Personalized Pricing and Consumer Welfare / Jean-Pierre Dubé, Sanjog Misra.

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Dubé, Jean-Pierre

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Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w23775.Publication details: Cambridge, Mass. National Bureau of Economic Research 2017.Description: 1 online resource: illustrations (black and white)Subject(s):

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Abstract: We study the welfare implications of personalized pricing, an extreme form of third-degree price discrimination implemented with machine learning for a large, digital firm. Using data from a unique randomized controlled pricing field experiment we train a demand model and conduct inference about the effects of personalized pricing on firm and consumer surplus. In a second experiment, we validate our predictions in the field. The initial experiment reveals unexercised market power that allows the firm to raise its price optimally, generating a 55% increase in profits. Personalized pricing improves the firm's expected posterior profits by an additional 19%, relative to the optimized uniform price, and by 86%, relative to the firm's unoptimized status quo price. Turning to welfare effects on the demand side, total consumer surplus declines 23% under personalized pricing relative to uniform pricing, and 47% relative to the firm's unoptimized status quo price. However, over 60% of consumers benefit from lower prices under personalization and total welfare can increase under standard inequity-averse welfare functions. Simulations with our demand estimates reveal a non-monotonic relationship between the granularity of the segmentation data and the total consumer surplus under personalization. These findings indicate a need for caution in the current public policy debate regarding data privacy and personalized pricing insofar as some data restrictions may not per se improve consumer welfare.

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Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w23775 (Browse shelf(Opens below))	Not For Loan

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September 2017.

We study the welfare implications of personalized pricing, an extreme form of third-degree price discrimination implemented with machine learning for a large, digital firm. Using data from a unique randomized controlled pricing field experiment we train a demand model and conduct inference about the effects of personalized pricing on firm and consumer surplus. In a second experiment, we validate our predictions in the field. The initial experiment reveals unexercised market power that allows the firm to raise its price optimally, generating a 55% increase in profits. Personalized pricing improves the firm's expected posterior profits by an additional 19%, relative to the optimized uniform price, and by 86%, relative to the firm's unoptimized status quo price. Turning to welfare effects on the demand side, total consumer surplus declines 23% under personalized pricing relative to uniform pricing, and 47% relative to the firm's unoptimized status quo price. However, over 60% of consumers benefit from lower prices under personalization and total welfare can increase under standard inequity-averse welfare functions. Simulations with our demand estimates reveal a non-monotonic relationship between the granularity of the segmentation data and the total consumer surplus under personalization. These findings indicate a need for caution in the current public policy debate regarding data privacy and personalized pricing insofar as some data restrictions may not per se improve consumer welfare.