Sit through any product demo today and you’ll hear “AI” mentioned at least ten times. And while some instances might just be buzzwording, there’s ultimately good reason for the excitement. From intelligent chatbots capable of providing instant customer support to tailored product recommendations, AI’s ability to transform online experiences is undeniable. That transformation is especially powerful when AI models are applied to behavioral data. Those paying close attention in those product demos may have started to notice two distinct approaches to AI, shaping how businesses engage with their audiences::
Reactive AI focuses on analyzing past behavior to surface insights about what’s already happened.
Proactive AI looks forward, using historical data to predict what’s coming next.
Reactive AI
Reactive AI focuses on making complex data more digestible. It enhances usability by transforming an overwhelming pile of user event data into a metric of your choosing. You’ve likely heard this referred to as “causal AI”.
Example:
A website owner asks an AI chatbot, “How many conversions did I have yesterday, and what friction points did users encounter?”
The AI responds by synthesizing this data into actionable insights: it generates a conversion metric and summarizes the user experience, highlighting key friction points along the conversion journey.
This is obviously a powerful workflow that allows you to understand what's going on in your user experience. The drawback here is that the analytics you’re getting are things that have already happened. If the metric generated from the query surfaces a problem, you’re now scrambling to get a fix pushed out as quickly as possible to stop the bleeding.
Proactive AI
Proactive AI, on the other hand, aims to anticipate user behavior based on behavioral data patterns, all without a user explicitly stating their intention or asking for help (through a website chatbot, for example). It transforms what were once just data points—traditionally visualized as dashboard trends—into predictive insights that empower real-time decision-making.
Example:
A website owner sends all their behavioral data into a data warehouse and utilizes a behavioral data model that identifies patterns linked to conversions. The model receives a real-time data feed from a user's session and makes a prediction of the outcome of the user’s session. Based on these predictions, a signal can be sent to a CMS so that they can personalize the user’s experience dynamically. For instance, offering the user a discount code at a critical moment in their checkout flow or recommending products to increase their likelihood to purchase based on behaviors exhibited by similar users.
As with Reactive AI, historical data is also important in this context, but instead of reacting to the data in a more delayed manner, you’ve got the power to influence the success of your users’ visits in real time.
The best of both worlds
There are lots of behavioral data providers out there today that have put their stake in the ground by focusing solely on reactive uses of AI. At Fullstory, we recognize that it’s not a matter of one vs. the other; it’s both. In fact, our customers use both to serve a large range of use cases. Reactive AI works well for analyzing trends and finding strategic opportunities to adjust the user experience, like redesigning how customers move from their cart to a completed purchase. By using these two approaches, platforms in the behavioral data space can either react to the past or use it to anticipate the future, enabling businesses to enhance user experiences and drive meaningful outcomes.
Discover the possibilities of both approaches with Fullstory today.
