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

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Abstract

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%.

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Data availability

The data set used in this study is available from the Pakistan's Largest E-Commerce Dataset repository on www.kaggle.com.

Notes

  1. Recency: the number of days since the last purchase date. Frequency: the number of all purchases. Monetary: the total amount of money spent on all purchases.

  2. See: https://keras.io/api/layers/reshaping_layers/repeat_vector/.

  3. See: https://github.com/ibrahimerdem/application_storage.

  4. See: https://tensorflow.org/guide/keras/masking_and_padding.

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  1. Department of Industrial Engineering, Cukurova University, Sarıçam, 01330, Adana, Türkiye

    İbrahim Erdem Kalkan & Cenk Şahin

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Kalkan, İ.E., Şahin, C. Evaluating cross-selling opportunities with recurrent neural networks on retail marketing. Neural Comput & Applic 35, 6247–6263 (2023). https://doi.org/10.1007/s00521-022-08019-1

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