively adapting to shape it.

Optimize Quiz Strategies for Maximum ROI

While initial setup and deployment are crucial, the real magic happens when you continually optimize your quiz strategies for better ROI. Think of your quiz surveys as living, breathing entities that can be tweaked and improved upon, rather than set-and-forget campaigns.

Start by regularly reviewing the analytics and performance metrics of your quizzes. Are there questions that consistently see high drop-off rates? Are certain answers correlated with higher customer lifetime value? Use these insights to refine your questions, answer choices, and even the quiz flow.

But don’t just stop at the quiz itself. Look at the entire customer journey. For example, are the leads generated by the quiz being effectively nurtured through email campaigns or retargeting ads? If not, you might need to tweak your post-quiz marketing strategies for better conversion rates.

Lastly, make it a habit to A/B test different elements of your quiz, from the questions and answer options to the design and CTA buttons. Even small changes can lead to significant improvements in ROI. By adopting a culture of continuous optimization, you ensure that your quiz surveys are always performing at their best, maximizing your return on investment.

The Quizmaster’s Playbook: Your Blueprint for Success

So, you’ve journeyed through the ins and outs of quiz surveys, from understanding their rising popularity to leveraging them for ROI, community building, and even future-proofing your strategy. It’s a lot to take in, but the beauty of quiz surveys is that they’re as simple or as complex as you make them. Whether you’re a marketing manager or a market research pro, the power of quizzes is now in your hands.

First off, let’s talk about customer engagement. Gone are the days when quizzes were just a fun gimmick. Today, they’re a robust tool for diving deep into customer psychographics, segmenting your audience, and even serving as a two-way street for customer feedback. The data you collect isn’t just a one-off; it’s the beginning of a conversation, a long-term relationship with your customer that can be nurtured and grown.

But what’s data without action? We delved into the nitty-gritty of turning quiz data into actionable insights. Integration with your existing CRM and analytics platforms is key here. And let’s not forget the role of advanced analytics and real-time reporting in making your data work for you. The future is in machine learning and AI, so don’t get left behind.

Then comes the ROI. Every marketing activity needs to justify its existence, and quiz surveys are no different. From conducting a cost-benefit analysis to understanding the long-term value and continually optimizing for maximum ROI, the financial aspect of quiz surveys can’t be ignored. They’re not just an engagement tool; they’re a revenue generator.

Community building is the secret sauce that can turn your brand from a one-hit-wonder to a long-lasting hit. Quizzes can help you turn customers into advocates, foster loyalty through exclusive content, and even engage your community through social sharing and challenges. In a world where customer loyalty is hard to come by, quizzes offer a unique way to build a community around your brand.

And let’s not forget about future-proofing. The digital landscape is ever-changing, and your quiz strategy needs to adapt to stay relevant. Whether it’s staying updated with industry trends, integrating emerging technologies, or preparing for platform changes, a proactive approach is what will set you apart from the competition.

So, what’s next? It’s time to put these insights into action. Start small if you have to, but start. Experiment with different types of quizzes, measure their impact, and refine your strategy as you go along. The road to quiz mastery is a journey, not a destination. And with this playbook in hand, you’re well on your way to becoming a quizmaster extraordinaire.

The post Unlocking Customer Insights While Respecting Privacy: The Power of Quiz Surveys appeared first on The Experience Journal by QuestionPro.

About the Author:

Nick Jain, CFA is the CEO of IdeaScale, a top player in the innovation software industry. As the chief exec, Nick’s role is multi-faceted: he sets the company’s strategy, oversees its implementation, and ensures that the entire operation aligns with those goals. His leadership is crucial in a tech sector that’s always changing. He’s not just the guy behind the desk; he’s also the face of IdeaScale, often interacting with key stakeholders and clients. Under his guidance, IdeaScale focuses on delivering high-quality, user-friendly platforms that encourage creativity and problem-solving within organizations. Overall, Nick’s leadership aims to keep IdeaScale at the forefront of innovation software.

Organizations are constantly looking for ways to improve productivity. That’s where Generative AI comes in. It’s so revolutionary that, just 6 months after being “invented”, it’s already reshaping how humanity works.

Not only does GenAI save time (the most obvious use case), it also helps unleash human-led creativity and decision-making (the more powerful use case). Let’s take a quick spin through how GenAI is driving automation, both to save costs and to drive superior decision-making, and make possible entirely new business models.

Time is Precious: Let AI Handle the Mundane Tasks

Generative AI is a game-changer to reclaim valuable time previously lost to mundane, low value-add tasks such as writing lower-order emails. Think about those laborious hours spent on tasks like refining (“wordsmithing”) emails, blog posts, and marketing collateral. Tasks that once devoured countless human hours can now be automated for 0.1% of the cost of human labor – a 1000x increase in productivity!

Consider content creation, for instance. In the past, crafting high-quality written materials demanded extensive human effort by extremely skilled (and expensive) content experts. Today, AI-driven content generators craft compelling articles, reports, and marketing materials with remarkable precision and speed. This not only saves time but ensures unwavering consistency in messaging, fortifying brand identity.

As a result, businesses can have fewer people doing marketing content, and more people building or selling products. The workforce can now focus on endeavors that demand uniquely human traits—insight, intuition, and emotional intelligence—rather than grinding through repetitive jobs.

Cost Efficiency: Automating for Financial Gains

Generative AI not only saves time but also offers substantial cost-reduction opportunities. Automation is a powerful cost-cutting tool, streamlining the handling of low-level tasks that would otherwise necessitate human intervention.

Take customer support, a vital yet resource-intensive function. Chatbots, driven by generative AI, proficiently manage routine customer inquiries, allowing human agents to address the complicated stuff that inevitability arises. This not only slashes labor costs but gives greater better customer service (i.e., since customer gets easy questions answered immediately by bots, and giving more time to customer service staff to handle the complicated issues with white-glove services).

All these savings can then be reinvested into higher ROI-areas, increasing your business’ growth. Imagine what Google could do with a 1% savings on its $200 BILLION of revenues? Or about the infrastructure improvements the US Federal Highway Administration could complete that they don’t have the budget to do currently?

Empowered Decision-Making: AI as Your Trusted Ally

Generative AI isn’t just about automation – it can collaborate with humans to make better decisions. It unlocks access to knowledge and insights that were once locked away in vast data repositories. Just as Wikipedia was a step-change improvement in information access over physical libraries and encyclopedias, Generative AI is a step-change improvement over Wikipedia, allowing anyone to employ unprecedented amounts of information.

Imagine a world where AI can analyze colossal datasets, generate predictive models, and provide real-time insights. This is the world we already live in.

New Horizons: Pioneering Innovative Business Models

Generative AI isn’t just a tool for streamlining existing processes; it enables entirely new business models to emerge. Just as high-speed mobile data and location tracking paved the way for transformative services like Uber, generative AI is unlocking uncharted territories.

Consider the concept of personalized content creation at scale. AI systems can analyze individual preferences and craft tailor-made content, be it news articles, product recommendations, or educational materials. This personalization can lead to innovative subscription-based content services that cater to unique tastes.

Moreover, generative AI fosters the growth of the gig economy, offering opportunities for freelance professionals to collaborate with AI systems. These human-AI partnerships can give rise to specialized, high-value content and services.

In conclusion, generative AI isn’t merely a technological advancement; it’s a game-changer for businesses navigating the fast-paced digital realm. It streamlines operations, reduces costs, enhances decision-making, and unveils innovative business models. The future belongs to those who embrace AI’s potential to supercharge automation, unleashing a world of new possibilities.

In the age of generative AI, the sky isn’t the limit—it’s just the beginning.

The post Unleashing the Power of Generative AI in Automation appeared first on The Experience Journal by QuestionPro.

In today’s dynamic and ever-evolving business landscape, one fundamental truth stands out – organizations that consistently deliver exceptional experiences are the ones that emerge victorious. Whether it’s providing on-demand customer service or implementing cutting-edge employee engagement strategies, companies that invest in experiences are the ones leading the way. However, as organizational priorities shift, the challenge arises in allocating resources effectively across brand, employee, customer, and product experiences. While leaders understand the interconnectedness of these experiences, pinpointing the specific parameters that drive optimal outcomes and positively impact the bottom line remains elusive.

Historically, many organizations have treated Employee Experience (EX) and Customer Experience (CX) as isolated endeavors. Despite attempts to align them, current approaches often operate in silos, offering fragmented insights. The key to unlocking true potential lies in embracing a more human-centric approach to experience management, one that bridges the gap between employee and customer experiences.

 



 

The Rise of Customer-Centricity

Customer-centricity has become more than just a buzzword; it’s a strategic imperative. Businesses are placing customers at the core of their operations, crafting personalized experiences, and leveraging data-driven insights to meet and exceed customer expectations. Yet, achieving true customer-centricity is akin to aiming for a moving target. As customer expectations continue to evolve, organizations are grappling with the challenge of staying ahead of the curve. The biggest obstacles for companies when delivering customer experience remain organizational silos, company culture, business process and technology. However, at the core of every organization are its dedicated employees, the invaluable human assets entrusted with the responsibility of harnessing their skills and talents to embody and deliver the fundamental principles and mission of the business.

 A time of great change and great opportunity

The pace of change in the business environment has accelerated significantly, driven by technological advancements, global shifts, and evolving societal expectations. In this context, organizations must place a heightened emphasis on cultivating a culture and employee experience that not only attracts but retains top talent. Recent data reveals that more than one-third of workers are actively considering changing jobs within the next three months. This underscores the urgency for organizations to connect with their employees effectively and empathetically.




 

The Importance of Effective Connection:

1. Enhanced Engagement and Productivity: Employees who feel connected to their organization are more engaged and motivated. Engaged employees are not only more productive but also contribute positively to the overall workplace culture.
2. Increased Retention Rates: Building strong connections with employees reduces turnover rates. When employees feel valued and heard, they are more likely to stay committed to their current roles and the organization.
3. Positive Brand Image: Organizations that prioritize employee connection and empathy are viewed more favorably by both current and potential employees. A positive brand image can attract top talent and enhance customer perception.
4. Innovation and Problem-Solving: Connected employees are more likely to collaborate and share ideas. This promotes innovation, leading to the development of new solutions and strategies that can propel the organization forward.
   
   
   
    
   
   Why Employee Experience Matters
   
   Enter Employee Experience – the often-underestimated linchpin of CX excellence. EX encompasses every facet of an employee’s journey within an organization, from recruitment and onboarding to professional development and beyond. A positive EX fosters an engaged, motivated, and satisfied workforce, leading to an array of benefits that cascade outward to shape the CX landscape.
   Engaged Employees Drive Customer Satisfaction: Research consistently demonstrates that engaged employees are more attuned to customer needs. When employees are content, motivated, and aligned with organizational goals, they are better equipped to provide superior service, solve customer problems efficiently, and create memorable interactions. The Power of Employee Advocacy: Happy employees are not just satisfied; they become advocates for the brand. Their enthusiasm and genuine belief in the organization’s mission translate into authentic, word-of-mouth marketing. These advocates bolster brand sentiment and positively impact the organization’s market position. A Unified Culture of Excellence: A harmonious work environment where employees are committed to delivering excellence naturally extends to their interactions with customers. A unified culture of service excellence, driven by a positive EX, ensures that the customer is consistently at the center of every decision and action.
   
   
   
    
   
   Trends Reinforcing the EX-CX Connection
   
   Several prevailing trends underscore the undeniable connection between EX and CX:
   
5. Data-Driven Insights: In the era of big data and advanced analytics, organizations are leveraging data to gain deeper insights into both employee and customer experiences. Analyzing EX data alongside CX data allows organizations to pinpoint correlations, identify areas for improvement, and make informed, strategic decisions.
6. Technology Empowerment: The proliferation of technology solutions has ushered in an era of enhanced communication and collaboration. Tools like employee engagement platforms, internal social networks, and customer relationship management systems are facilitating the alignment of employee and customer experiences.
7. Remote and Hybrid Work Environments: The global shift towards remote and hybrid work arrangements has accentuated the importance of digital tools for communication and collaboration. A positive EX in these contexts hinges on seamless digital experiences, which, when aligned with CX, create a unified brand image.
8. The Rise of Employee Well-Being: Organizations are increasingly recognizing the importance of employee well-being. Well-being initiatives that promote work-life balance, mental health support, and flexible work arrangements not only enhance the EX but also contribute to reduced employee turnover and improved CX.
   
    
   
   Positive EX does not necessarily translate to positive CX
   
   The relationship between employee experience (EX) and customer experience (CX) is undeniably interconnected, but it’s important to recognize that it operates differently in each direction. The assertion that “negative employee experience will almost always result in negative customer experience, but positive employee experience does not directly correlate to positive customer experience” reflects a nuanced understanding of this relationship. When employees have a negative experience within an organization, several adverse consequences can follow, which can ultimately impact the customer experience. Negative employee experiences have a profound impact on customer service and satisfaction. When employees feel undervalued, unsupported, or unhappy, their motivation to provide exceptional customer service diminishes, often resulting in them merely fulfilling their job duties without going the extra mile to meet customer needs. Moreover, these negative experiences can lead to reduced productivity, causing slower response times, lower service quality, and delayed customer interactions. Unhappiness among employees can also translate into higher turnover rates, disrupting customer relationships and resulting in the loss of institutional knowledge, leading to inconsistencies in service delivery. Additionally, employees who are dissatisfied with their work environment may inadvertently project their frustration onto customers, potentially manifesting as impatience, rudeness, or indifference during customer interactions. Consequently, ineffective problem-solving can arise, as employees experiencing negative employee experiences may be less equipped to address customer issues efficiently, ultimately leading to customer dissatisfaction.
   
   
   Positive employee experience undoubtedly holds significant value, yet it doesn’t always ensure an immediate positive customer experience due to several key factors. Firstly, alignment with organizational values plays a crucial role; employees may thrive within an organization, but if the company’s values and priorities aren’t congruent with customer-centric principles, the resulting customer experience may not reflect the same positivity. For example, a firm might focus on enhancing employee benefits while neglecting core customer service improvement. Additionally, while a positive employee experience can motivate and engage the workforce, the absence of necessary training or skills to meet customer needs effectively can still leave the customer experience falling short of expectations. Moreover, an organization’s internal structure and processes, such as bureaucracy, inefficiencies, or a lack of customer-centric policies, can either facilitate or hinder the translation of positive employee experience into positive customer experience. Lastly, fostering a customer-centric culture, alongside a positive employee experience, requires a concerted effort; this entails an organization-wide commitment to prioritize customer needs and feedback, ensuring a harmonious alignment between employee and customer experiences.
   
    
   
   
   
    
   
   So how do leaders design EX to better align with CX?
   
    
   
   Leaders play a pivotal role in designing EX to align seamlessly with CX. This alignment not only enhances employee engagement and satisfaction but also drives improved customer satisfaction and loyalty. This essay explores four critical strategies that leaders can employ to achieve this alignment.
   
    
   
9. Understanding the Input and Output Parameters to EX and CX:

At the core of aligning EX with CX is the need for leaders to gain a deep understanding of the input and output parameters for both experiences. In essence, this means comprehending how various factors impact the experiences of both employees and customers.

From an employee perspective, input parameters include factors such as workplace culture, job roles, leadership, and career development opportunities. Output parameters involve metrics like employee engagement, retention rates, and job satisfaction. By analyzing these factors comprehensively, leaders can identify areas where improvements are needed to positively influence EX and, consequently, CX.

For customers, input parameters encompass product quality, service offerings, pricing, and brand reputation. Output parameters include customer satisfaction, loyalty, and advocacy. Leaders must recognize that every interaction employees have with the organization indirectly affects customers. Thus, understanding these parameters enables leaders to make strategic decisions that enhance both EX and CX simultaneously.

 

10. Mapping Customer and Employee Journeys:

Another crucial step in aligning EX with CX is the mapping of customer and employee journeys. Leaders should visualize and analyze these journeys to identify touchpoints, pain points, and areas of intersection.

Customer journey mapping involves tracking the various stages a customer goes through, from initial awareness to post-purchase interactions. Concurrently, employee journey mapping charts the lifecycle of an employee within the organization, from recruitment and onboarding to ongoing development and potential exit. By comparing these journeys, leaders can pinpoint areas where employee experiences directly influence customer interactions.

For instance, if employees have a challenging time accessing necessary customer information due to inefficient internal processes, this could result in delayed response times or inaccurate information during customer interactions. Identifying these junctures allows leaders to implement changes that streamline processes, enhance employee efficiency, and subsequently elevate CX.

 

11. Driving Change and Giving Visibility to Progress:

Successful alignment of EX with CX requires leaders to drive meaningful change within the organization and give visibility to the progress being made. This involves fostering a culture of continuous improvement, where leaders encourage innovation and collaboration.

Leaders should communicate the importance of EX and CX alignment to all employees, emphasizing how each individual contributes to the broader goal. This communication should be complemented by tangible actions, such as investing in employee development, redesigning workflows to reduce friction, and gathering and acting upon customer feedback.

Moreover, leaders must establish key performance indicators (KPIs) that reflect both EX and CX improvements. These KPIs should be tracked and shared with employees to provide visibility into the progress being made. Celebrating successes and addressing setbacks openly fosters a sense of accountability and ownership among employees, further aligning EX with CX.

 



 

12. Having a Single View of Performance:

A critical imperative for leaders is to establish a single view of performance that integrates EX and CX metrics. This unified perspective enables leaders to assess the holistic impact of their decisions and initiatives on both employees and customers.

Leaders should create dashboards or reporting systems that consolidate key metrics from EX and CX efforts. This consolidation allows for a comprehensive evaluation of performance, offering insights into areas that require attention or adjustment.

Moreover, a single view of performance fosters transparency and accountability within the organization. When employees understand the interconnectedness of EX and CX metrics, they are more likely to take ownership of their roles in driving excellence in both domains.

 



 

13. Prioritizing EX in CX:

Finally, leaders must prioritize Employee Experience within the broader context of Customer Experience. This involves acknowledging that employees are the primary drivers of CX excellence.

Leaders should ensure that CX initiatives consider the impact on employees. For instance, when introducing new technologies or processes aimed at enhancing CX, leaders should also consider how these changes affect employees’ workload, stress levels, and job satisfaction.

Moreover, leaders should encourage cross-functional collaboration between departments responsible for EX and CX, recognizing that a seamless alignment requires ongoing cooperation and coordination.

Conclusion:

In conclusion, leaders have a pivotal role to play in aligning Employee Experience with Customer Experience. By understanding the input and output parameters, mapping customer and employee journeys, driving change, and giving visibility to progress, and prioritizing EX within CX, leaders can create a harmonious synergy that benefits both employees and customers. This alignment not only fosters a positive workplace culture but also enhances customer satisfaction and loyalty, ultimately contributing to the long-term success and sustainability of the organization. It is a strategic imperative that forward-thinking leaders cannot afford to overlook and the key to sustained success lies in recognizing that the path to exceptional CX begins with a thriving and engaged workforce. Your employees are the true architects of memorable customer experiences.

The post The Interdependencies of Customer and Employee Experiences appeared first on The Experience Journal by QuestionPro.

“Good is the enemy of great … It is the key reason why so few become great” – Jim Collins

It is puzzling that employee experience (EX) has only recently gained traction. After all, customer experience (CX) has been around for decades, so why has it taken organizations so long to look inward—at their people—who make things happen? 

The evidence for EX is strong, yet many organizations are failing at it and are unable to achieve this state in practice. Only a select few firms have executed people-first approaches, excelled at placing their employees at the core, attained the ‘elite’ status, and, in turn, are rewarded with a highly engaged, productive workforce and increased revenue and profits.

What is an “Elite”—an HPO? How do we describe this rare, unique, and often elusive state of exclusivity?

High Performing Organizations (HPOs), aka the ‘Elites,’ “are considered the best or most powerful compared to others of a similar type” (Cambridge Dictionary). They possess core traits that set them apart from the rest and typically achieve “financial and non-financial results that are exceedingly better than those of its peer group over some time, by focusing in a disciplined way on that which matters to the organization” (de Waal, 2019, p. 5). Instead of concentrating solely on outputs, such as revenue and profits, the emphasis is typically on the employees who make those outputs possible. Consequently, these firms have found the right balance between people and financial value.

n.b. It is important to note that sustainable desired results over a prolonged period characterize high performance. An ‘elite’ organization does not perform well out of sheer luck but because of an effective strategic plan with an average time horizon of three years or more to increase performance and beat the competitive peer group.

The driving force for adding value is evident in ‘elite’ organizations. Long-term strategic commitment, as opposed to short-term financial goals, is intact. Culture is actively integrated into corporate plans, and there is a conscious attempt to connect culture with people, systems, processes, and behaviors. 

The Modus Operandi

Members of the ‘elite’ club have a common thread. The ‘elites’ know that core organizational capabilities and people competencies drive financial and operational performance and enable strategy. They have figured out what it takes for sustained success. So, they set their sights on the right target—their people—to achieve measurable results. 

The ’elites’ employ a rare combination of people-first leadership and an employee-centered approach and effectively translate business strategy into a robust people strategy. They place the employees front and center and ensure an impactful employee experience (EX) to combat external threats, mitigate competitive forces, and achieve their goals and objectives.  

Also, they are disciplined not to be distracted by the latest leadership fad and remain on track with people-centered strategic initiatives aligned with their core values, purpose, and vision.

Further, the ‘elites’ improve what makes them successful—people, processes, systems, behaviors—and strive for improvement efforts on a continuum to excel in those contexts. 

The primary reason for an ‘elite’ taking a different angle and changing strategy (a deviation from the initial plan) is if an opportunity arises to strengthen its core capabilities and competencies. They know that deviating from an established path could directly impact and mean decreased engagement and declining performance, so leadership is usually keen on maintaining discipline (de Waal, 2019; Morgan, 2017; WTW, 2019).

The Key Differentiators

There are many ingredients, granted, to the secret sauce that differentiates the ‘elites’ from the rest; however, the decision is to highlight the essential contexts!

They are:

People First Leadership –Transformational leaders who invoke a people-first philosophy and signal a commitment to meeting the needs of their employees. People-first leaders realize that the employees are the decisive factor for the measurable results, so they use a people-centered design to create an employee-focused culture. It fosters an environment of trust, inspiration, loyalty, commitment, and respect in the workforce.

People-Centered Strategies – As evident, the ‘elites’ wisely focus on their ‘people’ first and places them at the core of their business strategies. They effectively translate business strategy into a robust people stratagem by attracting and retaining the most capable individuals and connecting their people with corporate strategy. It transcends a mere employee value proposition. 

High Engagement Corporate Culture – The organizational culture is not accidental; it is a planned, aligned, established, managed, and regularly monitored environment designed to achieve strategic goals and objectives. As a result, regardless of job roles/positions, employees are motivated to pursue corporate goals actively beyond the call of duty. 

Employee Experience Design –Looking through the lens of the employees and formulating an applicable EX. In short, creating an employee-focused organizational culture/environment based on their needs, desires, and expectations—i.e., an overlap between the employees’ reality and the organizational design, actions, and behaviors aligned with that reality (Morgan, 2017). (see Figure 1)

EX: The intersection of employees’ needs, wants & expectations and a corporate design and actions & behaviors that fulfill those needs, wants & expectations

An ‘elite’ organization knows its employees, recognizes that its employees have expectations, offers what they desire, and delivers an excellent employee experience (EX), the foundation of producing a superior customer experience (CX), and, in theory, exceptional financial performance.

Key differentiators/characteristics have allowed the ‘elites’ to consistently outperform their peers over the long term in both people and financial indices.

So, what exactly is the employee experience—EX?

Defining EX

EX has many definitions; it is a broad and fluid term that is defined differently depending on the context in which the nomenclature is depicted. 

From a human resource management (HRM) perspective, EX refers to the totality of encounters, observations, and emotional responses felt by an employee due to multiple workplace tasks, processes, protocols, and behaviors during his/her tenure in an organization. In essence, it is an employee’s experience through various touchpoints—from the recruitment process, through the employee lifecycle, to the offboarding/outplacement stage.

Dery & Sebastian (2016) view EX “as the work complexity and behavioral norms that influence employees’ ability to create value” (para. 4).

Morgan (2017) describes EX as “the intersection of employee expectations, needs, and wants and the organizational design of those expectations, needs, and wants” (p. 8).

Willis Towers Watson (2019) views EX as (a) “connecting with people and organizational purpose” and (b) “contributing to work and being rewarded accordingly” (p. 2). 

Massachusetts Institute of Technology (MIT) Center for Information Research (2022) defines EX as “The extent to which employees are enabled or constrained by the work environment and work habits to do their jobs today and to re-imagine their jobs of tomorrow” (para. 6). 

Gartner (2023) declares, “Employee experience is the way in which employees internalize and interpret the interactions they have with their organization, as well as the context that underlies those interactions” (para. 1).

Deloitte (2023) explains EX as “the human connections and mechanisms that create a high level of purpose and meaning between workers and the organization. At the core of any human experience is the desire to belong, feel connected with others, and contribute to something of significance and value” (para. 3).

So, it is evident there is no clear definition; however, the constants are organizational design & behavior, corporate culture, and workplace environment. It is about the overall experience throughout an employee’s lifecycle. A highly positive EX contributes to engagement, motivation, performance, and job satisfaction, whereas a highly negative EX could have an adverse effect.

The EX Factor

So, what does it take to create and foster an excellent employee-focused workplace and deliver the desired EX? Young and Kulesa (2019)—Willis Towers Watson (WTW)—offer some insights! 

Based on extensive and reliable research, WTW developed an evidence-based model of EX, identifying the four factors that matter most to employees. (see Figure 2) 

They are … 

• Purpose – A strong sense of purpose
• People – Connecting with great people and leaders
• Work – Doing great work in a thriving organization
• Total Rewards – Growth & Reward opportunities in return

Note. Adapted from Breakthrough research: Identifying the factors that make a high-performance employee experience (HPEX), by S. Young and P. Kulesa, 2019, Willis Towers Watson, Copyright © 2017 by Willis Towers Watson. 

Each dimension contributes to an organizational culture—its DNA—the beliefs, values, and practices that guide behaviors. Consequently, a firm’s cultural framework directly affects its EX, customer experience (CX), shareholder value, business-partner relationships, and business results.

 Along the four dimensions—People, Purpose, Work & Total Rewards—the EX-factor that tops the employees’ list are: Inspiration, Drive, Trust, Growth & Market Focus. (see Figure 3)

Global High-Performance norm companies vs. Global average (% favorable)

Note. Adapted from Breakthrough research: Identifying the factors that make a high-performance employee experience (HPEX), by S. Young and P. Kulesa, 2019, Willis Towers Watson, Copyright © 2017 by Willis Towers Watson

The ‘elites’ excel in these areas and generally set themselves apart from their peers with the key differentiators shared prior. They are unique because of their people-first leadership, people-centered strategies, high-engagement corporate culture, and EX design, which lead to inspiring employees, building trust, helping employees achieve their potential, and being agile and innovative in the marketplace. 

As evident, the key differentiators go far beyond the basics of rewards and tend to be more philosophically based—a people-first mindset—and woven into the organization’s culture. While rewards and other benefits (tangible & intangible) are vital, they are supplementary to the EX. 

The Data

WTW discovered that the dimensions—People, Purpose, Work & Total Rewards—determine whether an organization will underperform or outperform its peers. There is a direct correlation between EX and business results. EX predicts sustained financial success, and the ‘elites’ have impressive and sustained performance. They outperform their peers for top-line growth, bottom-line profitability, and shareholders’ return. (see Figure 4) 

Note. Adapted from Breakthrough research: Identifying the factors that make a high-performance employee experience (HPEX), by S. Young and P. Kulesa, 2019, Willis Towers Watson, Copyright © 2017 by Willis Towers Watson. 

WTW also found that people-centered organizations are:

• 3x as likely as other organizations to report employees are highly engaged.
• 93% more likely to report significantly outperforming their industry peers financially.
• 10% less likely to report difficulty attracting and retaining key employee segments.

Other Supporting Evidence

Despite a shortage of scientific research, various case studies (including the WTW research) and business reports have documented the vital benefits of EX design and application. 

Research by Jacob Morgan (2017) of 250 diverse organizations reveals companies that invest in EX outperform their competitors. These firms have “more than four times the average profit, more profitable and more than two times the average revenue” (para. 6). Additionally, they outperformed the S&P 500 and the NASDAQ and are often showcased in Fortune’s 100 Best Companies to Work. 

An MIT study by Dery & Sebastian (2016) produced similar findings, observing that EX predicts business performance. Companies in the top quartile for EX are determined to be 25% more profitable, have twice the innovation, and double the CX than competitors.

Also, Gallup (2019) discovered that firms with highly engaged employees have more than four (4)times the earnings-per-share (EPS) growth rate of those with low engagement scores. They also have 21% higher profitability and substantially better customer engagement, productivity, and employee retention.

Additionally, a study by The Josh Bersin Company (2021) uncovered that “Level 4” companies (the ‘elites’)—i.e., those that offer the most robust EX, compared to their peers, have 20% more equitable growth.

Further, McKinsey & Company (2021) research found a strong correspondence between employees’ stated needs and the underlying drivers of their engagement, well-being, and work effectiveness. 

Finally, Gautier et al. (2022), Harvard Business Review, established a causal link between EX, CX, and business outcomes—revenue and profits. The data reveal that EX drives revenue. 

Based on the data, there is a direct linkage between EX, employee engagement, and workforce potential that drives high employee productivity and increases corporate performance and financial returns. There is undeniably a significant return to organizations focusing on EX over the long term, not just short-term employee engagement in the here and now.

So, how do organizations become a member of the ‘elite’ club and achieve ‘excellence’ status? It requires adopting a new mindset, rethinking the work experience, and crafting an appropriate EX strategy!

Designing an EX Strategy

Creating the EX is not a process but a corporate strategy. Since EX includes touchstones that cover the entire employee lifecycle journey, leaders need to see employees as customers and create an environment that allows them to thrive in the workplace and their careers. A well-designed and executed EX strategy could fit the bill. To that end, when drafting an EX strategy, the key is enhancing the workforce experience spanning multiple functions/departments/units/teams/workgroups. It is an organizational-wide employee initiative, not for a select few! 

A core framework with the vital elements of an EX strategy is essential to serve as a blueprint for success. This article provides a basic framework. (see Figure 5) 

Align: Develop experience vision aligned with talent and business strategies

Focus: Define and brand the end-to-end employee experience

Execute: Implement prioritized initiatives roadmap with targeted people & business strategies

Measure: Capture the value and find and implement enhancements

Monitor: Review progress daily, identify risks, and offer solutions to unexpected outcomes 

n.b. The implementation and measurement of EX are like an audit of the corporate culture and current workplace environment. It is a continuous process, not a one-time initiative.

Design an EX-framework to meet the employees’ needs, desires, and expectations that align with the DNA—core values, culture, goals, mission, and vision of the enterprise. The EX-framework should be company-specific and translated by leaders to the precise organizational situation in its current state. 

Once the strategic framework has been planned and crafted, the implementation and change management journey can begin to ensure a distinct, impactful, and lasting EX effect. 

An EX-Roadmap

“The change management journey has many obstacles and challenges; however, the destination will be reached by staying the course” Karen Wagner-Clarke.

First, it is crucial to preface this roadmap by stating that EX is not an event; it is not a project; it is not a process; it is a cross-functional strategy that covers the entire employee lifecycle. 

Second, assuming one employee-improvement action magically moves the organization to deliver excellent EX is unwise. 

Third, each company has its DNA, so the best practices at one company may not work at your organization. Therefore, the EX design should be company-specific and the best fit for the enterprise. (see Figure 1) 

Fourth, an impactful EX cannot be created and implemented in a day; it is a long-term plan that should align with the firm’s core values, goals, and objectives, be thoughtfully developed, and be adopted over time. 

The goal is to create a corporate mindset, state, and employee reality to build a community of highly engaged, committed employees eager to work towards a common mission and vision. The result will likely be an enriched EX, elevated CX, enhanced productivity, increased profits, and high return on investments (ROI).

n.b. The roadmap intends to serve as a core guide, nothing else! It offers actionable insights and advice for leaders who want to implement effective practices to drive EX excellence.

Recommendations:

Getting out of the comfort zone: Abandoning the traditional idea of wanting to achieve higher turnover, revenue, and profit growth. Instead, broadening the horizon and rethinking the workplace and EX by placing people as the core strategy will eventually translate into increased growth and profitability. 

Instilling shared purpose: Employees want to feel invested in their organization’s purpose and ultimate objective. So, institute regular meetings to discuss emerging issues and create a framework for addressing them and deciding ‘why,’ ‘what,’ ‘how,’ and ‘when.’ Also, create a protocol for sharing the results specifically, timely, and transparently throughout the organization. 

Being employee-centric: Placing the needs and expectations of the employees as the focus of the organization’s operations. An ‘employee-centric’ environment means prioritizing psychological safety, accountability, engagement, autonomy, and creativity. The employees become the top priority!

Creating a workplace culture where everyone feels valued: While every organization has its recipe, prioritizing people’s value over financial value—should always be the secret ingredient. From involving employees in the decision-making process and garnering ‘buy-in’ to providing support to achieve their full potential, leaders should make the workforce feel that their needs, desires, and expectations are prioritized. Creating the right workplace environment and state for employees to excel! 

When employees feel valued and considered more important than profit margins, it increases their motivation, engagement, satisfaction, and performance. Engaged employees work passionately, feel connected to the company, and help move the organization toward excellence. 

Linking Culture with Systems & Processes: It means adapting a collaborative ‘systems thinking’ approach and creating seamless experiences with cross-functional and cross-enterprise (when applicable) workflows to connect people, places, functions, systems, and processes and achieve better business outcomes.

Fostering psychological safety: Having a culture grounded in psychological safety makes it easier for people to take interpersonal risks, raise their concerns, voice their perspectives, and challenge the status quo. In addition, it means creating a high-performance zone of high psychological safety and accountability, which allows employees to create, innovate, and strive for excellence. 

Promoting personal & professional growth & development: Most organizations offer programs to foster professional growth and development; however, employees also want opportunities for personal growth, for example, implementing career coaching, external training courses, certifications, language lessons, and community service programs. Broadening the definition of ‘employee development’ provides opportunities to increase employees’ skills and performance. Learning something new, even when not directly work-related, hones employees’ learning skills. It is a winning proposition for both employees and employers!

Listening to the voice of the employees: Actively listening to employees about their needs, desires, and expectations is imperative. It is best to take the time to monitor behaviors, send out surveys, listen to ideas, and have casual meetings and conversations. Also, take a genuine interest in what employees are doing to gain deeper insights that will help to better inform where the firm’s people-centered approach should be focused. Then, act proactively on the knowledge gained to improve their work experience!

Suggestion: The proposal is to replace annual or biannual employee engagement surveys with an ongoing pulse, organizational culture, business process feedback surveys, and open feedback platforms to capture employee feedback on a continuum. Include candidate interviews; engagement, employee satisfaction, and EX surveys; ongoing performance conversations; quarterly, semi-annual, or annual performance reviews; and exit interviews to gather a real-time understanding of employee issues. When reviewing the results, contemplate these questions:

1. How do our employees feel about the workplace culture/environment?
2. Is the organization living up to the employees’ expectations?
3. What have we learned based on the responses?
4. What improvements could be made to meet our employees’ needs and expectations?

EX efforts should build a continuous flow of feedback (a feedback loop) between leadership and the workforce. In this manner, leadership is aware of those aspects of the EX that are working well and areas requiring improvements. 

Emphasizing the employee experience: The ‘people-first’ philosophy and EX initiative should be woven into the company’s DNA and emphasized throughout the employee lifecycle. Leaders should ensure that all programs, processes, protocols, and strategies are developed and implemented, keeping employees front and center to facilitate a culture that takes care of its people from hire to departure/retirement. EX is a cross-functional strategy spanning all units, teams, and work groups!

In Summary

Undoubtedly, the ‘elites’ have discovered ways to enrich the EX, leading to purposeful, productive, meaningful work. They provide employees with an all-encompassing encounter supported by a people-first cross-functional strategy that makes a difference. 

Delivering a firm’s EX-factor, achieving workforce excellence, and distinguishing an organization as an ‘elite’ is rare but not impossible! Nevertheless, joining the ‘elite’ club and being an employer of choice requires looking beyond the traditional perks and programs companies have long touted as differentiators. Instead, it necessitates focusing on the EX factors that matter to your workforce, delivering the best EX, and meeting the employees’ needs, desires, and expectations. 

After all, “In a world where money is no longer the primary motivating factor for employees, focusing on the employee experience is the most promising competitive advantage that organizations can create!” – Jacob Morgan, The Employee Experience Advantage

References

Cambridge Dictionary. (n.d.). Elite. In Cambridge Dictionary.org. Retrieved May 2, 2023, from https://dictionary.cambridge.org/us/dictionary/english/elite

Collins, J. (2001). Good to Great: Why some companies make the leap and others don’t Hardcover. New York, NY: HarperCollins.

de Waal, A. (2019). What makes a high-performance organization: Five validated factors of competitive advantage that apply worldwide. Amsterdam, Netherlands: Warden Press.

Deloitte (2023). Elevating the workplace experience: The importance of employee engagement and meaningful work. https://www2.deloitte.com/us/en/pages/human-capital/articles/elevating-workplace-experience.html

Dery, K. & Sebastian, I. M. (2017). Building business value with employee experience: Massachusetts Institute of Technology Center for Information Systems Research. https://cisr.mit.edu/publication/2017_0601_EmployeeExperience_DerySebastian 

Gallup (2019). Employee Engagement on the Rise in the U.S. https://news.gallup.com/poll/241649/employee-engagement-rise.aspx

Gartner (2023). Gartner Glossary: Employee Experience. https://www.gartner.com/en/human-resources/glossary/employee-experience#:~:text=Employee%20experience%20is%20the%20way,context%20that%20underlies%20those%20interactions.

Gautier, K., Bova, T., Chen, K. & Munasinghe, L. (2022). Research: How Employee Experience Impacts Your Bottom Line. Harvard Business Review. https://hbr.org/2022/03/research-how-employee-experience-impacts-your-bottom-line

Massachusetts Institute of Technology (MIT) (Dec 13, 2022). Exploring the employee experience. https://news.mit.edu/2022/exploring-employee-experience-nick-van-der-meulen-1213

Morgan, J. (2017). Why the Millions We Spend on Employee Engagement Buy Us So Little. Harvard Business Review. https://hbr.org/2017/03/why-the-millions-we-spend-on-employee-engagement-buy-us-so-little.

Morgan, J. (2017). The employee experience advantage: How to win the war for talent by giving employees the workspaces they want, the tools they need, and a culture they can celebrate. Hoboken, NJ: Wiley & Sons. 

Young, S. & Kulesa, P. (2019). Breakthrough research: Identifying the factors that make a high-performance employee experience (HPEX). Willis Towers Watson. https://www.wtwco.com/en-GB/Insights/campaigns/breakthrough-research-on-employee-experience

The post The ‘EX’ Factor: What the ‘Elites’ do differently appeared first on The Experience Journal by QuestionPro.

Summary

The most successful brands build their marketing strategies from a deep understanding of the people they are trying to resonate with and reach. And for years, marketing research has provided brands with answers to their questions about what consumers are doing in order to gain this deep understanding of people. When this understanding is truly met, brands grow and innovate and remain relevant to their customers. But on the flip side, not all market research has been successful. And thus, some brands have made decisions based on market research that were ultimately fails. For example, did Colgate really think expanding into the Frozen Entrees was acceptable for its brand?¹

Market research has been around for over one hundred years now. It started out with a desire to ask consumers questions. And today, consumer research still mainly relies on consumers to tell us answers to questions we have about their lives, their behaviors, and their actions. This type of insight is still very valuable, but it must be done right. And we believe asking consumers to recall their behaviors should be secondary to leveraging their actual behavioral data. 

With consumers’ busy lives paired with the complexity of the world we live in we can’t expect consumers to accurately recall what they did. Not because they don’t want to tell us the truth, but because with everything someone is doing, it would be hard for any of us to recall exactly what we did yesterday, let alone last week or last month, with precise detail. 

Fortunately, with the explosion of technology and the connected world we live in today, consumer behavior can be recorded in real-time, providing actual consumer behavior versus recalled behaviors. 

Table of Contents

• Summary
• What is Behavioral Data and Specifically Digital Behavioral Data:
• Why Behavioral Data:
• The Right Business Challenges for Behavioral Data:
  • Defining a competitive set and keeping a pulse on key competitors
  • Define an audience 
  • Pinpoint how and where to reach your target customer
  • Uncover areas of confusion or opportunity areas where customers may be struggling to understand or find what they are looking for.
  • Discover the consumer journey
  • Keep a pulse on new trends or foresights
  • Guide Search Term Optimization
• How to be successful with behavioral data:
• Conclusion:
  • • Resources

What is Behavioral Data and Specifically Digital Behavioral Data:

Behavioral data could be described as any data set that describes consumer behaviors, which could include stated behaviors based on recall and collected through surveys. However, I’d argue that type of data is still survey data on behaviors, not behavioral data. 

True behavioral data is captured in real-time when consumers are doing it, without any recall or reliance on their memory of what was happening. Behavioral data is being captured all around you – think about smart devices, GPS/map software, loyalty programs, connected vehicles, and more. 

The type of behavioral data that I am focusing on today is digital behavioral data. There are various panels out there that capture consumers’ digital behaviors, tracking everything they are doing online, where they are going, what they are searching for, how much time they are spending, sites they are shopping, and what they are purchasing – ultimately painting a picture of the online journey and path that consumers are taking. Some provide more than others, and some even provide cross-device, in-person location data, or social advertising data. 

True behavioral data sets are permission-based, meaning consumers have chosen to opt-in and allow for their data to be shared. They do not rely on cookies, meaning it is future-proof. The data is longitudinal, capturing every click, every app open, and every search a consumer is making. 

These data sets are extremely large (terabytes and terabytes), they are unwieldy, and for the average marketer or researcher, may seem impossible to analyze and make sense of. Each line of the data represents a separate consumer event that was captured – a search, a click, a website visit, an app open, an in-person location visit, an ad they saw. 

In addition to just the digital events collected, the data often has many descriptive variables that accompany it. An event timestamp provides the order of events and how long someone spent in an app or looking at a specific page. When someone agrees to share their data, often a short sign-up survey collects key demographics or attitudinal statements that can be appended to the behavioral data. 

The data itself, while it may not seem insightful at first, provides a digital footprint for consumers which when analyzed, can help brands understand who these consumers really are. Not just what they are doing online but who they are as humans. What are their interests, what hobbies do they have, what keeps them up at night, what things are they buying online for use offline, and more? Behavioral data ultimately provides brands with a leg up on being truly consumer-centric and getting to know consumers better than anyone else.

Why Behavioral Data:

The average person spends 6 hours online every day. And today, there are over 200 million active websites and over 3 million apps available in the app store. And it does not stop there… over 2000 new apps and 250,000 websites are released each day!

We live in a digital world; everyone is online all the time. Traditional primary research would ask people to tell us about everything they are doing. While they may try, it would be hard to accurately recall everything they did even if it was just 5 minutes ago. 

Additionally, traditional panels continue to see an increase in bad data – whether that be bots, people rushing through surveys and not really reading them, or even the creation of the survey itself being poor. However, consumers are increasingly interested in signing up for panels that are easy and have a fair exchange. In the case of behavioral data panels, consumers are rewarded with fair incentives or other types of rewards like promotions and coupons in exchange for providing access to their data. The details of what they are sharing are very transparent, and most find it to be a very fair exchange. 

While traditional survey methods can provide a glimpse into consumers’ behaviors, it simply isn’t the most accurate way to understand behaviors. I’ve said it before, but at this point, it’s not really a choice. It’s our responsibility as an industry to stop asking consumers to recall behaviors that we know they can’t articulate. 

Leveraging this rich digital consumer behavior allows for a true understanding of consumers. It can help brands to extract insights to guide digital strategies, uncover how consumers are shopping and cross-shopping brands, keep a pulse on key competitors, and understand consumer research processes and shopping journeys. 

The ability to monitor consumers’ digital engagements with key brands and understand retention, loyalty, and social ad effectiveness in real-time presents unlimited opportunities. 

The Right Business Challenges for Behavioral Data:

Not all business questions are right for any one methodology. But many common business challenges brands come up against aim to understand consumers’ behaviors better. When this is the case, a behavioral-first mindset should be embraced. 

Some of the most common business challenges could be better answered and solved using digital behavioral data. 

Defining a competitive set and keeping a pulse on key competitors

Brand tracking has been around for many years and aims to track consumer perceptions and behaviors longitudinally. A typical brand tracking survey asks consumers about their awareness, current interactions, and future intentions with the brand and key competitors. It may also ask questions about brand perceptions and brand imagery. While consumers may not interact the same way digitally with all brands, ‘digital is not for me’, is no longer an excuse any brand should be using. 

Digital behavioral data can provide a glimpse into those who may be exploring a category or a specific brand but not admit that they are considering – the data provides upstream shopping and research that is happening, those touchpoints that are shaping and influencing future behaviors, that consumers don’t even realize are occurring. 

With digital behavioral data, a true competitive set can be revealed. You may not initially consider these other brands or categories competitors, but ultimately the behavioral data can show you they are competing for your customer’s share of wallet or time. Monitoring these brands, in addition to yours and your traditional key competitors, can ensure you keep a pulse on what is happening in real-time instead of waiting to see the impact happen in the market. You can observe overlap, switch, and even find new entrants before they make a splash in the market.

Digital behavioral data should not replace brand trackers but be sought to be the foundation of competitive and category insights. And when paired with a primary survey to capture those additional data points which cannot be derived from the behavioral data, a holistic picture of the consumer is gained. Connecting the behaviors that drive brand perceptions or connecting to some level of offline behavior can ensure your brand is showing up in the right place and resonating in the right way.

Define an audience

For years we’ve relied on survey data, asking consumers to tell us about their hobbies, their interests, and what things they like to do. But can you remember the last time someone asked you that? Did you know the answer right away or did you have to really think about it? Sometimes I feel like the things I like to do are not even the things I’m able to spend my time doing. So how does that help a brand know how to message directly to me?

The industry made a slight move to appending this type of information from third-party data sets. Much of which is modeled off where you live – assuming that you are like your neighbors. Maybe true, maybe not. These approaches increase lift by attempting to send messages to those who it will resonate with versus sending it out blindly to everyone. 

Profiling an audience based on behavioral data is mainly an untapped resource today. An individual’s behaviors are indicative of who they are as a person. Digital behavioral data analyzed in the right way can tell us what someone’s hobbies and interests are, what they are buying online to use offline, what types of media and advertising attracts them, and more. 

Knowing this type of information can guide messaging content and ad placement strategies. It can be appended to consumer segmentation and a brand’s target segments to better understand who they are, bringing them to life and revealing actionable strategies to reach them. 

Pinpoint how and where to reach your target customer

Ad agencies have access to many different sources of information guiding their digital ad placement recommendations. Many of these include the top websites overall or the top within a category. And many brands embrace an endemic digital marketing strategy placing ads in locations where consumers are engaging with their category. However, with the millions of things, consumers can do online, their engagement with your category might be so small that you’re actually missing them and ultimately wasting your media budget. 

With behavioral data, you can compare your target customer to a representative general population sample and understand those places online where your target is spending more time than the rest of the population. It also can guide strategy, uncovering lesser-known or hidden pockets where you can find your target customer. 

Uncover areas of confusion or opportunity areas where customers may be struggling to understand or find what they are looking for.

How many times have you gone online looking for something, scrolling through the search results and don’t see exactly what you’re looking for. Try a different search, still don’t see it. Maybe you try to click on something that could be it, but no. Go back search again. Get frustrated and go do something else. But you still don’t have an answer, so you try again later. 

Behavioral data can help you see these frustrating consumer journeys. What specifically they are asking for, what things don’t help them, and ultimately help you to curate new content that will answer their questions and guide them to your brand. 

Discover the consumer journey

Brands are all trying to build connections that will last with their customers – gain loyalty, be well-known, differentiate and excel above their competitors, and remain relevant overtime. Understanding the consumer journey and all the touchpoints along the way, including when they are not happy and consider switching is vital to showing up strong and maintaining and growing a brand. 

There are a million different ways to approach a consumer journey study. But why not start with actual observed behaviors? How consumers are interacting with your category, your brand, your competitors. How does that differ based on if that person is new to the category or has been with your brand for years? Or what else are consumers doing when engaging with your brand – let’s say they are in one of your stores, but they are on their phone. What are they doing? Checking your site online? Or a competitor’s site? Or looking for help on what product(s) to purchase?

Digital behavioral data can answer all these questions and more, connecting the dots on the activities and touchpoints that are occurring with your brand and key competitors. Paired with a survey or qualitative interview, gaps in the journey such as how they are feeling, what they are trying to accomplish and why they are doing the things they are doing can be layered in. 

Keep a pulse on new trends or foresights

Brands are always trying to stay on-top of what new trends or fads are happening and incorporate them into their innovation pipelines. But knowing what is going to be the next ‘hot’ trend is hard to uncover. Looking within your own category and keeping a close watch on your competitors is a must. But some also look to other categories for early-adopters and take cues from them. 

Leveraging behavioral data to watch for upticks in keywords can provide an indication of when the market is really starting to adopt or move on something. Whether it’s the next new cool ingredient, or package sustainability, or something else. Monitoring digital behavioral trends can ensure you’re staying in the forefront of foresights.  

Guide Search Term Optimization

How many times do you start with search versus going directly to a site. A search engine is an easy way to navigate to a website without remembering the exact site you’re looking for. But what if you think you know where you want to go but the results show something different… Did you switch your original plan? 

All brands, whether small or large, want to appear in consumers’ search results. Even if a consumer has already made up their mind where they want to go, if they use the search and see a brand or a product, the opportunity is there for the consumer to change their mind and click on anything that appears. Behavioral data can easily uncover traffic-driving keywords for brands and their key competitors. 

How to be successful with behavioral data:

TIP #1: Know not all behavioral data sets are the same… make sure you carefully evaluate and choose the right behavioral data set for your needs. 

Behavioral data sets are going to vary based on the device they are capturing data on, the size of the data, and what types of data they collect. It’s important to understand your objectives and your target audience before choosing a data set. 

In 2021, 85% of Americans owned a Smartphone and 15% of Americans were smartphone only Internet users.² And globally in 2020, Internet traffic was reported as 51% mobile, 47% desktop, and 3% tablets. So, consumers are online using both desktop and mobile devices. ³ And what they are doing on each device may differ. Behavioral panels may specialize in just one device, or they may have both, and the best ones have a portion of panelists that are sharing their data on both mobile and desktop. 

With traditional market research, whether qualitative or quantitative, one may be ok with a smaller sample size – say 50 to 300. However, with behavioral data, there are so many different paths and touchpoints that we recommend a larger, more robust sample in most cases. If you’re looking for a niche audience and can get 200-400 people who have exhibited a specific behavior, that is enough to understand who those people are and what they are doing. However, if you’re looking for specific paths people take or want to drill down into various regional or other types of data cuts, a behavioral sample in the thousands is going to get you better insights and ensure you are not missing anything. 

You should also ask about panel representativeness. Now, representation can mean a lot of different things, so make sure you define that before you ask. Do you want a representative within a specific market? Do you want census representativeness? Do you care? Make sure you know the answers to these questions. If you do want representativeness, know all panels skew in some ways. Hence, the question is not ‘Is your panel representative,’ but rather, it should be ‘Can I acquire a representative sample that is analyzable from your panel’. 

The last but one of the most important pieces of information you need to understand is what type of data the behavioral data will provide you with. Some panels track only a certain number of websites, which can be useful to compare brands (if they have your desired sites) but lack the full journey of what consumers are doing online. Others track everything someone is doing online but may be limited to just browser-level data and what is captured in the url. Others may collect the background calls on a page to allow you to see things a consumer is interacting with on a page that are not reflected in a changing url – these typically include pop-ups on a page whether that be videos, advertisements, or even check-out carts. 

In addition to browser-level data, some behavioral panels will have additional behavioral data on consumers. These variables could include geo-location data – venue locations where consumers are visiting, app usage data – what apps consumers have and how often they are using them; in-app data – what people are doing in the app, products viewing, products purchasing, videos watched, etc. 

TIP #2: Ensure you have the right skill set, expertise, and tools to analyze the data. 

One of the biggest hurdles to date with embracing behavioral data has been the ability to wrangle, analyze it, and ultimately make sense of it. 

If you’re looking to purchase access to the raw data, you’re going to need to be able to analyze and work with it in raw form. From acquisition to cleaning and structuring to analysis. Excel or even SPSS is not going to cut it. You will need to be able to work with S3 buckets, data lakes or data warehouses, and parquet or JSON files. All these things will require the right tools and employees. 

You can also hire a company that is an expert in behavioral data and can structure and analyze the data for you. Providing a more manageable output to work with. 

Either way, having a strong analytical plan of what you want to do with the data is important. An analytical-minded individual who is familiar with the data dictionary and the results of the data is extremely important. It’s often hard for someone who has worked mainly with just survey data to grasp the possibilities behavioral data can provide and build out an analytical plan. 

Data platforms to analyze data are extremely helpful. But be careful because many of the data visualization platforms out there today are great for making charts and graphs to show insights. Still, they take a lot of wrangling of the data before importing as many of them are not built to efficiently analyze and visualize time-series data. There are, however, some great intelligence platforms that specialize in time-series data that can cut the data engineering and data wrangling time from weeks to hours. 

TIP #3: Don’t expect to analyze behavioral data like survey data – it’s different. 

With survey data, you have a finite list of options you’re asking consumers to choose from, or you may ask in open-end form and code them, but still, it typically is a manageable number of codes. With behavioral data you might have millions of different search terms, websites, apps, venues, etc. Manual coding could take days, maybe even weeks. Often pre-existing coding is scrapped, and AI models are built to assist with the coding, training the models to get better and better over time. With behavioral data, we must be ok with some gaps in the data, whether that be missing some of the websites or apps that are not as prevalent in consumers’ behavior or some false positives or false negatives appearing in the AI models. Over time, those working with behavioral data will train and improve these models, and they will get better. 

Another difference between survey data and behavioral data is the ability to exhaust your analysis. Behavioral data is so vast, and the possibilities are limitless – which makes it fun. But you also must have a plan of what you want to explore so you’re getting insights before the data becomes outdated and irrelevant to the decisions you want to make. My recommendation is to focus and hone in on answering specific business questions but leave some room to explore and expand beyond there when you find something interesting in the data.

TIP #4: Combine rich behavioral data with other consumer insights to have a holistic view of the consumer – but avoid redundancies. 

Behavioral data is great and provides so much rich detail on exactly what consumers are doing. And we believe it should be the first place any brand researcher, brand marketer, or brand strategist begins. But oftentimes, more questions will arise or more data will be necessary. 

First, consider additional behavioral data sources that can be appended to the behavioral data through a PII match. These include in-store receipt data, VIN numbers, magazine subscriptions, or even a third-party data set like Acxiom, Experian, or Merkle to help facilitate the creation of look-a-like models. This additional layer of behavioral data can provide more insight into how these individuals are behaving, where you can reach them, and how to resonate best with them. 

Secondly, consider a traditional survey or qualitative research to talk to these individuals and marry the behavioral data with rich attitudinal or explanatory data to explain the whys behind the data. Or if there are behaviors that might be occurring that are not captured in a behavioral data set, for example, signing up in-person or over the phone, those are behaviors that we should ask people about. But make sure you’re asking high-level questions and not trying to get into details consumers can’t recall. 

What we shouldn’t do is ask about things that we already know through the observed behavioral data. When asking consumers to recall how much they spent, the order they did something, or what keyword they searched for, we already know they are going to have a hard time getting us the truthful details. So rather, leverage behavioral data that already exists, and marry it with additional data to provide you with a holistic view of the consumer. 

Conclusion:

We live in a digital world, which presents so many opportunities for researchers and marketing folks. However, it can also be scary to know how to leverage all the rich data that exists. And brands don’t want to waste their precious resources – whether that be time or money experimenting with things that won’t help them. But digital behavioral data is not new anymore; it’s just not being leveraged to its full potential. When it is used, it’s changing the way brands do things. It’s improving their understanding of the consumer, it’s helping to grow brand recognition, brand consideration, and brand loyalty.  

BEHAVIORAL DATA IS HERE. IT’S NOW. AND WE SHOULD ALL BE EMBRACING IT. 

Resources

1. The 10 Biggest Market Research Fails of All Time (trendsource.com)

2. Demographics of Mobile Device Ownership and Adoption in the United States | Pew Research Center

3. Desktop and Mobile Internet Usage Statistics – 2022 – High Speed Internet

The post <h1>A Behavioral-First Mindset</h1> <h3>Changing the way brands understand consumers</h3> appeared first on The Experience Journal by QuestionPro.

Introduction

There are several challenges and opportunities when it comes to capturing the true, authentic voice of the customer. Customer Voice is nothing new. It has been around for years, but it’s the way it is approached, the way it is measured, and the way it is shared that is changing. Fortunately, I come from a sales and customer service perspective in the B2C world where the customers are always right, and you NEED to listen, or their business will be taken elsewhere.

That is just as true in the B2B world, but there is more word of mouth and more research that goes into a B2B buying and renewing process than a low ticket B2C ticket item. So it’s even more critical to ensure you not only capture customer voice but share it out there, do not just let the word-of-mouth marketing be the source; contribute to the narrative. Let’s start with the pitfalls so we can all agree on the challenges and then dive into tips to put into practice.

Pitfalls to avoid

Hands down, there is always bias: One of the most significant challenges in capturing the authentic voice of the customer is bias. This can include how questions are phrased or the sample selection that is pulled from the primary source of content. Take the middle person out, ask the customers to self-record, self respond without being asked or begged. Serve it up as an opportunity.

“The customer’s perception is your reality.“

Kate Zabriskie

In addition, another challenge is around lack of participation. Again this comes back to DO NOT beg for favors, but if you had positioned your customers upfront when they partnered with you on what opportunities they would like to be survived up, there are no surprises; they participate enthusiastically.  

“Repeat business or behavior can be bribed. Loyalty has to be earned.” – Janet Robinson

Sometimes, customers may be unwilling or unable to provide feedback, which can make it challenging to capture their authentic voice and find ways they can still participate – even if it’s anonymously. To squash the busy aspect, meet your customers where they are, ask, and partner with the internal champion at your organization that is CLOSEST to the customer to make the ask – do not put another layer of a person into the mix overcomplicating the ask. Huge success if you keep it simple, have the right person ask, and offer up the opportunity the customer is interested in engaging in. 

And there is always the chance of incorrect or Incomplete data. Your CRM is not current with how often the customer has participated in acts of advocacy or shared their voice. Track now, and make it visible to all to avoid burnout of your customers. Then when you need them, they will not be there; and a step further, even when customers provide feedback, it may not be enough to have the probing question, get in and ask the good, the bad, and the ugly. All types of feedback are valuable.

Another pitfall to avoid is an unrepresentative sample. You want to have a variety of customer voice assets from all types of customers—different use cases, different industries, different customer sizes, etc. If the sample of customers providing feedback does not represent the overall customer population, the input may not accurately reach the audience in the way you want. You will miss the mark for specific business segments and or personas. Another layer also is if the sample consists mainly of highly satisfied or highly dissatisfied customers, the feedback may not be representative of the entire customer base. You want to spotlight the satisfied and show the dissatisfied and what you did to turn it around. That’s authenticity! 

You are getting too much Customer Voice, Feedback, and Data. A good problem to have, right? Well, if you do not have the proper processes in place for how the voice of the customer gets put to work, it will fail. You will fail internally and externally with your customers. How many times have you personally shared feedback in any, written, audio, or video, and it went into the black hole of the unknown? No follow-up UNLESS you were outraged, and rarely any acknowledgment. In the competing world, especially in the B2B SaaS world, the time of your customers value it. Value their time and value all the feedback.

If you cannot follow up timely, do not cast such a wide net of customer voice capture. If you have no campaigns for follow-up in social, your website, your community, or your newsletter, build one, make a content calendar, and plug-in customer voice assets regularly. On top of that, sorting through large amounts of feedback can be time-consuming and make it challenging to identify the most critical issues. Work smarter, not harder!! 

Addressing these challenges requires careful planning and execution of customer voice programs, including ensuring that the program has been thoroughly researched, includes customer input, and, most importantly, executive leadership buy-in. It’s also essential to remain open-minded and flexible and to continuously refine the customer program to improve its effectiveness in capturing the authentic voice of the customer. It is not a set it and forgets it. And most definitely not. If you build it, they will come. It takes work, but with the proper planning and rollout, researching, and TALKING to your customer base, you will be set up for success.

Tips to put into practice

So how can businesses leverage the power of the authentic customer voice to drive revenue? Here are some tips:

Use customer voice to help drive product development: Collecting customer feedback on their needs and pain points can help businesses develop better products and services that meet their customers’ needs. You need to be constantly reinventing and always new—and not just new customers for existing customers too. You want to be sure they renew year after year, and they do not go looking at the new shiny thing. So not only showing and collaborating but listening to what your customers want can lead to increased revenue. 

Use customer feedback to improve customer experience. You can do this by listening to customer feedback; businesses can identify areas where they need to improve their customer experience. Do onboarding interviews and collect customer voices at various stages in the customer lifecycle. It’s not just one-and-done for customer voice. It’s ongoing.

Use customer testimonials to build trust. But not the drone shots of the customer headquarters and cheesy elevator music. You want the true authentic voice of your customers in testimonials. Including customer testimonials on your website and in marketing materials can help build trust with potential customers, build FOMO in the market, and create a peer to peer engagement as other customers see who else is using your solution. We know that people are more likely to trust recommendations from their peers than they are to trust advertising or a sales rep. Give the people what they want! True authentic customer voice. No more happy quotes or logo banners on your site. Let’s know the users, the champions, the ones realizing the value and securing their job with the successes of your product solution or services. 

“Make the customer the hero of your story.”

Ann Handley.

What we all talk about, but let’s be honest, do not do the greatest on as for the longest time, the mentality at SaaS-based companies especially is new deals are king. But upselling and cross-selling are Queen, and it’s here to help everyone.   For example, if a customer uses one of your products, use the power of the customer’s voice to share a success story another customer had with that exact use case. Introduce them to that customer so the real, unscripted, authentic chats can happen. 

True authentic customer voice

By leveraging the power of the authentic customer voice, businesses can improve their products and services, improve customer service, build trust, improve the customer experience, and identify opportunities for upselling and cross-selling. Individuals tasked with owning, managing, and reporting on customer voice must carefully plan and execute customer voice programs, remain open-minded and flexible, and continuously refine their approach to ensure they capture the most valuable insights from their customers. 

Conclusion

In conclusion, capturing the authentic voice of the customer is critical in driving revenue for any business. It is essential to listen to what customers say, whether positive or negative and take action based on their feedback. 

“Loyal customers, they don’t just come back, they don’t simply recommend you, they insist that their friends do business with you.”

Chip Bell

Now how are you practicing true authentic customer voice?

The post <h1>Using the Power of Customer Voice to drive revenue:</h1> <h3>Traditional Customer Voice vs the True Authentic Voice of your Customers</h3> appeared first on The Experience Journal by QuestionPro.

Introduction

There have been many interactions about the promise of how generative AI and machine learning will revolutionize the way we conduct business. There is always a promise of what could be the next big thing on the scale of the “internet”. The market research industry, too, has grown by leaps and bounds. We are moving towards technology-reliant insights collection and management that scratches beneath the surface and showcases trends faster. Conversational AI has set us on the path of Research 3.0.

What is Research 3.0? In the words of Ray Poynter and NewMR, a QuestionPro partner, Research 3.0 is the link between things like chatbots, video, extended media, and observational research with non-experts.

The article discusses the use of the AI language model ChatGPT in surveys and the potential impact it may have on data quality and the research industry. Jamin, one of the commentators, believes that ChatGPT can revolutionize the way customer insights are gathered and analyzed by enabling natural conversation and analyzing open-ended questions with more sophistication. 

The other panelists of this paper, Jamin Brazil and Vivek Bhaskaran, discuss how technology like ChatGPT can help with qualitative research, allowing for more efficient and accurate data processing and the opportunity to decouple from the survey in some cases. They also mention potential concerns around the impact of such technology on jobs traditionally part of the back-end field operations and research industry. 

QuestionPro has implemented a system to detect when ChatGPT is used in open-ended responses and flag them for review. The text also mentions a separate project run by QuestionPro that helps non-researchers design surveys using ChatGPT to generate a list of questions around a particular topic.

What is ChatGPT?

ChatGPT is a generative AI engine with a chat interface where you can ask any questions, and it’ll come back with intelligent answers. Assistant-like features are among its functionalities; you can ask it to do things, create content and summarize it. 

The platform works on the model of conservational AI and constantly trains itself to adapt and learn the basis of the persona you give to it. It can continuously evolve and learn from various parameters to be highly adaptive based on the user’s prompts.

It can be used to summarize text, answer questions, make iterative judgments, hypothesize and synthesize text in data, troubleshoot, and more. The possibilities are limitless!

ChatGPT or GPT-3 in the form currently released by OpenAI 175 billion parameters. However, GPT-4, which will soon replace the earlier model, will have 100 trillion parameters. Let that sink in for a second. The processing that is currently mind-boggling is going to pale in comparison when the new version launches.

Nothing defines ChatGPT better than asking itself, though; this is what it says about itself – ChatGPT is an extensive language model developed by OpenAI that is trained on a massive dataset of conversational text. It is designed to generate human-like text in response to a given prompt. It can be used for various natural language processing tasks such as language translation, question answering, and text summarization.

ChatGPT will significantly impact how we gather and analyze consumer data in 2023 more than the internet did from 1996 to 2006.  

The impact of ChatGPT in data gathering and data analysis in 2023

You think about the advantage that the internet provides consumer insights professionals predominantly around speed. You could argue price, maybe, but I think the most significant benefit has been speed and accessibility. Before, we had to intercept people in malls to get them to complete a survey. Now we intercept them in their inboxes to get the same thing done, and certainly, in the period you referenced in the early 2000s. Online surveys were copied and pasted from phone inner phone interviews or model intercepts, so there wasn’t anything unique about it except for the format, the place it was being done, you fast forward to, let’s say, yesterday like pre-ChatGPT. You look at our industry; we are dealing with similar problems that we dealt with 20 or 30 years ago.  

Unstructured data is one of the most significant wastes in consumer insights. 

In the past, it has happened that brands conducting a customer sat survey would get about half a billion completes a year, so a lot of completes. And if they are majorly open-ended questions, that’s a lot of data to churn. Frequently, such brands would randomly select a segment of about 20 responses, hand-code those and say these are relatively representative of the sentiment on the subject in research. This model wasn’t foolproof. 

If you start decoupling the framework, you think about the rationale of the actual survey, and its instrument is quite literally just a conversation at a scale. 

Nobody speaks in Likert Scales; we don’t talk about; a five-point scale around how much you like your coffee. Instead, we have a conversation. This technology employs the same logic of enabling conversations to surface feelings and vectors and generate a highlight reel for us that is very actionable.

Research at Scale

The internet enabled quantitative research due to the survey angle, but qualitative research has primarily been labor intensive. The only reason qualitative research struggled in comparison is that it lacked the back-end technology and the ability to analyze at scale. ChatGPT is changing that paradigm for research. 

GreenBook Research Industry Trends (GRIT) Report has been working with a text analytics provider called Yabble, which has a text analytics component that you know is relatively standard to get to themes and clustering. Leonard Murphy at GreenBookbut mentioned that when they used ChatGPT, he and the team were blown away at how the technology could interrogate the data to ask for a summary. GPT-3 wrote an excellent overview, even if it didn’t cover everything needed. 

In a podcast that Lenny did with Simon Chadwick, they used the tool to summarize the discussion. It wasn’t full of wonderful flowy language, but it was accurate, summarized the salient points, and communicated skillfully and professionally. It did an excellent job of citing examples from the open-ended discussion and filling in the blanks. There was an ability to decouple from the survey in a few instances, but that was not a deal-breaker. 

We have an opportunity; if we combine ChatGPT with other an existing technology stack that we’ve seen in use, like text analysts that get into emotional effect right, not just sentiment but actually to try and understand the emotion and the states of mind that are occurring within that textual conversation, voice analytics all of those things, there is a real opportunity to unlock immense value in discussions whether it’s via text or voice.

Big organizations that do a lot of insights collection and management can tap into this and deploy it at scale. Clients working with several models on the Gen 2 side are working with large-scale research buyers on the future of their insights organizations and developing the ability to unlock qualitative insights at the scale of unstructured data regardless of the form factor. It doesn’t matter whether it’s video or text, or voice; this is a huge priority for them. They want to be able to deploy this because they see the value of understanding people at a deeper level than what a survey can deliver, so it’s exciting times. 

It’s also scary; let’s be honest, this could replace researchers from a process standpoint. I don’t think it’s anywhere close to being able to replace the thinking and the overall analysis. Still, it can streamline many jobs for people who have traditionally been part of the back-end field operations and research industry.

Summarization of insights

Another critical use case of ChatGPT is the ability to summarize information. We spoke about this briefly in the past, but it is one of the most significant use cases of the generative AI model. With a little bit of coercing and assigning a persona, you can get to a level of surfacing important information. 

For example, from a productivity perspective, when you want to summarize something, you have to read an entire blog and then summarize it or read a whole set of reports and summarize it, which is not super intellectual. With, ChatGPT, you can summarize a complete transcript or an entire blog in five points if that’s what you need, which is a true game changer.

Many people in technology and software development believe we no longer need software developers because AI will write software, which is entirely untrue. In the practical, real world, you cannot mock up what a ScreenFlow should do, and then you got code that, you know, yes, there are code assists right now, same thing like summarization. They’re called assist tools. Having said that, if you write the first three words, it’ll write out the entire component. That’s how I think this AI will evolve around an assistant type of model; when you’re trying to do something, it will help you do whatever you’re trying to do at an exponentially faster scale. 

Uses of ChatGPT

The Managing Director of GreenBook shared an interesting story about his children’s school, a specialty stem kind of Charter School in New Jersey; they’re already drawing the line on this type of technology. They say there is a definite benefit of utilizing such technology, but it cannot be used for tests, writing papers, or real deliverables. It is fascinating that its implications would be different from an educational standpoint. How do we put kind of walls around this? And what are the use cases? Because there are a lot of use cases that could be nefarious and detrimental in many aspects of the world, not just within the research space, I think that’s going to be interesting as well.

While we were promoting the special Live with Dan panel discussion episode on the implications of chatGPT, we sent out a few emails to customers, partners, and the general MRX ecosystem, asking them to tune in. Someone wrote back to me, saying, ” Well, this is really, you know, current because we’re facing something like this in their research studies. Other than patterned responses, one thing that stood out was the response for writing excellent complete sentences that were well thought out and, you know, which doesn’t happen all the time. And you know this isn’t human-written. There is a big challenge –  with anything positive, there’s always going to be that negative side too.

The impact of ChatGPT on market research and what QuestionPro is doing about it

While we are speaking about the caveats of ChatGPT and its negative implications, I think there is also a need to talk about how the system impacts market research. 

In our world, for every virus, there’s an antivirus. In market research, survey bots, open ends, and other things fall under the gamut of data quality. And a lot is being done to mitigate the impact of lousy survey data on insights management. 

At QuestionPro, we’ve used ChatGPT to determine whether you’ve used that ChatGPT in our surveys. For our data quality product, we determine if somebody is using ChatGPT to respond to an open-ended response, so we get a pattern matching around it. If you’ve used ChatGPT to respond to an open-ended comment, we will flag that response, so that’s our short-term solution. Currently, that’s a cat-and-mouse game to some extent, but it’s a big deal to have been able to solve that problem natively.

As with every technology, this could lead to whether we found ChatGPT doing an excellent job grading the outcomes on those open-ended questions. And are we dealing with a lot of false positives?

To provide a little more context about how we are using technology to reduce the impact of insights in the path of Research 3.0, the answer lies in the tool itself. When you add a text comment box, we ask how you can answer the same question differently. Think of five different ways of asking the same question, then look at the answers coming out of ChatGPT, and if there is any pattern matching between somebody’s responses stating that they’re copying and pasting from ChatGPT, we flag it. To further prevent it, let’s say we know that these are the possible list of 2 or 3 ways you could answer this on ChatGPT, so when someone who has the survey changes the question and asks the tool that same question, the tool catches that early on.

There is a threshold on what part of the question or an interpretation of the question is asked to the generative AI platform and what probable responses are. If the threshold crosses a specific limit, 70% or 80%, is copied, then the answer gets flagged. We have already built this into the tool and are working with customers to increase the threshold.  

The first part is to build the system to do the catches and start the monitoring. Right now, it’s too early to say how many of these are getting caught since we need a large sample size, but we’ve moved quickly to get this natively ingrained into the system. So in around 30 days, we will share the level of accuracy and what level of importance we are catching right now. In a month or so, we can confidently say, “Well, we’re catching, you know, 80% of the stuff. 

However, this is bad or good, depending on which side of the table you’re sitting on to know the impact of ChatGPT on market research. 

Now the definite good side is a separate project we are running, which helps people design surveys to get to highly impactful surveys as quickly as possible. Not everyone who uses QuestionPro is a researcher. Due to the nature of the tool, it’s easy even for novices and non-researchers to use it to gather the insights they need. We sell to product managers; we sell to entrepreneurs who have an idea to start a company and want to do some research about this new product they want to build. These people who don’t have large budgets need quick turnaround time responses and agile research and will be significantly impacted by this tech. It is easy to plug into that ChatGPT to get a list of questions about the product that you could ask around usability, flexibility or on, price testing, and more, and that comes back. Then we put that into the survey so we can automatically create a survey in as low as 22 seconds about a particular field. As well as you know, it’ll help them, so you know it’s people having specific use cases on product innovation, pricing, or even due diligence.

However, the proliferation of technology in market research hasn’t made people creative thinkers. The MRX industry is so process-oriented that researchers are still doing more of the same things, even with an expansion of technology. 

What is the impact of digitization and ChatGPT on researchers?

Digitization in research and future research depends on how effectively technology aids market research. Each researcher has a different viewpoint and methodology for approaching insights. With time and the use of technology, expertise expands. With people not leveraging technology, there is a lot of potential disruption in the job market for people not growing their expertise.

ChatGPT may not take you the whole way. Still, suppose it saves considerable time to create a survey or analyze responses. In that case, it can free up time to work on more strategic initiatives, including analysis, posturing, data management, and more. 

With technology, you have to be careful that it doesn’t impede you. You have to consider it a way to kill repetitive tasks and improve operational efficacy. But relying solely on it may be counter-productive to your end goal.

This can be best depicted with an example. Content production can be easily automated even before but now so much easier with ChatGPT. It can take on a user persona and make content creation a breeze. However, the flip side of this discussion: can Google recognize that this is ChatGPT reproducing content and, therefore, your SEO strategy won’t work? Nobody knows yet how the algorithm will work. Let’s say you produce a fantastic blog, and Google says oh, this is ChatGPT; therefore, we will downgrade it. At the end of the day, this strategy doesn’t help. 

Making technology do the work for you, but smartly, is critical. 

Another example of driving home is how researchers can think of ChatGPT in day-to-day tasks. It’s around honing your skill around asking the right question and being able to iterate on those questions. When you ask the right questions and ask for the correct answers, you can get ChatGPT to summarize the key points, surface important information, and even do tasks like rank order from most important to least important, etc. This way, you can have your functional research assistant surface rich insights and create a more compelling story, but the process takes minutes. 

Does generative AI impact search-based taxonomy for knowledge management platforms?

Another distinct component of Research 3.0 and the use of technology is knowledge management platforms and insights repositories. With the use of ChatGPT, does it disrupt this model? And should you be worried as a knowledge management platform CEO?

An excellent way to look at this problem statement is that knowledge management (KM) platforms are taxonomies. With ChatGPT, you curate and summarize information reasonably quickly. You can structure the data and put it in the generative AI, and it figures out various ways to slice up data and dress it up any way you like. The implications for every research organization every buyer and organization says that they waste so much previous research that it just goes into a folder and sits there. Companies like Lucy AI have been trying to make a far more efficient process utilizing AI so that you can go through all of your historical data and find railing the points. We’re there now with these technologies allowing you to do just that. 

I can ask in the last year what males aged 18 to 24 think about “topic A,” and it will come up with a point of view based on the data reservoir, and that’s pretty remarkable. The infrastructure is such that you don’t know the technology can functionally be bolted on. Now all those projects you’ve got are the asset moving forward instead of having to have them all scripted and constructed. All of a sudden, you don’t have to worry about creating commonality across projects; you can generate discoverability and accessibility, and those are the two things necessary to implement this technology inside of your research. That’s been the Holy Grail that nobody has achieved, and we are on the precipice of being able to accomplish that.

In larger organizations, a lot of time is spent asking the same questions again for different reasons. Now, there is the ability to be agile and iterative across the entire organization and only explore new dimensions of insights that don’t have an answer. Now it also allows the ability to move past just trying to mine past information and start exploring new things that we haven’t gotten to because we’ve just been stuck in data structure and taxonomy.

The knowledge reservoir kind of model where multiple technologies you can query across numerous platforms cross, and so that’s what will be, is a definite win for the researchers as it becomes the common lexicon slash kind of storage. Cross-functional teams can surface insights across languages, studies, methodologies, and more in a commonality. 

If you can put all of this tribal knowledge into one AI repository and then query that AI repository, then it’s a huge win. With data tangibility, you can pump data into it with text which is the standard data format. This innovation and a query interface that is more a chat but a human conversational interface make this a game changer. 

Knowledge management platforms will be fine until AI repositories across cross-functional tools become a reality. They, however, must evolve to keep up with generative AI and machine learning. 

Negative implications of ChatGPT

We’ve spoken in detail about what ChatGPT can do. However, some negative implications are essential to note. 

One of the most critical components of this tool is plagiarism. The temptation is always to put a query in the system and paste the response. Algorithms are, in a lot of ways, responsible for how we view the world and how we view data. So those biases and prejudices will naturally find their way into these systems as well. So there is an inherent danger to paste without applying the like research expertise on top to try to eliminate as much of that as possible when expressing a population’s point of view.

To take on an exploratory research standpoint on any given topic, we’re seeing immense fragmentation in online channels. Still, a massive push of expert-led niche content is through platforms like Substack. There’s so much content on topics that needs to be curated and a lot of great stuff out there, but there is also this fragmentation. There’s no central point of truth anymore, and that can create a lot of opportunity and confusion. Depending on the persona that a user takes defines the answer, so in that sense, do generative AI and machine learning have ethics? 

Another drawback of ChatGPT is it can really help replace customer service or human beings? A few years ago, chatbots indirectly promised to make customer service reps obsolete. However, recent studies have shown that 97 out of 100 people still want to take to an actual human being. Can you ask your bank a question or if you’re trying to reschedule your flight or ask questions that need emotions? ChatGPT may not have all the answers! Yes, you can request to refine the answer, but the question framing is immaterial if the answer doesn’t exist. 

In its nascent stage, another negative implication of ChatGPT in market research is around technical know-how and costs. Karine Pepin, Vice President / 2CV Research has been extensively using GPT-3 to work out different models to work coding open-ended survey responses. Some of the notable things she encountered are mentioned below in her own words:

• To code open-ended responses, since ChatGPT cannot scour data through Google Sheets, using the API model is the only way forward. Even though the API is relatively simple to use, integrating it into tools still requires some technical assembly, and it is not as straightforward as one might expect. And since the GPT-3 trains on the generally available internet knowledge, it cannot understand tribal knowledge related to the survey. As a result, additional survey-context-sensitive information, such as how to code responses or codes to categorize the reactions (i.e. a coding frame), must be included in every API prompt call. Depending on the underlying model used, the cost differs. However, the cost and time factors are very high at a nascent stage.
• It’s possible to create custom versions of GPT models using sample prompts. It is especially useful for classification exercises like coding, as it allows the AI to understand the context of the task through coding examples (i.e. code frame). The quality of the model, however, is still purely subjective. 
• ChatGPT can help you identify leading survey questions. However, it is inefficient to input prompts one-by-one since GPT-3 is not scalable. OpenAI’s potential is tied to the underlying GPT-3 engine, which requires some technical knowledge to program and train to improve accuracy in a variety of scenarios.

How far back does knowledge need to go to be effective? 

In our panel discussion on LiveWithDan, recently, a user was curious that since the knowledge of ChatGPT or GPT-3 doesn’t go beyond 2021, how does that affect the quality of output? And that brings up an interesting point about how market research used to be highly longitudinal in nature but has now evolved post the pandemic to ad-hoc market research. Comparability across data sets has completely changed because the world has changed how we know it! Going from traditional 70% longitudinal research to high-frequency research and discovery, A lot of the research is also DIY to the end that the frameworks are much more in the context of primary market research being gathered today and analyzing that data in the context of that data set as opposed to pulling from historical points of view.

It does bring up an interesting opportunity for innovation if you combine this technology with other predictive technologies. There’s a lot of opportunity to take the historical information and shift it on its head to be more predictive. Brands and researchers want to know what happens next, and if there’s a way to combine past data with that, it’s great.

Regression analysis is a big part of insights management in 2023, where we take stated purchase intent and compare it to similar purchase intent or historical purchase to like products. Then that gives us the weighted, you know, probability of that outcome.

Is ChatGPT a threat to traditional market research or a superpower?

A pertinent question that we have seen murmurs about in the past few days is with ChatGPT, does this make end users and clients self-sufficient and do more themselves or is this a superpower or a mix of both? The short answer is, it definitely is a superpower and it’s path-breaking. Not on the level of the internet but it’s certainly up there. It is definitely an efficiency accelerator and it improves efficiency by an exponential amount.

There are limitless possibilities to leveraging ChatGPT in market research. It is just going to need the community to come together and brainstorm different ideas to make this work for the industry at large. 

Does generative AI impact panel and research quality?

There is a case to be made for AI bot panels and the general integrity of traditional panels being threatened by such technologies. Traditional panel supplies do 30% of most research. There is already an issue in dealing with fraudulent responses, bot farms, and other data quality issues in insights management. 

Research needs a paradigm shift to move away from purely transactional surveys to more collaborative and conversational forms. Engaging with respondents offers a better experience that’s holistic, iterative and natural and that will go far towards addressing these types of issues.

Conclusion

ChatGPT is fast, easy to use, and highly intuitive in its current form. But it cannot replace researchers and operate in a silo to complete modern tasks like creating a survey to analyze and synthesize insights. There are specific use cases for both qualitative and quantitative market research with a generative AI tool like ChatGPT but the tool is too native, and building models is complicated and not as cost-effective either. This technology definitely puts us well into the path of Research 3.0, but it isn’t perfect yet. Maybe GPT-4 takes us to the next level? All we can currently do is wait and watch. 

References

• https://www.linkedin.com/posts/karine-pepin-1802625_chatgpt-mrx-survey-activity-7018939579596476416-W1LX?utm_source=share&utm_medium=member_desktop
• https://www.linkedin.com/posts/karine-pepin-1802625_chatgpt-mrx-activity-7021113906274193408-yDHF?utm_source=share&utm_medium=member_desktop
• https://www.linkedin.com/posts/karine-pepin-1802625_mrx-chatgpt-survey-activity-7021551433720242176-xczS?utm_source=share&utm_medium=member_desktop
• https://www.linkedin.com/posts/adrian-stanica-56a734_market-research-done-in-minutes-when-i-ugcPost-7017350755930046464-9S6I?utm_source=share&utm_medium=member_ios
• https://newmr.org/blog/chatgpt-is-ushering-in-research-3-but-what-is-research-3/
• https://www.greenbook.org/mr/market-research-technology/chatgpt-and-implications-for-market-research/
• http://www.researchnewslive.com.au/2023/01/16/chatgpt-ai-a-market-researchers-best-friend/

The post <h1>The good, bad & ugly of Generative AI & ChatGPT in the future of insights!</h1> <h3>Generative AI and ChatGPT are moving us toward Research 3.0. But what does that mean for you?</h3> appeared first on The Experience Journal by QuestionPro.

Table of Contents

• Introduction & Executive Summary
• History of CX & VOC Programs
• The Why!
• Root Cause & Systemic Upgrades
• Outer Loop – The Process
• The Intersection
• Conclusion

Introduction & Executive Summary

Identifying and isolating specific customer feedback for follow-up triage and action is familiar, indeed commonplace. Closing the loop with survey respondents who may have expressed dissatisfaction, offered constructive criticism, or experienced friction at a specific touchpoint is considered table stakes in a modern voice of the customer (VoC) program. Such follow-up not only allows an opportunity to course correct and make a bad situation better but also sends a clear message to the respondent. “We’ve listened, considered, and now we are acting on your behalf”, it says, and, in many ways, a straightforward action reaffirms the importance of the feedback shared, making the customer feel valued for their loyalty and direct input given. 

What if you see more significant and systemic themes appear in your customer feedback? What if big and strategic parts of your customer experience must be in sync with your market and customer expectations and go beyond only a few customers or cases? What if solving the root cause most closely linked to this outcry of dissatisfaction went beyond what a single rep could do? What if there was a need to re-engineer a key business process or even a part of your product to truly satisfy, let alone delight? What then? 

We propose that VoC systems connect the Closed Loop systems to an Outer Loop / Systematic process changes/change management system. 

This connection is necessary for systemic and lasting changes to improve the experience at the speed of business and expectation. 

History of CX & VOC Programs

Thinking back to early VoC initiatives, particularly early Net Promoter Score (NPS) programs specifically, those early programs too often fixated on closed-ended feedback – scores, mostly. Even the originator of NPS, Fred Reichheld, theorized we’d only need a single question to assess loyalty, as he wrote in his seminal The Ultimate Question book when he first described NPS to the world. Asking someone, he stated, about the likelihood they’d recommend a brand to a colleague or friend would be the ultimate measure of satisfaction as measured by their advocacy. Indeed, that makes sense, even if slightly misguided.

Knowing your customer rated you as a high scoring “9” or a lower-scoring “3” is directionally exciting and can serve as a good marker for how they feel about their experience. Anyone can see that a 9 is likely a far better and more coveted score than a three and is easily explained and understood. 

Where the notion of an “ultimate question” falls short, however, is that while directionally interesting, a score alone is virtually impossible to act upon and recover from. There could be dozens of reasons a high-scoring promoter loves a brand and another dozen why they might not. The score alone is, at face value, insufficient to direct any meaningful recovery or even more than a surface reaction. 

I’d find it impossible to think of a business story that illustrates this better than to share one from a few years ago. A senior leader at a US-based HCM (Human Capital Management) and business services company with whom I’ve been doing some CX project work called me into his office. He told me, “I just paid a large consulting firm more than 2 million dollars to build out my NPS program, and now I get my scores on a 2x per month scorecard.” He said, “Some months the score goes up. Other months the score goes down,” as he drew in the air, with his finger, what looked like a hospital EKG report with a heartbeat “blips.” Up, down, back up again, back down. 

Here was the punchline. He went on to say, “The months where my score goes up, my boss calls, and I’m a hero. Other months when it goes down, my same boss calls, and I’m a moron.”, he said, grimacing. “I just want to maximize my hero-to-moron ratio.” It was clear that he had no idea what was behind the score he tracked month in and month out, and worse, had no idea of what to do about it, as they followed the Reichheld approach of just pursuing that magic, single question resulting in a score and little else.

Chasing scores was very common in the early days of NPS as it brought executive bragging rights to many, and that’s what people most wanted, it seemed. At least until they could not do much with it other than brag, they started to see how more would be needed. Even Fred Reichheld agreed and revised his original single-question thesis.

The Why!

Reichheld and his Bain colleagues returned to their authoring roots and updated their original writing, now called, The Ultimate Question 2.0, to incorporate more than just the one question and in fact, opened it to a critical and overlooked initially companion question that would unlock the reasons why someone gave the score they did and do this in their own words. The simple power and elegance of a question such as, “tell us why?” It would unleash the true power of NPS and CX more broadly.

The visibility of why someone scored us the way they did proves to even the staunchest naysayer that customer feedback could fuel significant transformation and overall business impact. 

If that old client who could not articulate how to satisfy his customers better and in it, keep his boss on the right side of his day with only a score and nothing else, now could know exactly where to focus his energies and, more so, even consider root cause and which ones would ensure the greatest and most positive impact for all. It was the key that unlocked the true power of great customer experience.

The same team who brought us NPS and, to a large extent, the original methodologies of closing the loop based on understanding the root cause of specific scores and, more so, customer dispositions also envisioned another situation. Early on, they taught us the importance of closing the loop and following up with survey takers who shared either highly negative or positive feedback with appropriate and targeted outreach. Rescuing detractors and amplifying promoters grew in importance and became staples of most VoC initiatives. 

However, it would become clear to many that it was realistically possible to gather feedback and see root causes emerge that were bigger than a series of easily rescued customers or one-off and isolated squeaky wheels. If you heard only a few complaints about your pricing, a small token coupon or discount would satisfy. That could be quickly initiated case-by-case by a single customer service representative as needed. Single customers could be rescued and even delighted by a single recovery action. 

Root Cause & Systemic Upgrades

What if you begin to see larger and more systemic themes appear in your customer feedback? What if big, strategic parts of your customer experience need to be in sync with your market and customer expectations, and they go beyond only a few customers or cases? What if solving the root cause most closely linked to this outcry of dissatisfaction went beyond what a single rep could do, and what if there was a need to re-engineer an essential process or even a part of your business operation, product, or service to satisfy, let alone delight truly? What then?

Reichheld and his colleagues described a systematic approach to vetting and acting on this more strategic feedback as an “outer loop”; in effect, comparing it was what they similarly called an “inner loop,” which described the 1:1 feedback handling and close follow-ups. 

Outer Loop – The Process

The outer loop, as described, is a process of tackling larger-scale, more systemic challenges, suggestions, and opportunities for improvement that go beyond a small segment of customers or problem solvers. Often, the outer loop process can involve a large multifaceted team and if implemented well, can have a lasting and positive impact on many customers. A clear “win-win,” Reichheld wrote. 

Evaluating outer loop feedback for action is complex and to be taken seriously. Efforts to make large-scale and systemic changes to a business and its operation are expensive and impact many. As such, they require input and consideration from diverse collaborators and solution designers. Vetting these ideas in terms of feasibility and impact are crucial underpinnings. These decisions will make the difference between successful change and little more than a money pit of bad ideas.

Outer loop tools have emerged as components of the most advanced VoC and CX platform solutions, closing the gap between feedback solicitation, analytics, and this new collaborative vetting and action management. Some of these systems have taken a crowdsourcing model of idea evaluation with Reddit-style voting for the best ideas and a natural burying of the worst. The best ideas can be prioritized and managed as they mature and take shape based on regular evaluation, consideration, and collaboration. The result has the potential to be transformative and stunning and impact the experience many, if not all of our customers have.

Driving change and not simply problem-solving is fundamental in our customer experience toolbox, and ensuring the changes we choose to pursue are the right ones, at the right time, for the right customers is what the outer loop is truly about and what it enables us to do.

The Intersection

Interestingly, there’s also a natural intersection of both the inner and outer loops. Very often, as we open surveys to feedback, we may see initial traces of feedback that could make us think the input is only a couple of small isolated situations and pursue easy rescues. Only when things emerge to be more systemic and recurrent do we promote them to the outer loop and look at solution options beyond a short-term reactive measure. 

Conclusion

As a final thought, I’ll leave you with this. Simple inner loop feedback handling is very similar to that old carnival game, “Whack a Mole,” where a player has to hit a stuffed mole that pops its head out of one of 9 holes in a wooden playing board with a large mallet. The mole quickly moves from hole to hole and is a genuine reaction time test. Imagine that the mole, in this case, represents your customers sharing feedback.

An inner loop is an effective tool to help satisfy one mole in one hole at one time, but you will forever be firefighting in a reactive mode and unable to pause and consider more scalable solutions for more moles in more holes. By its very nature, the outer loop ensures we are strategic in our problem-solving and innovation and can address all at once if done well and handled thoughtfully.

The power of the pair, inner, and outer loop together is immense and should serve as the foundation of every VoC program from now on. Without a balance of the two, you’ll be forever chasing customers who, like those moles in the game, pop up unexpectedly and move quickly and, without it, risk losing control, let alone the loyalty of your customers served.

The post Connecting the dots – A holistic approach to CX appeared first on The Experience Journal by QuestionPro.

Summary

This article talks about how we can better utilize the data collected at various digital interaction points. It requires a significant amount of time, energy and resources to be invested in the data collection exercise and deriving meaningful insights.

Data collection also comes with three inherent challenges – Scale, silos and bias. This further creates a challenge in turning data into insight. Research repositories are a powerful instrument for helping to solve the data problem. However, they need better integration within the datasets and research methods. Here are the solutions to these 3 challenges.

• Starting from the analytics frees us from allocating research efforts based only on roadmap priorities, pet projects/hunches, and known UX issues.
• Once a problem is identified, start viewing it through layers of filters and segments that eliminate as much of the noise as possible, leaving us with just the most relevant data. 
• By integrating these qualitative and quantitative sides, create a constant iterative loop between observation and investigation.

Table of Contents

• Summary
• Thinking about our relationship with data
• The big 3 problems of experience data
  • Diving in: How 3 big data problems manifest
    • More data, more problems
    • Things you never knew you never knew
    • Oceans apart, day after day
• Better integration leads to better insights
  • The rise (and fall – or transformation?) of research repositories
  • Integrating research from the beginning
    • 1. Solving the problem of bias
    • 2. Solving the problem of quantity
    • 3. Solving the problem of siloing
• Conclusion

Thinking about our relationship with data

Every year, the week spanning from Christmas to New Year’s is the busiest travel period of the year, as people around the world embark to be with family and friends during the holiday season. In America alone, an estimated 112 million people – roughly a third of the total national population – were traveling in this period. And that’s just in one country. 

Just a few weeks later, millions more worldwide were embarking once again on holiday travel for the Lunar New Year. China saw roughly 226 million people traveling in this period, and while it’s the largest nation with major Lunar New Year celebrations (and the accompanying holiday travel), it’s hardly the only one. 

From the data above, it’s safe to say that many of us have been on the road or in the air in the last couple of months; and if you weren’t, there’s a good chance someone else was traveling to visit you instead. That being the case, there’s a shared digital interaction that many of us experienced in that window. Whether you were planning to drive a few hours to see family, or you were looking into flight tickets, or you had relatives visiting from out of town, state, or country… you probably opened your phone’s weather app and checked the forecast. 

Should I drive over in the morning or the afternoon? Friday or Saturday? What are the chances it’s snowing when I do? How likely is it that Aunt Linda’s flight gets delayed due to weather? When will be a good time to take my parents on a walking tour of the neighborhood? 

Whatever questions were floating around in your head, the weather app had an easy answer ready. Meanwhile, though, on the other side of that simple interaction is a meteorologist busy predicting the weather trends for the next several weeks in your region. She’s looking at large swathes of data collected from satellites, radar, surface maps, and more, comparing it to historical patterns, and collating and condensing all of it into simple answers to be displayed to users like you and me on our weather apps. 

All of that meteorological data they’re working with is valuable – but in a practical sense, that immense value only manifests once the meteorologist has analyzed and interpreted it into an accessible weather report. 

If we instead opened our weather apps to find satellite readouts, radar maps, wind speed tables, and decades-spanning historical climatic datasets, 99% of us would be no better prepared to plan that long drive to Grandma’s than we were before. 

* * *

At this point, I could turn this into an article about the value of experts. (Indeed, there is certainly a conversation to be had about the current trends in the data on public trust in experts). However, while that’s definitely part of the picture when it comes to topics of data analysis, my point here is really about the data itself. 

We are in an age now where the amount of data we have access to is frankly staggering. How did we get to this point? Perhaps it started in 2017 with this Economist article calling data the world’s most valuable resource. Maybe it began further back in the 1960s, when we first started using computers for decision-making support systems. Or maybe our obsession with it dates back even to the 19th century, with the advent of time management exercises. Whatever the chain of events, there is so much data around us that we’re practically drowning in it. 

Some might say that the world today runs on data. But I’d say that that’s not exactly the case. Rather, the world runs on the actionable insights gleaned from data. 

Without doing the work to truly understand what our data is telling us, it’s all but useless. 

The big 3 problems of experience data

I have no doubt that everyone reading this has at least a few data collection tools that they regularly use in their work. In the User Experience / Digital Experience space, we rely on many different kinds of data: web analytics, behavioral data, survey responses, usability testing sessions, interview findings, and more. 

It’s also almost certainly not news to you that you have to analyze that data to find the proverbial diamonds in the rough – I know that it’s not a groundbreaking assertion. 

I think it’s important, though, to set up this discussion with the above framework. When we collect any kind of data, it represents a commitment of time, money, and energy to actually get something out of that exercise. So naturally, it’s important that as we engage in data-gathering activities, we have a clear understanding of the inherent problems that can potentially undermine our work, and make it overly time-consuming, inaccurate, or just flat-out wrong. 

So let’s talk about the big problems with the data we collect in the experience research space. In my view, there are 3.

1. Problem of SCALE / QUANTITY: 

The first problem has to do with scale. Many times, the amount of data collected from research activities far outstrips the time in the day that we have to analyze it. What compounds this problem is that within that huge quantity of data, only some of it even contains the answers we’re looking for. By definition, major portions of practically any dataset will consist of noise. That noise doesn’t negate the value of the truly meaningful pieces of data – but it does make it more time-consuming to find those pieces. 

2. Problem of BIAS: 

The second problem is related to bias. Bias can enter the picture at many levels, but one that is often overlooked is the very question of which datasets to collect. It’s easy to forget that this very basic initial decision is itself subject to our biases about what’s important, what we expect to find, and what problems we do or don’t already know about. As useful as a hunch can be, it can also lead us to collect data to investigate a pet project when a more worthwhile issue is flying under the radar. 

3. Problem of SILOS: 

The market for research tools is now filled with a great variety of powerful tools that enable different data collection methods. In many organizations today, each internal team has its own subscriptions to the different tools it has chosen to spend budget on. But with different unique datasets spread between so many different tools used by so many different teams, we’re often missing out on the complete picture. Each of us has only one slice of the whole – like the 6 blind men touching the elephant. 

Diving in: How 3 big data problems manifest

More data, more problems

The problem of quantity is an obvious one, and one you’ve probably faced yourself. Deciding where to dedicate your time as you perform data analysis can make the difference between finding that diamond in the rough, or brushing right past it. Of course, you could always increase the amount of time spent analyzing your data, to ensure that you’ve combed through every corner of it from every angle. 

Unfortunately, that isn’t actually a feasible strategy – we all have other responsibilities to take care of, and spending time on analysis inherently involves a tradeoff for time spent on other things. Instead, the solution must necessarily involve using the same amount of time better, more efficiently. 

Things you never knew you never knew

The question of bias in the datasets we collect, as I’ve argued above, is often overlooked – but I believe it’s an extremely important one. 

Imagine that you’re preparing a usability test to get feedback on your website’s user journey. You write a series of tasks leading the participants from Page A, to Page B, C, then D. You run the test and hear all sorts of opinions from respondents about what could be improved. You spend time analyzing that data, collating the findings, and then updating the designs of your funnel pages based on what your respondents told you. 

But, the A-B-C-D user flow that you tested was based on the ideal-case user journey that your web design intends for users to follow – when in reality, the most common pathway your website visitors follow starts at Page C, and then goes -A-B-A-D-X-Y-Z.

The fact is, it’s often not easy to know what steps actually constitute “our user journey.” It’s often not easy to know where the root of a problem actually lies. If a problem that’s happening deep into the user journey is triggered by an action users can take way back during signup, you may not even know to look there. And then there’s the obvious difficulty of collecting data for a problem you don’t know exists. If you don’t even know it’s there, how can you know the data you should be gathering? 

Oceans apart, day after day

This brings us to the problem of siloing – because sometimes, the answers we need are actually available somewhere – just not to us. Maybe the marketing team has access to data about top entry pages and user pathways that would have helped with that user test setup. Maybe a PM from a different team has a tool that flags rage clicks and errors, but the issues they’re prioritizing don’t overlap with yours, and so no one notices issues that are afflicting your product. 

How many designers would benefit from some of the traffic and behavioral data on Google Analytics, but don’t have access to it? How many have access to it, but just don’t have the advanced Google Analytics knowledge needed to drill down and find the relevant information? After all, it’s not an easy platform to figure out (if it was, there wouldn’t be such a thing as Google Analytics certifications); and if you don’t know your way around, you might never find the data points you need. 

The outcome of all this is that many teams today are making important decisions that affect the organization’s bottom line, based on the incomplete view they’ve gleaned from just their own research projects, tools, and datasets. 

All 3 of these big problems lead back to our overarching problem set out at the beginning: How do we turn data into insight? The overabundance of data fuels inefficiency in finding insights. Bias in the collection process skews the reliability of our insights. And siloed data leaves us to fill in the blanks in our insights with assumptions. 

Better integration leads to better insights

In the above sections, my goal has been to clearly lay out the overlapping challenges we face when relying on data in our work. In this next section, my hope is twofold: to (1) identify in concise terms a trend that is already beginning to take form in our space; and (2) reflect on the best ways forward. 

The trend I’m speaking of is the drive for experience data integration. 

The rise (and fall – or transformation?) of research repositories

One of the clearest reflections of this trend, in my view, is the rise of research repositories as an important tool for UX, product, and marketing teams. Even more specifically, though, this trend has been followed almost immediately by another one you may have noticed: that of established research companies building or acquiring research repositories to supplement their data collection tools. 

We at Trymata, for example, acquired the repository tool Considerly; our competitors at UserZoom acquired EnjoyHQ. Qualdesk was bought up by Miro. QuestionPro, rather than making an acquisition, has built their own research repository in the form of InsightsHub. We can only assume this trend will continue; already the number of independent repository companies has dwindled as one after another follows this very logical outcome. 

Research repositories are a powerful instrument for helping to solve our data problem. By allowing researchers and other data users to aggregate datasets of all kinds and provenances in one place and analyze it in enormous cross-sections, these platforms strengthen the quality of the insights to be gained. Surveys, interviews, user tests, behavioral data, and more can be pulled together and compared, filling in the various gaps that each method by itself leaves. 

Despite these notable benefits, repositories by themselves are not the perfect, heaven-sent solution to our data problems. With a repository, your data is still only united at the end of the collection process. To truly improve our relationship with data, the integration must begin much earlier. 

Integrating research from the beginning

To truly solve our big 3 data problems, we need to look further back in the research process. Functionally, this means using different research methods and data types in a constantly reinforcing cycle, so that they’re never really separate processes at all – just different threads in the same research tapestry. 

We’re already starting to see success with the integrated digital experience research approach we’ve recently undertaken here at Trymata. Late last year, we decided to make a bet on this very idea – the idea that different research methods are better together. So, in addition to our user testing tools, which was until then our sole offering, we unveiled an integrated product analytics suite. Traditionally, many of the data types available from the new suite would be used primarily by marketers, product managers, or even technical teams: for example, web analytics and traffic data like you can find in Google Analytics, and behavioral tagging like rage clicks and error clicks.

We believed, though, that the designers and UX researchers that comprised the majority of our users could benefit greatly from this data, and that, in fact, they should be using it to inform the way they do user research and make design decisions. Not only that, but we believed equally that the marketer and PM type users that would traditionally engage with the product analytics data would benefit just as much from integrating their quantitative datasets and behavioral methods with the in-depth user interviews and feedback sessions available to run through our integrated user testing suite. 

While still in its early stage, this combination of research methods and data types in one place has allowed us – and our customers – to start experimenting with an integrated research process where all of these datasets inform and enhance each other. 

The next 3 sections will address in specific terms how this approach can help to solve each of the 3 big problems with data. 

1. Solving the problem of bias

By starting with web analytics data, we learn about what’s happening in key user flows at a macro level, and objectively evaluate what those flows actually look like, and how they look over time. We can measure performance and set benchmarks for our existing goals using a variety of real usage-based KPIs, and observe improvements over time as we fix known issues. 

Importantly, we can also learn about patterns we didn’t know about. These may be common pathways we didn’t know users were taking, or clusters of frustration indicators like rage clicks or dead clicks. It may be exit rates or bounce rates that are higher than we’d like. It could be differences in behavioral patterns between user segments, or even device or language trends among groups of users. Any of these could be a hint for us to dive deeper into the relevant behavioral data, or to run a usability test to learn more. 

Starting from the analytics frees us from allocating research efforts based only on our roadmap priorities, pet projects/hunches, and known UX issues. We can open our eyes to research opportunities, both known and unknown, and weigh all of them to strategize a more unbiased set of research goals and testing plans. 

What’s different about this approach?

None of these pieces of data are new or revolutionary in and of themselves; you may be checking some of them already in different tools. The key differences here are (1) the intention with which we’re looking at this data, and (2) the application of the knowledge as it relates to other product research and decision-making processes. 

As far as point (1), the idea here is that web analytics should not just be used to track KPIs and measure performance, but to actually constantly assess your research and design priorities. 

If you’re mainly checking your traffic numbers, exit rates, time on page, rage clicks, and other such metrics just when it’s time to report on your KPIs, and not as part of the actual research and discovery process, you’ll almost certainly see different things in the data. Approaching the data with curiosity and open-mindedness, and with the intention of noticing new things, will affect what you get out of that exercise. 

As far as point (2), you can then apply what you notice to your decisions, both about the research projects you choose to run and the design projects you choose to work on. 

If your team currently runs usability tests and other in-depth qualitative studies only when releasing a new feature or evaluating a feature that’s already been slated for design updates on your roadmap, you’re far from alone. Many teams distribute their research efforts in this way. But the fact is, there are many avenues of inquiry that are worthwhile beyond these specific moments. 

If your macro data reveals different pathways that users are following, hidden inefficiencies in your navigational layout, undesirable usage patterns among certain user segments, or dips in the numbers for flows that were thought to be safe – any of these can be followed up on with more thorough and targeted research. Just getting those opportunities on your organizational radar and into the list of possible projects is already a win. 

Lastly, there’s a key difference related to (3) access. As mentioned previously, in many organizations these insights are not distributed to all of the teams that would benefit from them, or considered together in the same decision-making flow. By bringing these data types together in a single research platform, we reframe the relationship between them and, hopefully, the way people conceptualize that. 

2. Solving the problem of quantity

Once a problem is identified within the traffic data (or picked from the list of known issues), we can take that same data and start viewing it through layers of filters and segments that eliminate as much of the noise as possible, leaving us with just the most relevant users/session data. 

Once we’ve reached a scale of data that’s manageable – and on-topic – it becomes much more feasible to start working with behavioral data: for example, examining individual users’ event logs and session replay videos. This kind of analysis is undoubtedly “nitty-gritty,” but it’s also what enables us to analyze and understand issues from a human perspective. 

This is the point where we start reaching a deeper level of insight into our users, and what we can do to improve their experience (and our performance); but it only happens after we’re able to effectively eliminate or ignore huge amounts of the data that’s been collected, in favor of the few handfuls most likely to answer our research question. 

What’s different about this approach?

Chiefly, the difference here is about the transition from the macro-level, quantitative data to the micro-level, qualitative data in the investigative process. 

When those two pieces of the puzzle are separated, housed in different platforms with different terminologies, collection / measurement methods, and base datasets, it’s harder to make the jump. If you’ve ever tried to line up Google Analytics numbers with the corresponding numbers from a referring campaign or other marketing analytics platform, for example, you’ve probably had to deal with significant discrepancies between the data from the two sides. 

With a platform that combines the big-picture analytics of Google Analytics with extensive behavioral data that can be examined at the level of an individual user, there’s essentially a lossless transition. If you want to know why the data shows a drop in throughput, you can open up the exact list of user sessions relevant to that question and watch user sessions, or scan navigational logs and event logs. While many integrations with the usability testing side are yet to come, you can imagine even finding the web analytics for specific test participants and combining those views. 

3. Solving the problem of siloing

At this point, of course, the other thing we can do is to run small-scale, targeted usability tests on the flow we’re investigating. 

We’ve identified the issue(s) we want to explore and explain, and we’ve learned about which users are relevant to the issue and how they’re acting. Depending on what’s come out of the behavioral data, though, we may or may not yet understand why they’re behaving the way they are. 

This is where we jump to the usability testing side of the platform, to set up a scenario and tasks that will replicate the twists and turns of the user journey we’ve observed already, and get really solid qualitative feedback about what’s going on in people’s heads throughout that experience. 

That’s not where the idea of de-siloing ends, though. Once the user tests have been run, the data analyzed, the necessary design changes proposed and executed, we can go right back to our web analytics data and start watching what happens. 

If we optimized our designs to decrease abandonment, do we actually observe a decrease post-release? If we were trying to optimize an inefficient navigational pattern, do we actually observe users following the intended pathway at higher rates? 

By integrating these qualitative and quantitative sides of the equation together, we can create a constant iterative loop between observation and investigation that’s accessible to everyone involved. 

What’s different about this approach?

The big idea here is that iteration is not limited to just the design side of the product sphere. Research too can be constantly optimized, honed, and refocused in tandem with design and engineering. By ensuring that research decisions are an active part of the iterative cycle, we can better ensure that we are optimizing the choice of product questions that are asked, investigated, and solved. 

Again, this is not a revolutionary new discovery. Rather, the point is to encourage a mental framework in which research is not an appendage of roadmaps that are chosen beforehand, but an active driver of the roadmap in itself. By discussing a way of thinking about this process in concrete, well-defined terms, and supporting it with an integrated platform that embodies that way of thinking, I hope to positively influence the way this flow is conceptualized in organizations that still have room to grow in their research processes (which is all of them – ourselves included!)

Conclusion

In summary, a workflow like described here helps to address and eliminate the 3 major problems of data and insights in our industry in multiple, layered ways. 

1. By starting with all of the web data, you can reduce the bias of where you’re looking, and what you’re running user tests on. 
2. Segmenting your product analytics data and collecting small-batch usability testing feedback allows you to focus on a manageable quantity of data to solve a specific problem. 
3. Doing it all together in a platform designed for product and marketing people to use without a high level of expertise makes it easy to get a holistic understanding without leaving out a key piece of the puzzle. 

While our vision of a seamless digital experience research workflow is still in its early stages, we are continuing to develop our platform to support an integrated workflow that’s easy to execute and enables you to reach a higher level of success turning your data into insights. 

The post <h1>The 3 Big Problems with Experience Data – and How to Solve Them</h1> <h3>What we can do to combat problems of scale, silos, and bias</h3> appeared first on The Experience Journal by QuestionPro.

Summary

Delighting a customer can be an uphill task if you don’t constantly innovate. You may ‘satisfy’ customers, but not ‘delight’ them. Innovation at every step of the customer journey – technology, product, process, service, etc. ensures a ‘wow’ factor each time a customer interacts with your brand. Employees form another important touchpoint and so working on employee engagement and motivation can significantly improve customer experience.

This article talks about innovating at various levels, the challenges encountered and how you can gain customer loyalty.

Table of Contents

• Summary
• Introduction
• Technological Innovation
• Product Innovation
• Process Innovation
• Service Innovation
• Innovation Challenges
• Importance of employee engagement
• Design thinking
• Conclusion

Introduction

Delivering the right experience remains the key component of success for companies differentiating their brand and offerings from one another. With an ever-evolving growth and demand pace, Innovation remains the driving force behind a company’s success. Enabling creative ways of engaging customers to deliver unique and memorable experiences requires a shift away from a product focus to a customer-centric focus. How can companies then remain relevant? We look at innovation from two paradigms – the outer view of the organization (listening to customer needs) and the inner view (listening to employees). We can then segregate innovations based on process, product, service and technology. In this article, we will explore the benefits, impacts and barriers faced within each facet of driving innovation. Ultimately the customer journey plays a vital role in understanding what, where and how innovation applies, but for the sake of keeping the reader engaged I won’t go into the customer journey mapping process itself or the importance of customer journey mapping and the tools associated with it.

Technological Innovation

Technology has undoubtedly revolutionized the way companies operate and interact with their customers. On the outer view, there is a widespread use of digital tools with the internet, smartphones and social media, allowing customers to be ever-connected to brands and also driving purchase and interaction decisions. The upside allows companies to personalize experiences with their customers, track customer lifetime values, and understand customer patterns and behaviors, all while keeping them informed.

Let’s take, for example, chatbots and AI-powered virtual assistants that provide support, accessibility and availability to customers at all times. With the inner view, companies can collect, automate and integrate how data flows between platforms providing powerful dashboards with insights that help foster quick decision-making alongside realizing ROI on key initiatives.

Machine learning algorithms and robotic process automation (RPA) allow companies to automate repetitive tasks, which in turn free up resource allocations helping save costs. Another use of emerging technologies lies in the use of virtual reality (VR) and augmented reality (AR) to create exciting and immersive experiences. For example, real-estate companies now provide tools to allow customers a walk-through of properties, or even map out how furniture and artwork can be placed around. Similarly, retail companies provide platforms allowing customers to try out clothes, or shades of cosmetics, virtually to enhance the ease of purchasing online.

Of course, there are downsides to the use of technology, as not everyone can easily maneuver through these platforms. How often have you been frustrated trying to reach your bank with regards to a service only to be greeted by a virtual voice assistant that’s trained only to understand a small subset of the language used across multiple segments and personas of customers by eventually disconnecting your call and not letting you talk to an agent? Well, ChatGPT is a great example of algorithms learned well and with time we can only hope for these services to get trained and be better than the human connect. Or so I think anyway!

Product Innovation

Product Innovation, on the other hand, can be viewed through new product development or enhancement of existing products. By understanding customer needs and preferences, companies can deliver value to their customers in more meaningful ways. Different types of innovation can be used to improve product ease of use and increase accessibility or make the product more intuitive. Design changes to improve user experience, aesthetic appeal, or even grow product attractiveness within specific segments of buyers.

The real impact can be realized by driving long-term customer loyalty when the product is done right, and that’s the reason why companies should focus on listening to customers. Care should be taken when innovation in the new product development space is implemented because this could either enhance cross-sell opportunities or can also come at a price of impacting brand value. For example, a famous luxury brand lost its valuable clientele because it decided to diversify and make the same products (aka handbags) more accessible to a wider range of audience which in turn hurt its image and shareholder value as it lost customers who wanted to associate themselves with exclusivity.

So, while the outer view might dictate a need for a segment of affordability, from an inner view, they should strategize better to rebrand and disassociate. Elongating my point here, there are several high-value luxury brands that only provide a diversified range of products like cosmetics and perfumes that cater to a larger segment of buyers. This strategy allows for the brand association but not the primary products.

Process Innovation

A derivation of total quality management (TQM), the ultimate goal for process innovation remains improving efficiency and productivity while reducing friction in the company, which in turn requires rethinking and redesigning the way work is done within companies. From an outer view, this involves increasing convenience for customer interactions with company services. Simple examples include streamlined logistics services that enable customers to understand exactly when their products will be delivered, including abilities to track the logistics delivery stages or reach the right customer contact agent to clarify doubts, concerns, or issues. Companies then need to have mechanisms to track issues and close the loop.

From the inner view, companies can utilize technology to automate their processes, track process efficiencies and derive information on the productivity of resources. The outerloop is a mechanism that allows for operational efficiencies to be tracked by companies and associate the inner and outer views more efficiently. Order delivery and fulfillment systems are the best examples of improving customer journey by using intuitive and user-friendly interfaces.

Communication remains key, but providing the right messaging that delivers empathy for process-gone-wrong is an important aspect of consideration as well. Further use of data analytics and insights can inform decisions around broken processes that might be unknown to the company. QuestionPro’s NPS+ allows for collecting feedback alongside ideas for improving customer experiences which, at a deeper level, allows companies to realize customer churn. Data can also help analyze customer behaviors and preferences that can help improve the process delivery.

Service Innovation

The most important component of service delivery is creating new and unique ways in which service value is delivered to customers. Personalization in service delivery makes experiences memorable! Equally important is the inclusion of empathy when communicating with customers. In the digital world we live in, digitizing empathy is very important to include the right communication when dealing with issues faced by customers. Service innovation means delivering personalized experiences, for example, offering bespoke packages that are tailored to their preferences and needs, and providing offers and recommendations alongside access to exclusive events and experiences. Service channels can also be expanded to include multiple touchpoints, offering 24/7 support with physical and digital touchpoints spanning across social media and other channels.

Innovation Challenges

While innovation in the customer experience space can have multiple benefits, it comes with potential issues and challenges that need to be addressed. It’s worth noting these down before we tackle how best to address these problems:

1. Lack of resources or costly: It goes without saying it’s expensive to innovate, ideate and incorporate changes within an organization. With the bottom line of adding value to customers, the top line should not suffer the costs that cannot be realized. Dedicated teams, resources and technology come at a cost that can be rewarding if done right.
2. Security, privacy and compliance: Most important to consider in this digital age when using technologies and data analytics is handling how data is captured and stored. Consensus is an important topic in many countries and transparency is key in the use of customer data. Hence security and privacy policies need to stay in compliance with protecting customer data that needs to be customized geographically as well. IT and security systems need to be then robust as any small glitches can hurt the brand, as we’ve seen far too often.
   
3. Integration: Considerable efforts are required to understand the business architecture and integrate it with existing systems remains important for seamless data transition between multiple databases, including CRM, ERP and other business sources. This allows for a unified view of the data but will involve bringing together multiple teams to understand what and why data resides where it does.
4. Siloed departments: Large organizations face difficulties in coordinating efforts for innovation across multiple departments that especially work in silos. Lack of unification and sharing data and information create hindrances to innovating in the right direction and avoiding redundancies.
5. Measuring quality and ROI: If innovation efforts do not have a way to realize their value, how quality is enhanced and maintained and the overall value it creates, then the efforts of innovating are in vain.  Continuous testing and validating these efforts to ensure they are not causing additional hindrances to both the inner and outer view are important.
   
6. Success measurements: Effective means to measure customer experience in the process of innovation are important and companies tend to fail within these aspects. Continuous data collection, analysis and inferences come at additional time and cost to resources.
7. Resistance to change: The biggest parameter to why companies fail to innovate is the resistance to change from employees and the culture to incorporate risk. Usually, a lack of understanding of the real benefits of innovating causes resistance to acting upon these initiatives. Lack of effective communication and building a culture that fosters innovation are key reasons for these failures.
8. Staying relevant: Keeping up with the pace of industry change and uncertainty that is rapidly evolving, understanding customers changing demands and expectations timely, and adopting research, strategy and technology are downfalls to innovating.
9. Lack of management buy-in: Incorporating all the downfalls from the points listed so as to help create a solid business case takes dedicated teams and efforts in getting management buy-ins. This lack of coordination will create issues not just while implementing changes but also while realizing them.
   

Importance of employee engagement

Innovation begins from the inner view, with employees playing the most critical roles in understanding and helping translate customer needs to organizations that can, in turn, empower innovation. How can organizations then empower employees to help elevate the innovation process?

1. Innovation Culture: Fostering a culture of innovation should be the starting point towards encouraging employees to put forth their recommendations, provide ideas, enhance creativity, encourage experimentation and provide clear vision and mission statements to establish support.
2. Recognition: Organizations should not only recognize but reward employee contributions towards the innovation space. This helps encourage bringing new ideas or enhancements to the table, helps enable risk-taking and, with the right support, helps carve out a path to implement valuable proposals. For example, many large organizations focus on patenting and trademarking ideas that are proposed by employees and offer platforms that identify and push ideas through multiple stages. The support extends to providing time and possibly an alternative career roadmap so that employees are encouraged to pursue their ideas without the pressure of doing everything else. 
   
3. Encourage ideation: Clear communication encouraging employees to ideate should be made a part of the primary agenda so that improvements to process, product and service can be put forward. Hackathons, brainstorming sessions and providing suggestions and feedback mechanisms are some ways to encourage ideation.
4. Training and development: Organizations should invest time in training their employees to understand the importance of innovation and provide programs to include the process for innovation. New skills and knowledge sharing can be extremely helpful in elevating how employees think about problem-solving and innovation.
5. Cross-collaboration: Large organizations can face issues with working in silos, and it’s important to bring access to multiple functionalities that exist across different teams so that employees are encouraged to collaborate across the board with the right subject matter expertise and possibly across tiers. These efforts would increase the value of the ideas brought forward as they get better perspectives when they work across teams.
   
6. Encourage and provide constructive feedback: There needs to be considerable encouragement in educating the benefits of innovating. Not all ideas will be feasible, but feedback should be provided constructively so as to continue encouraging innovation.
7. Resources and experimentation: The ideation process can go through multiple stages, including research and experimentation, to assess the viability of implementation. Organizations should consider providing the right tools for innovation, resources and guidance, which include allocating budgets toward innovation.
   
8. Implement agile methodologies: A continuation of experimentation involves rapid prototyping, A/B testing and creating proof of concepts (POC) to get quick management buy-ins.
9. Crowdsource ideas using technology: Platforms like IdeaScale enable organizations to gather both the inner and outer views for enhancing their abilities through the generation of ideas not just from employees but also through customers.
10. Measure success: Many a time, I have found this element to contradict the intentions to grow innovation, but that is only because organizations have not thought through all the different ways to evaluate ideas. In the CX space, it’s easier to go back and check how customers feel about new additions by gauging their feedback and accessing the impact of the idea. However, KPI’s need to be put in place and re-evaluated where required to cater to the diversified ideas that can be brought to the table.

Design thinking

Last but definitely not least, design thinking should be considered an important tool to access innovation in the customer experience space. The principles that govern design thinking should be defined so that the organization uses a ‘Customer-first’ model allowing organizations to remain Customer-centric. By maintaining the target audience in mind, even internal processes can be enhanced so as to enhance the outer view of the customers.

Conclusion

In conclusion, innovation requires planning, needs a clear strategy, ownership and also the right communication so as to encourage stakeholders to ideate, keeping the right objectives in mind. There are bound to be challenges along the way, but if designed well, organizations will benefit by gaining memorable experiences and hence customer loyalty.

The post <h1>Experience Innovation: Give a Memorable Experience to your Customers</h1> <h3>Gain loyalty at multiple stages of customer journey</h3> appeared first on The Experience Journal by QuestionPro.