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The Effectiveness of Field Price Discretion: Empirical Evidence from Auto Lending

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  • Robert Phillips

    (Columbia Business School, New York, New York 10027; and Nomis Solutions, San Bruno, California 94066)

  • A. Serdar Şimşek

    (School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853)

  • Garrett van Ryzin

    (Columbia Business School, New York, New York 10027)

Abstract

In many markets, it is common for headquarters to create a price list but grant local salespeople discretion to negotiate prices for individual transactions. How much (if any) pricing discretion headquarters should grant is a topic of debate within many firms. We investigate this issue using a unique data set from an indirect lender with local pricing discretion. We estimate that the local sales force adjusted prices in a way that improved profits by approximately 11% on average. A counterfactual analysis shows that using a centralized, data-driven pricing optimization system could improve profits even further, up to 20% over those actually realized. This suggests that centralized pricing—if appropriately optimized—can be more effective than field price discretion. We discuss the implications of these findings for auto lending and other industries with similar pricing processes. This paper was accepted by Serguei Netessine, operations management .

Suggested Citation

  • Robert Phillips & A. Serdar Şimşek & Garrett van Ryzin, 2015. "The Effectiveness of Field Price Discretion: Empirical Evidence from Auto Lending," Management Science, INFORMS, vol. 61(8), pages 1741-1759, August.
    • Handle: RePEc:inm:ormnsc:v:61:y:2015:i:8:p:1741-1759
    DOI: 10.1287/mnsc.2014.2084
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    References listed on IDEAS

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    Cited by:

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    6. Christopher Bockel-Rickermann & Sam Verboven & Tim Verdonck & Wouter Verbeke, 2023. "A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions," Papers 2309.03730, arXiv.org.
    7. Özalp Özer & Upender Subramanian & Yu Wang, 2018. "Information Sharing, Advice Provision, or Delegation: What Leads to Higher Trust and Trustworthiness?," Management Science, INFORMS, vol. 64(1), pages 474-493, January.
    8. Song-Hee Kim & Carri W. Chan & Marcelo Olivares & Gabriel Escobar, 2015. "ICU Admission Control: An Empirical Study of Capacity Allocation and Its Implication for Patient Outcomes," Management Science, INFORMS, vol. 61(1), pages 19-38, January.
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    10. Wang Chi Cheung & David Simchi-Levi & Ruihao Zhu, 2022. "Hedging the Drift: Learning to Optimize Under Nonstationarity," Management Science, INFORMS, vol. 68(3), pages 1696-1713, March.
    11. Achal Bassamboo & Antonio Moreno & Ioannis Stamatopoulos, 2020. "Inventory Auditing and Replenishment Using Point‐of‐Sales Data," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1219-1231, May.
    12. Lukas, M., 2019. "Relative prices and product substitution: Evidence from shocks to consumer credit interest rates," Journal of Behavioral and Experimental Finance, Elsevier, vol. 21(C), pages 39-49.
    13. Lukas, Moritz, 2017. "Estimating interest rate elasticities in consumer credit," Economics Letters, Elsevier, vol. 156(C), pages 155-158.
    14. Renzhe Xu & Xingxuan Zhang & Peng Cui & Bo Li & Zheyan Shen & Jiazheng Xu, 2022. "Regulatory Instruments for Fair Personalized Pricing," Papers 2202.04245, arXiv.org, revised Feb 2022.
    15. Saravanan Kesavan & Tarun Kushwaha, 2020. "Field Experiment on the Profit Implications of Merchants’ Discretionary Power to Override Data-Driven Decision-Making Tools," Management Science, INFORMS, vol. 66(11), pages 5182-5190, November.
    16. Jan-Peter Kucklick & Jennifer Priefer & Daniel Beverungen & Oliver Müller, 2023. "Elucidating the Predictive Power of Search and Experience Qualities for Pricing of Complex Goods – A Machine Learning-based Study on Real Estate Appraisal," Working Papers Dissertations 112, Paderborn University, Faculty of Business Administration and Economics.
    17. Suresh P. Sethi & Sushil Gupta & Vipin K. Agrawal & Vijay K. Agrawal, 2022. "Nobel laureates’ contributions to and impacts on operations management," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4283-4303, December.
    18. Sandro Shelegia & Joshua Sherman, 2022. "Bargaining at Retail Stores: Evidence from Vienna," Management Science, INFORMS, vol. 68(1), pages 27-36, January.
    19. Teck-Hua Ho & Noah Lim & Sadat Reza & Xiaoyu Xia, 2017. "OM Forum—Causal Inference Models in Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 509-525, October.
    20. Bharadwaj Kadiyala & Robert Phillips & A. Serdar Şimşek & Garrett van Ryzin, 2023. "Predicting transaction outcomes under customized pricing with discretion: A structural estimation approach," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1654-1673, June.
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    22. Hermann Jahnke & Jan Thomas Martini & Tobias Wiens, 2019. "Price limits under incomplete preference information based on almost stochastic dominance," Business Research, Springer;German Academic Association for Business Research, vol. 12(1), pages 241-269, April.

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