Finding Lookalike Customers for E-Commerce Marketing
This addresses the need for e-commerce companies like Walmart to scale customer-centric marketing campaigns to drive business growth, though it appears incremental as it builds on existing embedding and search methods.
The paper tackles the problem of expanding targeted marketing audiences for e-commerce by developing a scalable system that uses deep learning embeddings and approximate nearest neighbor search to find lookalike customers, achieving efficient handling of hundreds of millions of customers.
Customer-centric marketing campaigns generate a large portion of e-commerce website traffic for Walmart. As the scale of customer data grows larger, expanding the marketing audience to reach more customers is becoming more critical for e-commerce companies to drive business growth and bring more value to customers. In this paper, we present a scalable and efficient system to expand targeted audience of marketing campaigns, which can handle hundreds of millions of customers. We use a deep learning based embedding model to represent customers and an approximate nearest neighbor search method to quickly find lookalike customers of interest. The model can deal with various business interests by constructing interpretable and meaningful customer similarity metrics. We conduct extensive experiments to demonstrate the great performance of our system and customer embedding model.