Predict User Behavior: Who are They and What Will They Do?

With third party cookies becoming increasingly ineffective due to the enforcement of data privacy laws and their phaseout by major browsers, first party data has risen to the forefront as a powerful and ethical means of predicting user behavior. However, many businesses still remain in the dark as to how to achieve this. This article breaks down the distinction between predicting who a user is and what they will do, along with the different types of data involved.

The “Who?” and “What?” of Predicting User Behavior

Predicting user behavior with first party data is a two-part process. Essentially, it is not enough to know who your customer is, but knowing what they are likely to do is impossible without this information. For this reason, you must first determine who they are and then leverage this information to discover what actions they are likely to take next, allowing you to respond accordingly. 

Determining “Who” a User Is

Every user records thousands of non-PII data points when visiting a page. These include the browser and device they use, their location, what the weather is like, which browser extensions they have installed, if they use incognito mode, the number of tabs they have open, and countless others. Further details, such as how long they remain on certain pages, which pages they visit and what they add to their cart, continue to shed light on the demographic and psychographic to which the user belongs, even as they browse. 

This forms an incredibly detailed and unique picture of the user, telling you “who” they are. Predictive analytics solutions can record all of this information in real time, and compare it to data registered by millions of users from other sites, sorting the new user into minute cohorts filled with people almost exactly like them, such as ‘cautious buyer’, or ‘bargain hunter’ (in reality, these cohort categories are much more nuanced, and generally defy simple explanation). 

Determining What a User Will Do

Once a user is separated into a cohort, their future actions can be predicted with surprising accuracy. In this process, the AI model performs a real time analysis of the past behavior of all other members of the cohort, examining for instance, when individuals abandoned carts, made purchases or subscribed to services, and what they bought or engaged with. 

The model then uses this data to extrapolate from the position of the user onsite, predicting with regard to the products and services on offer and any other interactions they are likely to make. It then analyzes how other members of the cohort reacted to promotions, personalized features, and churn reduction strategies.

Using this information, it can generate promotions, personalized content, or other features optimized to keep the user engaged or convince them to make a purchase. 

Why Predict User Behavior

Predicting user behavior has become essential for online businesses, allowing for a vast range of previous time and labor-intensive analytics tasks to be performed instantaneously and accurately. Let’s take a look at some of these below: 

Personalization: Understanding the interests and tendencies of each user allows platforms to tailor content, recommendations, and messaging for maximum resonance. This is fast becoming common practice. Research shows that 82% of users feel more positively about a brand when delivered personalized content, while 98% of marketers believe personalization advances customer relationships. Using the predictive methods described above, the reactions of a user’s cohort towards past content are analyzed to generate and serve customized recommendations. 

Consumer Insight: Individual behavior analysis provides better understanding of the preferences that shape product needs and marketing approaches. This data is essential for supporting development of new products and services. Collating first-party data directly from your customer base offers insights into their motivations, pain points, and desires that pertain specifically to your company’s needs, especially when compared to the greater degrees of abstraction inherent in analyses derived from third party data. 

Pre-emptive Support: Identifying users likely to churn or encounter issues allows proactive customer support and retention programs. For instance, an e-commerce company can leverage anonymous first-party data like shopping cart abandonment rates, and product return patterns to identify customers at risk of defecting. The company can then preemptively reach out with personalized incentives, streamlined checkout experiences, chatbot help to re-engage wavering customers before they take their business elsewhere. 

Fraud Analysis: Detecting outlier individuals with suspicious behavior allows for early interventions to limit risks and fraud. Just as predictive analytics separates customers into cohorts according to the likelihood or unlikelihood that they will buy, these tools also analyze first party data to create cohorts full of those most likely to attempt cybercrime, based on the past behavior of millions of users. Permissions can also be granted to AI to block an account or limit spending based on these signals. 

Growth Analysis: Granular data at user level helps identify the best audience segments to target for growth. Online companies can leverage first-party data to pinpoint their most valuable customer segments. Analyzing proprietary user profiles reveals high-potential clusters based on factors like average order value and brand affinity. Targeting growth campaigns at these lucrative micro-segments maximizes marketing ROI and acquisition efficiency.

Prediction is the Future

While you need to know ‘who’ your customer is in order to predict ‘what’ they will do, it is the ‘why’ that’s the most interesting part. Some companies say it’s to provide personalized content, some will tell you it’s to detect fraud, or forecast inventory. However, imagination is the limit in terms of how to wield this revolutionary technology. All that is certain is that predictive analytics are permeating every industry at an accelerating pace. The cohort to watch are those that embrace it first.