A causal inference framework for personalized badge selection at Walmart

In E-Commerce platforms such as Walmart, product badges (Bestseller, Reduced Price, etc.) play a pivotal role in guiding customer choices and enhancing product discoverability. Most E-Commerce platforms face a multi-badge selection problem in the sense that an item is eligible for multiple badges but there exists real estate to display only one product badge per item tile on search/browse pages. Multi-badge selection requires us to identify the most impactful badge, for a given context, from a list of eligible badges. There exists limited literature on multi-badge selection and the potential impact of such solutions. In this paper, we propose a novel causal-inference based personalized multi-badge selection framework which estimates preferences for different badges among individual customers and item segments based on historical interactions in an offline mode. The customer-level and item-level badge preferences thus estimated are used in the front-end to identify the most relevant badge for a given item on a search/browse page in real time. This process of estimating badge preference offline and using only the relevant estimated badge preferences in real time greatly helps in achieving tight latency requirements at the front-end for E-Commerce platforms operating at a large scale. The proposed multi-badge selection framework is evaluated using on-line tests (A/B tests) in Walmart web and app platforms, resulting in statistically significant improvements in the number of add-to-carts (ATCs), units purchased, gross merchandise value (GMV), etc. corroborating the efficacy of the proposed framework deployed in a real-world scenario.