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

Kalkan I. E., Şahin C.

NEURAL COMPUTING & APPLICATIONS, cilt.35, sa.8, ss.6247-6263, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 35 Sayı: 8
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s00521-022-08019-1
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Sayfa Sayıları: ss.6247-6263
  • Anahtar Kelimeler: Marketing management, Recurrent neural networks, Recommender systems, Cross-selling
  • Çukurova Üniversitesi Adresli: Evet

Özet

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%.