Evolution of Referrals over Customers’ Life Cycle: Evidence from a Ride-Sharing Platform

Carlos Fernández-Loría (), Maxime C. Cohen () and Anindya Ghose ()
Additional contact information
Carlos Fernández-Loría: Department of Information Systems, Business Analytics, and Operations Management, HKUST Business School, Hong Kong, China
Maxime C. Cohen: Department of Operations Management, Desautels Faculty of Management, McGill University, Montreal, Quebec H3A 1G5, Canada
Anindya Ghose: Department of Technology, Operations, and Statistics, Stern School of Business, New York, New York 10012

Information Systems Research, 2023, vol. 34, issue 2, 698-720

Abstract: Online platforms often ask their users to refer friends in exchange for a reward. This paper addresses how referral generation and referral value evolve throughout the customer’s life cycle as a function of service usage, experience level, and past referral behavior. Our analysis is based on a longitudinal data set that comprises the transactions and referral actions of 400,000 users in a ride-sharing platform over a year. The richness of our data set allows us to address two shortcomings from previous studies: modeling dynamic behavior (i.e., the relationship between past and future referrals) and accounting for unobserved heterogeneity across users. Our results show that users make more referrals when they have used the service recently and intensively. For example, users become 9% less likely to make referrals for each week they have not used the service. Furthermore, users make more high-value referrals as they become more experienced with the service. The referral generation and referral value of the top 10% most experienced users are more than 18% higher relative to when they first used the service. Finally, as users make more referrals, they become more likely to run out of friends to whom they can refer the service, leading to less referrals in the future. After users make their first referral, the probability of making additional referrals decreases by more than 78% and the value of subsequent referrals reduces by 19% on average. The results imply that platforms should consider tailoring their referral programs according to how referral generation and referral value evolve over time.

Keywords: referrals; ride-sharing; online platforms (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/isre.2022.1138 (application/pdf)

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:inm:orisre:v:34:y:2023:i:2:p:698-720

Access Statistics for this article

More articles in Information Systems Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().