Skip to Main Content
HBS Home
  • About
  • Academic Programs
  • Alumni
  • Faculty & Research
  • Baker Library
  • Giving
  • Harvard Business Review
  • Initiatives
  • News
  • Recruit
  • Map / Directions
Faculty & Research
  • Faculty
  • Research
  • Featured Topics
  • Academic Units
  • …→
  • Harvard Business School→
  • Faculty & Research→
Publications
Publications
  • Forthcoming
  • Article
  • Manufacturing & Service Operations Management

Demand Learning and Pricing for Varying Assortments

By: Kris Ferreira and Emily Mower
  • Format:Print
ShareBar

Abstract

Problem Definition: We consider the problem of demand learning and pricing for retailers who offer assortments of substitutable products that change frequently, e.g., due to limited inventory, perishable or time-sensitive products, or the retailer’s desire to frequently offer new styles. Academic/Practical Relevance: We are one of the first to consider the demand learning and pricing problem for retailers who offer product assortments that change frequently, and we propose and implement a learn-then-earn algorithm for use in this setting. Our algorithm prioritizes a short learning phase, an important practical characteristic that is only considered by a few other algorithms. Methodology: We develop a novel demand learning and pricing algorithm that learns quickly in an environment with varying assortments and limited price changes by adapting the commonly used marketing technique of conjoint analysis to our setting. We partner with Zenrez, an e-commerce company that partners with fitness studios to sell excess capacity of fitness classes, to implement our algorithm in a controlled field experiment to evaluate its effectiveness in practice using the synthetic control method. Results: Relative to a control group, our algorithm led to an expected initial dip in revenue during the learning phase, followed by a sustained and significant increase in average daily revenue of 14–18% throughout the earning phase, illustrating that our algorithmic contributions can make a significant impact in practice. Managerial Implications: The theoretical benefit of demand learning and pricing algorithms is well understood—they allow retailers to optimally match supply and demand in the face of uncertain pre-season demand. However, most existing demand learning and pricing algorithms require substantial sales volume and the ability to change prices frequently for each product. Our work provides retailers who do not have this luxury a powerful demand learning and pricing algorithm that has been proven in practice.

Keywords

Experiments; Pricing And Revenue Management; Retailing; Demand Estimation; Pricing Algorithm; Marketing; Price; Demand and Consumers; Mathematical Methods

Citation

Ferreira, Kris, and Emily Mower. "Demand Learning and Pricing for Varying Assortments." Manufacturing & Service Operations Management (forthcoming).
  • Find it at Harvard
  • Read Now

About The Author

Kris Johnson Ferreira

Technology and Operations Management
→More Publications

More from the Authors

    • 2022
    • Faculty Research

    Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence

    By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
    • March 2022
    • Faculty Research

    JOANN: Joannalytics Inventory Allocation Tool

    By: Kris Ferreira
    • March 2022
    • Management Science

    Learning to Rank an Assortment of Products

    By: Kris Ferreira, Sunanda Parthasarathy and Shreyas Sekar
More from the Authors
  • Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
  • JOANN: Joannalytics Inventory Allocation Tool By: Kris Ferreira
  • Learning to Rank an Assortment of Products By: Kris Ferreira, Sunanda Parthasarathy and Shreyas Sekar
ǁ
Campus Map
Harvard Business School
Soldiers Field
Boston, MA 02163
→Map & Directions
→More Contact Information
  • Make a Gift
  • Site Map
  • Jobs
  • Harvard University
  • Trademarks
  • Policies
  • Accessibility
  • Digital Accessibility
Copyright © President & Fellows of Harvard College