Evaluating cross-selling opportunities with recurrent neural networks on retail marketing

Recommender systems are considered to be capable of predicting what the next product a customer should purchase is. It is crucial to identify which customers are more suitable than others to target a product for cross-selling in the retail industry. Using recurrent neural networks with self-attention mechanisms, this study proposes a hybrid model. Furthermore, the proposed design is capable of handling both sequential and non-sequential features, which correspond to purchase behavior and non-behavioral customer specific information, respectively. This study represents an alternative solution to a well-known business problem: improving cross-selling effectiveness by estimating customers' likelihood for which products or services to buy next time. A recommender system which works on additional data configurations is the core concept of the framework. With an online shopping data set, this study shows that concatenation of relevant features adds additional information to the model, and it is found that evaluation metrics are improved by approximately 12%.

Automated summary

Recommender systems can predict customers' next purchases. This study proposes a hybrid model that handles both sequential and non-sequential features to improve cross-selling effectiveness.