A high-performance turnkey system for customer lifetime value prediction in retail brands

Yan Yan () and Nicholas Resnick ()
Additional contact information
Yan Yan: Amperity, Inc.
Nicholas Resnick: Amperity, Inc.

Quantitative Marketing and Economics (QME), 2024, vol. 22, issue 2, No 2, 169-192

Abstract: Abstract Customer lifetime value (CLV) modeling underpins modern marketing analytics, enabling the development of tailored customer relationship management strategies based on the predicted future value of their customers. As part of Amperity’s enterprise customer data platform (CDP), we deploy and maintain a CLV prediction system that caters to a rapidly growing list of brands across various industries, purchase behaviors, and scales. Given the impracticality of developing bespoke models for each brand, our solution must be adaptive, generalizable, and high-performing ”out of the box”. Furthermore, our platform demands daily prediction updates to facilitate prompt marketing decisions. This paper introduces a turnkey CLV prediction system that achieves state-of-the-art performance across a diverse set of brands. This system has several contributions: 1) the use of encodings and embeddings to incorporate signals from high-cardinality data; 2) a multi-stage churn-CLV modeling framework that augments additional flexibility in adjusting churn probabilities, subsequently reducing CLV prediction errors while maintaining a synergistic learning process; 3) a feature-weighted ensemble of both generative and discriminative models to accommodate diverse underlying purchase patterns. Empirical results show that our enhanced model consistently surpasses benchmark performances for twelve retail brands across six evaluation intervals from June 2020 to September 2022.

Keywords: Customer lifetime value; Churn prediction; Ensemble modeling; Marketing analytics; Customer data platform (search for similar items in EconPapers)
JEL-codes: M31 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11129-023-09272-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
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:22:y:2024:i:2:d:10.1007_s11129-023-09272-x

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

DOI: 10.1007/s11129-023-09272-x

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 ().