Don't just predict the future; engineer it. The Drivetrain framework ensures your Data Products are built to optimize actionable business levers rather than just delivering passive insights.
Many data initiatives fail because they start with data and search for a problem. The Drivetrain Approach reverses this, starting strictly with the objective.
What exact business outcome are we trying to achieve? It must be measurable.
What inputs can the business directly control to affect the objective?
What information connects the levers to our desired objective?
How do the levers and data statistically interact to impact the objective?
The final step of the Drivetrain is taking the predictive models and running an optimization algorithm to prescribe the exact lever settings that maximize the objective. The data product tells you exactly what to do.
Imagine building a model that predicts which customers are 90% likely to churn. It's highly accurate. It's mathematically sound. But it's fundamentally useless on its own.
Why? Because if a customer is 90% likely to leave, offering them a 10% discount might not change their mind—it just wastes margin. A Drivetrain approach doesn't ask "Who will churn?" It asks, "Which lever (discount, phone call, feature unlock) will maximize the probability of retention for this specific user?"
Data Products must be designed backwards from the business outcome, not forwards from the available data.
"We have a ton of customer data. Let's build a neural network to predict their behavior and put it in a dashboard for the marketing team to figure out how to use."
"Our objective is to maximize customer lifetime value. Our lever is the monthly promo email. We will collect response data, model the uplift of different promos, and build an engine that automatically sends the mathematically optimal promo to each user."
Ready to stop building passive dashboards and start building intelligent engines that drive your business forward?