Using Aggregate-Disaggregate Data Fusion to Forecast the Inflow and Outflow of Customers

64 Pages Posted: 11 Apr 2019

See all articles by Daniel McCarthy

Daniel McCarthy

Emory University - Department of Marketing

Elliot Shin Oblander

Columbia Business School

Date Written: March 14, 2019

Abstract

When forecasting the number of customers a firm will acquire and lose, some analysts rely upon aggregated data provided by the firm, while others rely upon granular panel data provided by third parties. There are benefits and limitations to both data sources, so a natural idea would be to combine them, with the prospect of obtaining the benefits of both while mitigating their limitations. Fusing them together in a valid manner, however, is complicated for several reasons: the data sources operate at differing levels of granularity, the third-party data's panel members may be a non-representative sample, and both data sources may be censored and/or truncated. This issue is particularly severe when forecasting the inflow and outflow of customers, because target populations are often very large (e.g., over one billion) and outcomes are high-dimensional. We propose a computationally scalable estimator for this data structure which maximizes a "proxy likelihood" function that asymptotically approximates the model likelihood function. Under mild regularity conditions, our estimator achieves consistency and asymptotic normality. We apply this estimator to data from Spotify, a music streaming service. Through this application and supporting simulations, we show that incorporating third-party panel data significantly improves predictive validity over simpler methods.

Keywords: data fusion, prediction, marketing, finance

JEL Classification: M31, C13

Suggested Citation

McCarthy, Daniel and Oblander, Elliot Shin, Using Aggregate-Disaggregate Data Fusion to Forecast the Inflow and Outflow of Customers (March 14, 2019). Available at SSRN: https://ssrn.com/abstract=3362595 or http://dx.doi.org/10.2139/ssrn.3362595

Daniel McCarthy (Contact Author)

Emory University - Department of Marketing ( email )

Goizueta Business School
1300 Clifton Road
Atlanta, GA 30322
United States

Elliot Shin Oblander

Columbia Business School ( email )

New York, NY 10027
United States

HOME PAGE: http://elliotshinoblander.com

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