Your Data Science Problem Is a Product Design Problem
Pop open Netflix. You are presented with a screen where a top movie is shown in a hero placement, followed by rows of recommendations. The laziest recommendation is the chart: here is what is popular. This is a self-reinforcing loop. These titles are popular because they are shown prominently. They are shown prominently because they are popular.
If you click on a title from the chart, what does that tell Netflix about you? A large number of people are being served this same impression. Your click could mean you saw the ads elsewhere and are following up. Or the thumbnail is compelling. Netflix can take a guess.
But what if the subscription charge reloaded a wallet each month? The wallet is used to “purchase” access to content. You could design various micro-transaction structures, for example get charged by viewing duration. You could allow a user to accumulate coins and lead to inter-temporal tradeoffs.
What is the point of adding this friction? You are forcing users to make a meaningful choice. Right now, clicking on something is costless. The user is not actively deciding what they want. With scarcity, even artificially created, you force tradeoffs. And tradeoffs are signal.
Now the data science team has something qualitatively different to work with. They can now see how someone trades off one title against another and can measure the intensity of preference, not just the direction. A person who spends 80% of their monthly coins on a single title is telling you something fundamentally different from a person who spreads them across ten titles.
This is not a recommendation for Netflix to adopt this model. Their business is built on growth and frictionless access, and adding transaction costs would conflict with that strategy. The illustration is meant to show what becomes possible when a product is designed to generate richer signals. The protocol creates investment: users think about tomorrow, about accumulation, about the next subscription round. There is a dynamic connection between present choices and future options. And that dynamic connection is itself informative.
You could extend this further. Let users commit coins to vote on what content they want next. Let them signal demand for types of content that do not yet exist on the platform. Now you have a forward-looking demand signal, not just a backward-looking consumption log.
Most data science teams are handed a fixed set of behavioral signals and asked to build models on top of them. The signals were not designed for analysis. They were designed for the product’s primary function. The analytical value is incidental. The real task is to re-design interactions where meaningful choice is embedded in the product, so that using it naturally reveals the deeper preferences of the user base.
The “what” approach works for monopoly platforms. When you control the entire market, anything goes. You can run gigantic deep learning models on billions of behavioral events and the sheer volume allows you to catch every possible behavior. You do not need to know why someone clicked. You just need enough clicks to find patterns.
Most platforms are not monopolies. If your platform actually needs to understand what your customers care about, the product should provide the core service but also create interactions that allow users to provide meaningful signals.
Part of the problem is inherent to certain business models. If a platform needs to sell ads, then less user agency is better. The platform’s incentive is to maximize time spent, not to understand why time is spent. The data science team inherits this impoverished signal environment and is asked to build personalization on top of it. But the data that was never designed to carry the information they need.
If this is the challenge you have been running into, the answer is not a better model. The answer is redesigning the interactions your product generates.
