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Scalable Price Targeting

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  • Jean-Pierre Dubé
  • Sanjog Misra

Abstract

We study the welfare implications of scalable price targeting, an extreme form of third-degree price discrimination implemented with machine learning for a large, digital firm. Targeted prices are computed by solving the firm's Bayesian Decision-Theoretic pricing problem based on a database with a high-dimensional vector of customer features that are observed prior to the price quote. To identify the causal effect of price on demand, we first run a large, randomized price experiment and use these data to train our demand model. We use l1 regularization (lasso) to select the set of customer features that moderate the heterogeneous treatment effect of price on demand. We use a weighted likelihood Bayesian bootstrap to quantify the firm's approximate statistical uncertainty in demand and profitability. We then conduct a second experiment that implements our proposed price targeting scheme out of sample. Theoretically, both firm and customer surplus could rise with scalable price targeting. Optimized uniform pricing improves revenues by 64.9% relative to the control pricing, whereas scalable price targeting improves revenues by 81.5%. Firm profits increase by over 10% under targeted pricing relative to optimal uniform pricing. Customer surplus declines by less than 1% with price targeting; although nearly 70% of customers are charged less than the uniform price. Our weighted likelihood bootstrap estimator also predicts demand and demand uncertainty out of sample better than several alternative approaches.

Suggested Citation

  • Jean-Pierre Dubé & Sanjog Misra, 2017. "Scalable Price Targeting," NBER Working Papers 23775, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:23775
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    References listed on IDEAS

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    1. Ben Shiller & Joel Waldfogel, 2009. "Music for a Song: An Empirical Look at Uniform Song Pricing and its Alternatives," NBER Working Papers 15390, National Bureau of Economic Research, Inc.
    2. Sanjog Misra & Harikesh Nair, 2011. "A structural model of sales-force compensation dynamics: Estimation and field implementation," Quantitative Marketing and Economics (QME), Springer, vol. 9(3), pages 211-257, September.
    3. Alessandro Acquisti & Hal R. Varian, 2005. "Conditioning Prices on Purchase History," Marketing Science, INFORMS, vol. 24(3), pages 367-381, May.
    4. Günter J. Hitsch, 2006. "An Empirical Model of Optimal Dynamic Product Launch and Exit Under Demand Uncertainty," Marketing Science, INFORMS, vol. 25(1), pages 25-50, 01-02.
    5. Jean Tirole, 1988. "The Theory of Industrial Organization," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262200716, January.
    6. Benjamin Reed Shiller, 2013. "First Degree Price Discrimination Using Big Data," Working Papers 58, Brandeis University, Department of Economics and International Businesss School, revised Jan 2014.
    7. Bauner, Christoph, 2015. "Mechanism choice and the buy-it-now auction: A structural model of competing buyers and sellers," International Journal of Industrial Organization, Elsevier, vol. 38(C), pages 19-31.
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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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