Predicting Market Basket Additions as a Way to Enhance Customer Service Levels

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Vera L. Miguéis (Author) FEUP; Teixeira, R (Author) Other

Conference proceedings International

Title: EXPLORING SERVICE SCIENCE (IESS 2020)

Pages: 121-134

10th International Conference on Exploring Service Science (IESS)

Univ Porto, Fac Engn, Porto, PORTUGAL, FEB 05-07, 2020

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00R-VSG

DOI: 10.1007/978-3-030-38724-2_9

Abstract (EN): It is imperative that online companies have a complete in-depth understanding of online behavior in order to provide a better service to their customers. This paper proposes a model for real-time basket addition in the e-grocery sector that includes predictors inferred from anonymous clickstream data, such as a Markov page view sequence discrimination value. This model aims at anticipating the addition and the non-addition of items to customers' market basket, in order to enable marketers to act conveniently, for example recommending more appropriate items. Two classification techniques are used in the empirical study: logistic regression and random forests. A real sample of anonymous clickstream data taken from the servers of a European e-retailing company is explored. The empirical results reveal the high predictive power of the model proposed, based on the explanatory variables introduced, as well as the supremacy of random forests over logistic regression.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14