It’s often said that data is the new oil, but instead of selling it, many companies can lead business decisions with their own proprietary findings. Once the appropriate infrastructure is set up to maximize collection, it’s all about understanding the data. And, to unlock better digital insights, we can fall back on the powerful pattern-matching prowess of AI.
Key Takeaways
- Data drives business decisions, and AI enhances understanding of user behavior.
- Contentsquare analyzes user interactions and measures frustration to improve experience.
- Mixpanel tracks user actions over time, predicting future behaviors and outcomes.
- Gong captures customer conversations, providing qualitative insights to complement analytics data.
- Combining these tools creates a comprehensive feedback loop, vital for effective analytics strategy.
Table of contents
Understanding the “Why” Behind User Behavior
A good tool to begin with is Contentsquare, as it’s a leader in the digital analytics space and is designed to help businesses understand on-site user behavior. Behavior is nuanced, and it’s not always immediately obvious as to why certain things are being clicked, engaged with, and so on. Contentsquare has the ability to go beyond clicks and pageviews to find the quality of digital interactions, as this is important in knowing how much weight to put on them. The platform uses AI to analyze user sessions and identify signs of frustration that traditional analytics would otherwise miss.
To get more specific for a moment about digital insights, one such feature is the Frustration Scoring. This is an AI metric that detects user struggles by analyzing rage clicks, repeated hovers, slow-loading page elements, and so on. Instead of guessing where the user experience is breaking down, teams get a prioritized list of issues backed by data.
To add to that, there is another feature called AI-Powered Session Replay Summaries. Manually watching hours of session replays is thankfully a thing of the past. AI watches them for you and delivers concise summaries that highlight the most critical moments and actionable insights. Ultimately, it helps allow teams to quickly understand user pain points and optimize the digital experience with way more precision than before. So to repeat, it’s about understanding the “why”.
Predicting Future Actions
While Contentsquare is sufficient at analyzing the on-page experience, Mixpanel takes things up a gear by tracking and predicting user actions over time. As an event-based analytics platform, Mixpanel helps understand the sequences of actions that users often take (or, in fact, don’t take) within the product. Where AI comes into it, is the insights are geared towards forecasting, allowing you to move from reactive analysis to a more proactive strategy.
Among the many AI tools, Mixpanel’s AI does a good job of looking at user event data like button clicks, feature adoption, and purchase completions, all to predict key outcomes like customer conversion and retention. For example, it can identify which user behaviors are the strongest indicators of long-term loyalty or which are early warning signs of churn.
This can create a synergy with Contentsquare. Imagine Contentsquare identifies high frustration on your pricing page. Mixpanel can then be used to track the long-term impact of that frustration, measuring whether users who experienced it were less likely to convert or more likely to churn in the following months.
Digital Insights from Customer Conversations
Digital analytics can show you what users do, but it can’t always tell you what they’re thinking – they’re not mind-readers (… yet). But that’s where Gong comes in, which is our closest thing to that.
As a conversation intelligence platform, Gong uses AI to record and transcribe customer-facing conversations from sales calls and support tickets. It captures the “voice of the customer” in its most direct form.
Of course, it’s not just there to listen, but to analyze. Gong’s AI can identify recurring themes and customer sentiment, like mentions of specific competitors or product features, all without anyone having to listen to hours of recordings or report back. It’s a passive way to mine an awful lot of customer service data, filled with sentiment and queries. This is an invaluable qualitative contextualizer compared to the previous quantitative data from other tools – just make sure you’re adhering to these (voluntary) AI NIST guidelines.
If Contentsquare is flagging a high frustration score during the checkout process, Gong’s AI might be able to pick up on negative sentiments around the, say, shipping form. This direct feedback validates the quantitative data and removes any ambiguity.
Building An Analytics Strategy
There are more and more AI tools popping up left, right and center, especially within digital insights. But the key is to not get overrun by them and to keep a fairly tight unit of services that are far-reaching. Each of the three platforms in this guide are complementary to one another, as opposed to overlapping.
- Contentsquare reveals the why behind on-site user behavior.
- Mixpanel predicts what’s next based on user actions over time.
- Gong understands the voice of the customer for invaluable qualitative context.
By stacking these insights together, you can create an extremely comprehensive feedback loop – something that has never been possible before AI, especially when considering the sheer scale. What customers say in Gong conversations can be validated by their on-site behavior in Contentsquare and then tracked over time in Mixpanel. Plus, using big-name platforms can help stay the right side of AI fair use.
AI analytics is no longer a futuristic concept but very much a present-day opportunity for digital insights. And, given that competitors are tuning into them more and more, they’re going to soon become a necessity, otherwise the customer experience of your product will fall behind.
