How to Model The Expected Value of Marketing Campaigns

2026-02-10 · Source: Towards Data Science · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning, Marketing Analytics · Depth: Intermediate, quick

Summary

Expected Value Modeling offers a data-driven approach to optimize marketing campaigns beyond traditional trial-and-error methods. This framework integrates business knowledge with machine learning, specifically a Purchase Likelihood Model, to predict customer purchasing probabilities. By assigning specific costs and benefits to each outcome in a confusion matrix (True Positive, False Positive, False Negative, True Negative), the model calculates the expected profit for targeting decisions. For instance, an e-commerce example demonstrates that targeting customers with a purchase probability exceeding 2% maximizes profit, based on a $50 profit per product sold and a $1 cost per click. The approach can be further refined using profit curves to evaluate expected value across various targeting thresholds, allowing for strategic adjustments based on budget or desired customer segments.

Key takeaway

For Marketing Professionals or Data Scientists designing ad campaigns, applying Expected Value Modeling can significantly enhance ROI by moving beyond simple accuracy metrics. You should integrate specific business costs and benefits into your targeting models to identify precise customer segments with the highest expected profit. This allows for data-driven campaign design, ensuring resources are allocated to customers most likely to convert profitably, even under budget constraints.

Key insights

Expected Value Modeling optimizes marketing by quantifying costs and benefits of targeting decisions using purchase likelihood.

Principles

• Not all "wrong" predictions carry equal cost.
• Business knowledge is crucial for defining outcome values.
• Optimize for expected profit, not just accuracy.

Method

Combine a Purchase Likelihood Model with a confusion matrix, assign monetary values to each outcome (TP, FP, FN, TN), then calculate expected profit using P(buy) × Profit if buy + (1 — P(buy)) × Loss if no buy.

In practice

• Use Purchase Likelihood Model to predict customer intent.
• Define profit per product and cost per click.
• Identify optimal targeting thresholds via profit curves.

Topics

• Expected Value Modeling
• Marketing Analytics
• Purchase Likelihood Models
• Confusion Matrix
• Profit Curves

Best for: Data Scientist, Business Analyst, Marketing Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.