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Publications
  • 2020
  • Working Paper
  • HBS Working Paper Series

Detecting Routines in Ridesharing: Implications for Customer Management

By: Ryan Dew, Eva Ascarza, Oded Netzer and Nachum Sicherman
  • Format:Print
  • | Language:English
  • | Pages:88
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Abstract

Routines shape many aspects of day-to-day consumption. 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 reason for this dearth is the difficulty of measuring routines from transaction data, particularly when routines vary substantially across customers. We propose a new approach for doing so, which we apply in the context of ridesharing. We model customer-level routines with Bayesian nonparametric Gaussian processes (GPs), leveraging a novel kernel that allows for flexible yet precise estimation of routines. These GPs are nested in inhomogeneous Poisson processes of usage, allowing us to estimate customers’ routines, and decompose their usage into routine and non-routine parts. We show the value of detecting routines for customer relationship management (CRM) in the context of ridesharing, where we find that routines are associated with higher future usage and activity rates, and more resilience to service failures. Moreover, we show how these outcomes vary by the types of routines customers have, and by whether trips are part of the customer’s routine, suggesting a role for routines in segmentation and targeting.

Keywords

Ride-sharing; Routine; Machine Learning; Customer Relationship Management; Consumer Behavior; Segmentation

Citation

Dew, Ryan, Eva Ascarza, Oded Netzer, and Nachum Sicherman. "Detecting Routines in Ridesharing: Implications for Customer Management." Harvard Business School Working Paper, No. 23-060, March 2023.
  • SSRN
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About The Author

Eva Ascarza

Marketing
→More Publications

More from the Authors

    • 2022
    • Faculty Research

    When Less Is More: Using Short-term Signals to Overcome Systematic Bias in Long-run Targeting

    By: Ta-Wei Huang and Eva Ascarza
    • August 2022
    • Faculty Research

    Retail Media Networks

    By: Eva Ascarza, Ayelet Israeli and Celine Chammas
    • March 2022 (Revised March 2022)
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    Managing Customers in the Digital Era

    By: Eva Ascarza
More from the Authors
  • When Less Is More: Using Short-term Signals to Overcome Systematic Bias in Long-run Targeting By: Ta-Wei Huang and Eva Ascarza
  • Retail Media Networks By: Eva Ascarza, Ayelet Israeli and Celine Chammas
  • Managing Customers in the Digital Era By: Eva Ascarza
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