CO
AI & ML2025
Campaign Optimizer
Predict who responds to a campaign, then spend where the next dollar earns most.
Built for: Independent build · public dataset
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Runs in your browser with an illustrative logistic model whose weights mirror the direction and rough size of the trained model's drivers. It is a teaching model, not the exported artifact. Move the sliders, then allocate a budget across segments.
The problem
Most campaign budgets are split by habit. The question a model can actually answer is: given what we know about each customer, where does one more dollar of contact cost produce the most responders?
How the real system works
- 01Features are built from demographics, tenure, recency, spend by category and past campaign responses.
- 02A classifier predicts response probability per customer; the Streamlit app exposes the score and the drivers.
- 03Customers are bucketed into segments, and budget is allocated by expected responders per dollar, not by segment size.
- 04The CLI exports the trained artifacts so the scoring can run offline in a pipeline.
What you take away
How a propensity score is built from a few features, and why allocating by marginal return beats allocating by headcount.