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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