Causal Uplift Modeling for Grocery Loyalty Coupon Targeting Among Persuadable Grocery Shoppers
Keywords:
Causal Inference, Uplift Modeling, Grocery Retail, Loyalty Programs, Targeted MarketingAbstract
The grocery retail sector operates on notoriously thin profit margins, making the efficient allocation of promotional budgets a critical imperative. Traditional targeted marketing relies heavily on predictive response models that estimate the probability of a customer redeeming a coupon. However, these models inherently conflate correlation with causation, often targeting customers who would have made a purchase regardless of the promotion. This paper investigates the application of causal uplift modeling to optimize loyalty coupon targeting by directly estimating the incremental impact of a promotional intervention on customer purchase behavior. Utilizing observational and experimental data from a large-scale grocery loyalty program, we evaluate the efficacy of various meta-learning architectures and tree-based uplift algorithms in isolating the persuadable customer segment. Our comprehensive analysis demonstrates that uplift modeling significantly outperforms traditional propensity models in generating incremental revenue and return on investment. By minimizing marketing waste associated with the treatment of sure things and avoiding negative impacts on sleeping dogs, causal models provide a more rigorous framework for promotional decision-making. The findings offer substantial managerial implications for retail marketers seeking to transition from predictive to prescriptive analytics, ultimately enhancing the profitability and sustainability of customer relationship management strategies in highly competitive retail environments.
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