Detecting Routines in Ride-sharing: Implications for Customer Management

56 Pages Posted: 11 Feb 2022

See all articles by Ryan Dew

Ryan Dew

University of Pennsylvania - Marketing Department

Eva Ascarza

Harvard Business School

Oded Netzer

Columbia University - Columbia Business School, Marketing

Nachum Sicherman

Columbia University; IZA Institute of Labor Economics

Date Written: December 10, 2021

Abstract

Routines often shape many aspects of day-to-day consumption, including transportation choice, use of mobile apps, or visits to a gym. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines, which we define as repeated behaviors with recurring temporal structures, for customer management. One possible reason for this lack of research is the difficulty of statistically modeling routines with customer-level transaction data, particularly when routines may vary substantially across customers. In this paper, we propose a new approach to measuring routine consumption, which we apply in the context of ride-sharing. We model customer-level routines with a hierarchical, Bayesian nonparametric Gaussian process, leveraging a novel kernel structure that allows for flexible yet precise estimation of routine behavior. We then nest this Gaussian process in an individual-level inhomogeneous Poisson point process, which allows us to estimate individual-level routines from transaction data, and decompose a customer’s overall usage into routine and non-routine components. We show that more routine users tend to be more valuable customers, with higher individual-level “routineness” being associated with higher future usage, lower churn rates, and more resilience to service failures.

Keywords: routines, customer management, customer relationship management, Bayesian nonparametrics, Gaussian processes, machine learning, ride-sharing

Suggested Citation

Dew, Ryan and Ascarza, Eva and Netzer, Oded and Sicherman, Nachum, Detecting Routines in Ride-sharing: Implications for Customer Management (December 10, 2021). Available at SSRN: https://ssrn.com/abstract=3982612 or http://dx.doi.org/10.2139/ssrn.3982612

Ryan Dew (Contact Author)

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States

Eva Ascarza

Harvard Business School ( email )

Soldiers Field
Boston, MA 02163
United States

HOME PAGE: http://evaascarza.com

Oded Netzer

Columbia University - Columbia Business School, Marketing ( email )

New York, NY 10027
United States

Nachum Sicherman

Columbia University ( email )

3022 Broadway
New York, NY 10027
United States
212-854-4464 (Phone)
212-316-9355 (Fax)

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

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