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Professor Ganesh Iyer
Edgar F. Kaiser Professor in Business Administration
Senior Editor for Marketing Science
Haas School of Business
University of California, Berkeley

ABSTRACT

We study how market competition influences the algorithmic design choices of firms in the context of targeting. Firms face a general bias-variance tradeoff when choosing the design of a supervised learning algorithm in terms of model complexity or the number of predictors to accommodate. Each firm has a data analyst who uses the chosen algorithm to estimate demand for multiple consumer segments, based on which, it devises a targeting policy to maximize estimated profits. We show that competition induces firms to strategically choose simpler algorithms which involve more bias but lower variance. Therefore, more complex/flexible algorithms may have higher value for firms with greater monopoly power.