<![CDATA[Evaluations of e-commerce marketing campaigns frequently depend on summary metrics like conversion and click-through rates, which fail to reveal the true causal effect of promotional activities. This study employs uplift modeling to estimate the individual-level causal impact of marketing interventions, clarifying where such approaches outperform traditional metrics, using both a simulated internal dataset and the Dunnhumby Complete Journey data. The objective is to identify which customer segments are causally influenced by marketing actions and to inform more precise targeting strategies. We implemented logistic regression, T Learner, and Causal Forest models to estimate individual treatment effects. Derived features include behavioral (recency, frequency, engagement), transactional (AOV, loyalty tier), and campaign based variables (channel, timing, offer type). Evaluation metrics include Uplift AUC, Qini Curve, and Precision@10%. Ethical safeguards such as pseudonymization and fairness audits were integrated throughout. Results show that Causal Forest significantly outperforms baseline models, achieving the highest uplift AUC and Precision@10%. Key drivers of uplift include campaign channel, customer recency, and loyalty tier. Segment analyses reveal that marketing effectiveness varies by lifecycle stage, device type, and region. Moreover, integrating uplift insights into real time marketing automation systems enables dynamic optimization of campaigns. In conclusion, uplift modeling offers a more robust framework for understanding and maximizing the causal impact of marketing strategies. It improves resource allocation, enhances personalization, and ensures marketing efforts are both effective and ethically responsible.]]>
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