Bigger Is Better, But At What Cost?
Many firms depend on third-party vendors to supply data for commercial predictive modeling applications. An issue that has received very little attention in the prior research literature is the estimation of a fair price for purchased data. In this work we present a methodology for estimating the economic value of adding incremental data to predictive modeling applications and present two cases studies. The methodology starts with estimating the effect that incremental data has on model performance in terms of common classification evaluation metrics. This effect is then translated into economic units, which gives an expected economic value that the firm might realize with the acquisition of a particular data asset. With this estimate a firm can then set a data acquisition price that targets a particular return on investment. This article presents the methodology in full detail and illustrates it in the context of two marketing case studies.