The average user was always the North Star for any modern digital strategy. Mean session duration, average order value, standard conversion rates – these are all aggregates that helped define success.
But in 2026, we must learn to see the differences in users. Behavior is high-variance, and in the end, statistics that describe everyone end up describing no one. This year, data scientists are increasingly looking for ways to move away from flat, linear reporting and towards high-dimensional behavioral models.
The fallacy of the mean
The main problem when relying on averages in digital spaces is that user behavior doesn’t follow a normal distribution. What happens is that an average session duration of four minutes ends up being one group of users who bounce in ten seconds and another group that spends twenty minutes deeply engaged in a product trial. Four minutes is meaningless and could lead to the wrong conclusion (e.g., they get partway through the funnel and lose interest).
When optimizing for the mean, we actually alienate both groups. It could mean dumbing down the content or features, thinking it’s too technical and that’s why they lose interest. At best, this serves the uninterested group at the expense of the interested group. At worst, it misses the point for both, and the issue was originally simply a lack of tutorial of the features.
Optimizing for the mean means creating a dead zone of utility. Engineering resources are spent on features that satisfy a mathematical midpoint. To find real alpha in product optimization, we must look at the variance. To do this, we need event-based data that captures the micro-hesitations and non-linear paths.
High-dimensional signals
Organizations are increasingly adopting sophisticated frameworks for product analytics to capture the aforementioned nuance. Instead of seeing a user’s path as a simple funnel, high-dimensional modeling treats it as a series of behavioral signals. This sounds more complex than it is. It simply means detecting scrolling velocity, cursor patterns, rage clicks, and essentially understanding the “why” behind actions. In some cases, we can even let Machine Learning pattern recognition on these data points to find things we can’t.
When looking at only converted/not converted labels, we end up ignoring 98% of the user experience. Behavioral dimensions can cluster users based on intent rather than demographic or other attributes. Identifying a hesitant researcher versus a determined buyer allows for real-time personalization, such as retargeting ads that cater to this motivation.
From reactive reporting to predictive intent modeling
Tools like Contentsquare are where the data stack has really changed by allowing teams to visualize the (otherwise invisible) friction that exists between data points. When you can see that a user is hovering over a CTA but failing to click, you’re looking for psychological barriers as much as technical ones.
When modeling this, we can begin to predict intent by analyzing sequences of events to calculate an intent score. This helps understand strategic friction, which is where intentionally slowing down a user during high-consequence decision points can improve data accuracy while removing obstacles for low-stakes tasks. When you know the behavioral precursors to churn, you can begin to trigger interventions, like a simplified UI or a targeted technical prompt.
Empathy in data science and scaling nuance
Moving toward these behavioral models requires a change in mindset of our own. We often view data as a cold representation of system performance, but in a product context, data is now a proxy for human behavior where over-quantification is undone and human understanding is brought back in.
Much like the misinterpretation that “God is dead” was a Nietchze warning rather than a radical new opportunity, the death of the average user is similarly misunderstood. It’s a chance for data scientists to acquire empathy as a skillset and lead with granular behavior insights.
The real challenge for the next generation is going to be that of scale. It is easy to understand one user’s journey through a manual session replay, but how would Netflix go about this? The sheer volume of unstructured behavioral data can overwhelm traditional relational databases and standard analytical workflows. So, new platforms and tools are slowly being released, and such SaaS will be a big part of scaling from the outset, as well as the introduction of conversational statistics (non-technical staff asking a chatbot to perform unique insights with a large pool of data). Reporting and dashboards, however, remain important, but more fragmented and comprehensive.
Like with all scientific research, it’s good to use a hybrid approach of qualitative insight and quantitative rigor. While each journey is unique, individual frustrations may well be shared. While there may be many more customer fragments that we previously acknowledged, it’s still possible to categorize (perhaps not by demographic, but by microbehavior). Opportunities to scale are there so long as we ditch the idea of “the average user”.