Scalable Data Fusion with Selection Correction: An Application to Customer Base Analysis-Reference-Cited by-同舟云学术

Scalable Data Fusion with Selection Correction: An Application to Customer Base Analysis

Published:2021-05 Issue:3 Volume:40 Page:459-480
ISSN:0732-2399
Container-title:Marketing Science
language:en
Short-container-title:Marketing Science

Author:

McCarthy Daniel Minh¹^ORCID,Oblander Elliot Shin²^ORCID

Affiliation:

1. Department of Marketing, Emory University, Atlanta, Georgia 30322;

2. Marketing Division, Columbia University, New York, New York 10027

Abstract

A computationally scalable, statistically efficient aggregate-disaggregate data fusion method that corrects for selection bias is applied to model customer relationship dynamics at a subscription-based firm.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Marketing,Business and International Management

Reference30 articles.

1. Do Disclosures of Customer Metrics Lower Investors’ and Analysts’ Uncertainty but Hurt Firm Performance?

2. Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market

3. Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model

4. Customer Franchise-A Hidden, Yet Crucial, Asset

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