Traditionally, marketing campaign analysis relies on simple metrics like the number of purchases made after a contact, or conversions following a promotion. While these numbers tell us what happened, they don’t reveal why it happened or if the campaign truly made a difference.

Such analysis can’t distinguish between customers who would have acted anyway and those who were genuinely influenced by the campaign. The key question is: did the campaign actually cause the desired effect?

In this practical and beginner-friendly session, we’ll explore how Causal Machine Learning provides the missing piece in campaign evaluation and targeting.

Starting from real-world scenarios, we’ll dive into:

• Why causality matters more than correlation when evaluating ad performance.
• How to estimate the true impact of a campaign using uplift modeling and treatment effect estimation in just a few lines of code.
• How to target users who are not just likely to interact with ads, but whose behavior can be influenced by the campaign (for example, to reduce churn or boost engagement).

The session will be hands-on with Python, with clear examples drawn from marketing applications.

Take-away:
Participants will gain a practical understanding of how to think causally in digital marketing, learning key techniques to measure impact and target campaigns more intelligently. moving from predictive to truly prescriptive analytics.