Cost-Effective Acquisition of First-Party Data for Business Analytics

Xiaoping Liu () and Xiao-Bai Li ()
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Xiaoping Liu: D’Amore-McKim School of Business, Northeastern University, Boston, Massachusetts 02115
Xiao-Bai Li: Department of Operations and Information Systems, University of Massachusetts Lowell, Lowell, Massachusetts 01854

INFORMS Journal on Computing, 2024, vol. 36, issue 5, 1242-1260

Abstract: Customer data acquisition is an important task in data-driven business analytics. Recently, there has been a growing interest in the effective use of an organization’s internal customer data, also known as first-party data. This work studies the acquisition of new data for business analytics based on first-party data resource. We address issues related to both acquisition cost and data quality. To reduce acquisition cost, we consider using auction-based methods, such as the generalized second price (GSP) auction, for acquiring data with differential prices for different customers. We find that the GSP-based data acquisition method incurs a lower cost and/or achieves a higher response rate than fixed price methods. To maximize data quality, we propose novel optimization models for different data acquisition methods and data quality measures. The proposed models maximize the quality of the acquired data while satisfying budget constraints. We derive and discuss the solutions to the optimization models analytically and provide managerial insights from the solutions. The proposed approach is effective in increasing customer responses, reducing selection bias, and enabling more accurate estimation and prediction for business analytics. The results of the experimental evaluation demonstrate the advantage of the proposed approach over existing data acquisition methods.

Keywords: data acquisition; optimization; data science; data analytics; business intelligence (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:36:y:2024:i:5:p:1242-1260

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