Causal machine learning for personalized discount targeting with technographic trace data

I built a profit-oriented targeting framework for online discounts that learns optimizes over treatment effect heterogneity directly from A/B-test data. My framework optimizes both the hyperparameter tuning and targeting algorithm training for economically relevant objective functions, as opposed to off-the-shelf statistical objectives. Using data from 148k website sessions from two campaigns, I show how the method boosts expected profits by 3-6 % versus best-practice baselines, entirely with technographic trace data (device, screen, browser, etc.).

Shows why profit-oriented criteria must balance baseline purchase rates and treatment effects—classic uplift rules leave money on the table. Project demonstrates the importance of optimizing both tuning and training algorithms for appropriate economic objectives.

Optimal policy outperforms both uniform and uplift baselines across two firms. Provides clear quantification of the value of modern causal ML in real-world discount targeting.

While geographic and behavioral variables do contribute to the accuracy of the targeting algorithms, basic device-level information (screen size, operating system, browser) drive most of the incremental value in profit lift.