Tapping into smarter analytics: how to combine AI-powered behavioral analytics and VoC to deliver better UX

Introduction

If you work in analytics, your most valuable resource isn’t spreadsheets, SQL databases or coffee—it’s time. That’s probably why so many analysts are excited about the prospect of only putting in 30 hours a week while artificial intelligence (AI) does the work for them. But is analytics AI really that good?

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.

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.

Key Areas of Advancement

• 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; for example, in customer service, when they monitor social media chatter to focus on interpreting customer sentiment and suggest improvements. In retail, they analyze customer purchase histories, helping teams personalize marketing campaigns.

For digital experiences, 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.

Here’s how you can put AI analytics to work for your business:

• 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 Chat with Sense 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 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 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, allowing you to 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.

• 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.

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, as it cannot understand the context behind human behavior.

Relying on AI algorithms alone to analyze user behavior 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!

Voice of 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.

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, create VoC surveys in seconds and collect responses in minutes.

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.

• Get total visibility into the buying journey: Move beyond superficial metrics and dive deeper into the behaviors, preferences, and emotions that drive customer decisions.

• Find human-centric solutions to customer pain points: Detect user pain points through AI and understand the underlying reasons via VoC.

• 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.

Next steps in behavioral analytics AI

Looking ahead, behavioral analytics AI is set to redefine the digital experience industry. Here’s a glimpse into what to expect in the near future:

• Practical AI in action: More ways of using AI to make workflows seamless, providing teams the speed they need for a continuous optimization strategy.
• Hyper-personalization becomes standard: AI-powered behavioral analytics will continue to refine personalization efforts.
• More accurate predictive user insights: AI algorithms will evolve to predict user behaviors and preferences with greater accuracy.

Conclusion

Contentsquare's unique data set will be available for you to experiment with and build your own cutting-edge AI data analytics solutions.