Solving the Peanut Butter Problem
We first define the problem from a business perspective and argue for how this solution helps a store’s bottom line. Then we propose a design for building a recommendation engine that uses available data to estimate a percent match between all products within a store. Finally, we propose an architecture for hosting the product data and recommender using a modern data warehouse.
Our proof of concept shows that the recommendation engine successfully identifies meaningful product substitutions. The design is robust to work with limited data and flexible to allow for customization at both the store level and customer level.
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“ One of the most challenging aspects of translating an in-store experience to an online experience
centers around product and inventory out of stocks.”
- Steve Krause, BlueGranite Retail Lead