Leveraging loyalty programs using competitor based targeting

Wayne Taylor () and Brett Hollenbeck
Additional contact information
Wayne Taylor: Southern Methodist University

Quantitative Marketing and Economics (QME), 2021, vol. 19, issue 3, No 6, 417-455

Abstract: Abstract Loyalty programs (LPs) are widely used by firms but not well understood. These programs provide discounts and perks to loyal customers and are costly to administer, but produce uncertain changes in spending patterns. We use a large and detailed dataset on customer shopping behavior at one of the largest U.S. retailers before and after joining a loyalty program to evaluate how behavior changes. We combine this with detailed spatial data on customer and store locations, including the locations of competing firms. We find significant changes in behavior associated with joining the LP with a large amount of heterogeneity across customers. We find that location relative to competitors is the factor most strongly associated with increases in spending following joining the LP, suggesting that the LP’s quantity discounts work primarily through business stealing and not through other demand expansion. We next estimate a model of what variables determine how spending will change after joining the LP. We use high-dimensional data on spatial relationships between customers, the focal firm’s stores, and competing stores as well as customers’ historical spending patterns. This model is used to test whether past sales data reflecting customer’s vertical value to the firm or spatial data reflecting customer’s horizontal vulnerability are more important determinants of post-LP spending increases. We show how LASSO regularization estimated on complex spatial relationships are more effective than are models using past sales data or simpler spatial models. Finally, we show how firms can use customer and competitor location data to substantially increase LP performance through spatially driven segmentation.

Keywords: Loyalty programs; Spatial models; Retail competition; LASSO estimation (search for similar items in EconPapers)
JEL-codes: C50 M31 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s11129-021-09237-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
Working Paper: Leveraging Loyalty Programs Using Competitor Based Targeting (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:qmktec:v:19:y:2021:i:3:d:10.1007_s11129-021-09237-y

Ordering information: This journal article can be ordered from
http://www.springer. ... ng/journal/11129/PS2

DOI: 10.1007/s11129-021-09237-y

Access Statistics for this article

Quantitative Marketing and Economics (QME) is currently edited by Pradeep Chintagunta

More articles in Quantitative Marketing and Economics (QME) from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().