A neural network based price sensitive recommender model to predict customer choices based on price effect

• Published in: Journal of retailing and consumer services Vol. 61; p. 102573
• Main Authors: Chen, Shiuann-Shuoh, Choubey, Bhaskar, Singh, Vinay
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2021
• Subjects: Consumer behaviour; Customer-product interaction; Deep learning; Matrix -factorization; Price sensitivities; Deep learning; Matrix -factorization; Customer-product interaction; Price sensitivities; Consumer behaviour
• ISSN: 0969-6989; 1873-1384
• DOI: 10.1016/j.jretconser.2021.102573

The impact of price and price changes should not be ignored while designing algorithms for predicting customer choice. Consumer preferences should be modeled with consideration of price effects. Businesses need to consider for efficient prediction of an individual's purchase behaviour. Personalized recommendation systems have been studied with machine learning algorithms. However, the price-aware personalized recommendation has received little attention. In this paper, we attempt to capture insightful economic results considered in the marketing and economics disciplines by employing modern machine learning architecture for predicting customer choice in a large-scale supermarket context. We extract personalized price sensitivities and examine their importance in consumer behaviour. The employed data collected from a supermarket chain in Germany consists of implicit feedback based on customer-product interactions and the price of every interaction. We propose a two-pathway matrix factorization (2way-MF) model that is price-aware and tries to memorize customer-product interaction's implicit feedback. The proposed models achieve better model performance than standard Matrix Factorization models widely used in the industry. The approach was re-validated with data from supermarket chain in Taiwan. Other industries can adopt the proposed framework of modeling customer's preferences based on price sensitivity. We suggest that further research and analyses could help understand the cross-price elasticities. [Display omitted] •Customers may have complex purchase histories between categories. Promotions by categories may not help increase revenue.•Offering promotional discounts on targeted products is necessary. personalized promotions would imply greater revenue.•The study demonstrates Matrix Factorization with Deep Learning can better predict customer choice based on price effects.•We propose two models considering price effects - 2way-Matrix Factorization and Matrix Factorization with Deep Learning.•We use the customer-product interactions as implicit feedback as well as the varying product prices.•Effective promotions require insights into the impact of price. Discerning price effects at the category level would be insufficient.•The proposed framework can be further translated to industries like clothing and electronics. Where the implementation cost is unpredictable, and success is still not guaranteed.