Beating the Algorithm: Consumer Manipulation, Personalized Pricing, and Big Data Management

Problem definition: Firms heavily invest in big data technologies to collect consumer data and infer consumer preferences for price discrimination. However, consumers can use technological devices to manipulate their data and fool firms to obtain better deals. We examine how a firm invests in collecting consumer data and makes pricing decisions and whether it should disclose its scope of data collection to consumers who can manipulate their data. Methodology/results: We develop a game-theoretic model to consider a market in which a firm caters to consumers with heterogeneous preferences for a product. The firm collects consumer data to identify their types and issue an individualized price, whereas consumers can incur a cost to manipulate data and mimic the other type. We find that when the firm does not disclose its scope of data collection to consumers, it collects more consumer data. When the firm discloses its scope of data collection, it reduces data collection even when collecting more data is costless. The optimal scope of data collection increases when it is more costly for consumers to manipulate data but decreases when consumer demand becomes more heterogeneous. Moreover, a lower cost for consumers to manipulate data can be detrimental to both the firm and consumers. Lastly, disclosure of data collection scope increases firm profit, consumer surplus, and social welfare. Managerial implications: Our findings suggest that a firm should adjust its scope of data collection and prices based on whether the firm discloses the data collection scope, consumers’ manipulation cost, and demand heterogeneity. Public policies should require firms to disclose their data collection scope to increase consumer surplus and social welfare. Even without such a mandatory disclosure policy, firms should voluntarily disclose their data collection scope to increase profit. Moreover, public educational programs that train consumers to manipulate their data or raise their awareness of manipulation tools can ultimately hurt consumers and firms. Funding: X. Li acknowledges financial support from the Hong Kong Research Grants Council Grant 21500920. K. J. Li acknowledges financial support from the Weimer Faculty Fellowship. Supplemental Material: The Online Appendix is available at https://doi.org/10.1287/msom.2022.1153 .