“Did the campaign actually cause the lift?” This section is about practical tools for answering that question in marketing. We’ll cover A/B testing, quasi-experiments, meta-learners for understanding which customers respond differently, and uplift modeling to help you target the people who actually change their behavior.
After this part, you will be able to:
- Distinguish correlation from causation in marketing data
- Design, run, and analyze A/B tests correctly
- Estimate causal effects when randomization isn’t possible
- Identify which customers benefit most from a treatment using uplift modeling
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