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.
What you'll learn:
- How today's economic environment is putting a high strain on traditional stores and ecommerce
- Technical challenges relating to supply chain disruptions
- Identifying substitute products through predictive models
- Business value of out-of-stock recommenders
- Incorporating continuous customers feedback
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