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Tapping into smarter analytics: how to combine AI-powered behavioral analytics and VoC to deliver better UX
Sure, AI has become amazing at crunching numbers, analyzing data and forecasting trends using analytics—and its future looks even brighter. However, it still can’t get the full picture of user sentiment and context without humans. That’s where the voice of the customer (VoC) comes into play, adding insights AI can’t replicate. Together, they provide you with better user insights, leading to smarter decisions and more impactful outcomes.
Follow along as we examine the advancements AI brings to behavioral analytics, discuss the opportunities it creates for analysts and marketers, and highlight why blending AI with VoC is key to maximizing your business analytics’ effectiveness.
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Transforming user insights: the latest in AI-driven behavioral analytics
The bond between technology, businesses and their customers fuels progress. As behavioral analytics and AI tech advance, businesses create smarter operations, and customers enjoy better experiences.
The past two years have seen remarkable progress in analytics, automation and AI, helping companies tap into artificial intelligence for quicker and smarter insights about their users.
- Machine learning (ML): Algorithms and machine learning models are able to handle complex data sets more efficiently than ever. This enables AI to automatically analyze user interactions and behaviors across digital platforms. For example, Contentsquare’s AI can identify patterns in user journeys, predict features or content types most likely to lead to conversions and detect anomalies that may affect the user experience (UX)—all without any time-consuming manual intervention.
- Big data handling: AI capabilities have evolved to handle and analyze large datasets efficiently. Experience Intelligence platforms (like Contentsquare 👋) support integration with data repositories like Snowflake and Redshift, giving your business intelligence teams what they need to create a lasting competitive advantage for your company.
- AI assistants: AI now acts as an always-on analyst, using generative AI to understand and respond to user queries in plain language. For instance, Contentsquare allows marketing teams to ask questions like “What are the key factors influencing customer satisfaction?”, and then generates real-time insights and actionable recommendations, guiding marketers in adjusting strategies to improve customer satisfaction and retention.
How AI algorithms help you improve digital experiences
AI algorithms have a range of powerful applications; like in customer service, when they monitor social media chatter to focus on interpreting customer sentiment and suggest improvements, or in retail, where they analyze customer purchase histories, helping teams personalize marketing campaigns.
For digital experiences, an analytics AI steps in to save marketing, ecommerce, product and analytics teams from sifting through endless data and performing repetitive tasks: cleaning it up, identifying patterns and trends, spotting anomalies and forecasting trends based on historical customer data. This in-depth behavioral analytics helps teams understand user intent, motivations and pain points.
With the heavy lifting out of the way, they can focus on what truly matters—making sense of behavioral data and finding actionable insights that drive business growth and efficiency.
Here’s how you can put AI analytics to work for your business and what that looks like in practice:
- Accessible analysis: Gain instant insights into user behavior, as it happens. For example, Contentsquare allows users to run real-time analysis by simply asking. Just ask AI CoPilot questions like “How many mobile users viewed the blog this week?” and get instant insights without manually sifting through data.
- UX optimization: Find and fix usability issues to enhance your user experience. Analytics AI can pinpoint areas where users struggle, like confusing navigation or slow-loading pages, and suggest improvements to streamline the user journey. Use Frustration Scoring—calculated from rage clicks, errors, repeated form field interactions and other indicators—to find the biggest problem areas and address them individually.
- Personalization: Tailor experiences to each user’s preferences. AI analyzes individual user behaviors at scale, helping you deliver custom recommendations and content. For example, analytics AI can analyze the browsing habits of your ecommerce website’s users and recommend personalized product suggestions, helping you increase engagement and satisfaction.
- Predictive modeling: Forecast future trends based on historical data sources. Analytics AI can predict which users are likely to churn or convert, letting you proactively address potential issues or capitalize on opportunities. Contentsquare analyzes user engagement metrics to predict which website features or content types are most likely to lead to conversions, helping you optimize digital experiences without manual intervention.
- Smart AI alerts and recommendations: AI keeps a close look at your analytics, letting you know as soon as key business metrics go up or down unexpectedly—like a sudden drop in conversion rates or a spike in app uninstalls. Setting up automated Contentsquare alerts on various error pages helped Wolverine Worldwide improve performance and reduce a page’s exit rate by 32%.
Pro tip: Give your team the gift of easily accessible analytics with Contentsquare
Contentsquare’s AI CoPilot leverages generative AI, using the latest artificial intelligence technology to provide the fastest path to actionable insights.
Just ask questions about user behavior and AI CoPilot will answer with explanations and charts, automatically figuring out specifics like events, properties, filters, and groupings for you.
You can also ask follow-up questions, such as “How does this vary by marketing channel?” or “What should I look into next?” and get a personalized response that allows you to move fast and effectively.
AI CoPilot makes it easy for anyone to get started with analytics, no matter their experience.
Why AI algorithms have their limits
When it comes to analyzing user behavior, AI algorithms are great at showing you the where and how—but what about the why?
While AI can show you where users drop off in their journey, it can’t explain why they found that particular step frustrating or confusing. That’s because AI can’t understand the context behind human behavior.
And relying on AI algorithms alone to analyze user behavior severely limits your understanding of it. An AI tool might not recognize if a specific design element is culturally insensitive or if a certain wording is off-putting to a particular audience. You need real people for that!
Real people are essential for providing the nuanced and emotional context that AI can’t replicate. The best way to collect these insights?
Voice of the customer or VoC feedback.
By collecting and evaluating customer feedback at scale—and at speed—VoC adds a human touch to your analysis, providing insights that AI alone can’t capture. Together, they help you connect the dots, understand the why behind the numbers and have a full data set to understand your customers.
VoC’s qualitative insights add context and depth to the numbers, revealing the reasons behind the behaviors and the emotions driving customer decisions.
How to use AI + VoC together to understand and improve digital experiences
To create experiences that drive conversions, you need to bridge the gap between user feedback and user behavior. The easiest way to do that is with Contentsquare VoC—where precision data meets real human insights.
Using intuitive AI-powered survey tools (that come with over 40 ready-to-use templates), create VoC surveys in seconds and collect responses in minutes.
Then, you can use our one-click AI Summary Reports feature to get an automatic overview of key issues and opportunities users raised in responses. And by automatically tagging responses and tracking user sentiment, you can see how often issues occur—and what their impact is.
Take a closer look at what you can achieve by combining VoC’s qualitative insights with AI’s analytical prowess:
- Validate and contextualize insights: Use VoC feedback to confirm and add context to AI-generated insights. For instance, while AI may flag a drop in your website’s conversion rates, a feedback button lets you ask users to explain whether it’s due to a specific usability issue, pricing concern or competitor comparison. With this knowledge, you can prioritize improvements and validate hypotheses derived from AI analytics.
- Get total visibility into the buying journey: Move beyond surface-level metrics and dive deeper into the behaviors, preferences and emotions that drive customer decisions. For eCommerce websites, AI algorithms can spot high drop-off rates at checkout, while an exit-intent survey gathers feedback on why visitors aren’t completing their purchases. Combining these insights shows issues with the checkout process and concerns about hidden fees. Fixing these problems leads to higher conversion rates.
- Find human-centric solutions to customer pain points: Detect user pain points through AI and understand the underlying reasons via VoC. If users frequently stop using your app after the initial setup phase, use onboarding surveys to ask about their experience and any difficulties they’ve encountered. With combined insights from AI and VoC, you can adapt the onboarding process and provide additional guidance to improve user retention.
- Enhance product features based on feedback: Make product launches a success with in-context feedback. Set up a CSAT or feature prioritization survey from a template to collect customer feedback and find out what features they actually want. Then, analyze responses with AI-powered summary reports and quantify feature requests by auto-tagging responses. Then, all that’s left is for you to share these customer responses with stakeholders and get buy-in for new and improved features.
Pro tip: Watch session replays to get more context into feedback and see the exact user experience.
VoC seamlessly integrates with Contentsquare’s Digital Experience Analytics (DXA) capabilities like Session Replay.
Replays show you how users engage with your site, where they click (and don’t) and how their behavior impacts your metrics. VoC and AI-generated insights help you uncover the ‘why’ behind this user behavior by collecting and analyzing direct feedback from your customers.
Session Replay lets you reconstruct individual visitor sessions to reveal hidden user behaviors in easy-to-read dashboards.
Next steps in behavioral analytics AI
Looking ahead, behavioral analytics AI is set fto redefine the digital experience industry. Here’s a glimpse into what to expect in the near future:
- Practical AI in action: We’re going to see more ways of using AI to make workflows seamless and, more importantly, give teams the speed they need for a continuous optimization strategy. Whether it’s using more generative AI (like in the Contentsquare product experience) for personalized in-context help or features like sentiment analysis for surveys, AI will be there to support digital teams in getting intelligent insights with automated actions.
- Hyper-personalization becomes standard: AI-powered behavioral analytics will continue to refine personalization efforts, helping digital teams provide rich and engaging customer experiences that are also intuitive. From personalized online shopping suggestions to responsive customer support, AI will help you enhance satisfaction and drive growth by meeting user needs effectively.
- More accurate predictive user insights: AI algorithms will evolve to predict user behaviors and preferences with greater accuracy. With access to predictive analytics on user behavior, such as drop-off points or hesitation patterns, teams will be able to optimize user journeys across every digital touchpoint, all without any of the hassle of decoding data.
- Your AI, powered by Contentsquare: Those looking for ways to innovate and come up with their own cutting-edge AI data analytics solutions will be able to use Contentsquare raw data to build and train their own AI models. By training your own machine learning algorithms around a comprehensive view of customer behavior, preferences, expectations and trends, you’ll be able to explore, stay ahead of the curve and unlock the potential of tomorrow’s customer journey.
Contentsquare’s Chief Produact Officer, Lucie Buisson and Chief Strategy Officer, Mohannad Ali give a sneak peek into the future of customer experience at CX Circle London 2024
“We want to make our unique data set available for you to try things out and experiment. So, you’ll be able to connect the data that we collect on your behalf to your own data warehouses, your own machine learning models and train them on our data. All so you can innovate, explore and come up with your own cutting-edge solutions.”
Mohannad Ali – Chief Strategy Officer at Contentsquare
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FAQs about analytics AI
Using AI helps analytics teams streamline their processes and improve the accuracy of their predictions. With the ability to handle complex data, AI extracts more valuable insights from vast datasets, which translate to quicker, more informed and better decision-making.
Yes, AI tools like those offered by Contentsquare can integrate analytics data from websites, mobile apps and other sources to create a holistic view of how customers interact with your business. This helps you identify key touchpoints in the customer journey, optimize user experiences and ultimately improve customer satisfaction and loyalty.
Yes! Our Contentsquare’s Voice of Customer product, powered by Hotjar, offers a variety of intelligent features that leverage AI to optimize the collection and analysis of customer feedback, such as:
- AI Survey Generator: Quickly create surveys by typing what you want to achieve in natural language.
- Summary Reports: Get a summary of major findings, actionable recommendations and matching customer quotes.
- Sentiment Analysis: Automatically categorize customer responses as positive, neutral or negative.
- Automated Tags: Automatically assign tags to survey responses to quantify issues faster.
