Template-Type: ReDIF-Paper 1.0
Author-Name: Jacobs, B.J.D.
Author-Name-Last: Jacobs
Author-Name-First: Bruno
Author-Name: Fok, D.
Author-Name-Last: Fok
Author-Name-First: Dennis
Author-Name: Donkers, A.C.D.
Author-Name-Last: Donkers
Author-Name-First: Bas
Author-Person: pdo14
Title: Understanding Large-Scale Dynamic Purchase Behavior
Abstract: In modern retail contexts, retailers sell products from vast product assortments to a
 large and heterogeneous customer base. Understanding purchase behavior in such a
 context is very important. Standard models cannot be used due to the high dimen-
 sionality of the data. We propose a new model that creates an efficient dimension
 reduction through the idea of purchase motivations. We only require customer-level
 purchase history data, which is ubiquitous in modern retailing. The model han-
 dles large-scale data and even works in settings with shopping trips consisting of
 few purchases. As scalability of the model is essential for practical applicability,
 we develop a fast, custom-made inference algorithm based on variational inference.
 Essential features of our model are that it accounts for the product, customer and
 time dimensions present in purchase history data; relates the relevance of moti-
 vations to customer- and shopping-trip characteristics; captures interdependencies
 between motivations; and achieves superior predictive performance. Estimation re-
 sults from this comprehensive model provide deep insights into purchase behavior.
 Such insights can be used by managers to create more intuitive, better informed,
 and more effective marketing actions. We illustrate the model using purchase history
 data from a Fortune 500 retailer involving more than 4,000 unique products.
Length: 45
Creation-Date: 2020-08-01
File-URL: https://repub.eur.nl/pub/129674/ERS-2020-010-MKT.pdf
File-Format: application/pdf
Series: RePEc:ems:eureri
Number: ERS-2020-010-MKT
Keywords: dynamic purchase behavior, large-scale assortment, purchase history data, topic model, machine learning, variational inference
Handle: RePEc:ems:eureri:129674