Estimating Customer Wallet Size and Allocation without Surveys: Evidence from E-Commerce Transaction

Thomas M. Menino Convention & Exhibition Center 

A single company typically supplies only a fraction of a customer's total demand, making both Size-of-Wallet (SioW) and Share-of-Wallet (SoW) unobservable from the firm's perspective. Existing studies often rely on survey data in which customers self-estimate their Share-of-Wallet, an approach that is impractical for scalable and periodic estimation. This study proposes a survey-free methodology based on statistical modeling and machine learning to estimate SoW and SioW directly from transactional data. The approach infers latent wallet size and allocation behavior without customer self-reports and is illustrated using purchase data from Adidas customers on the Amazon platform.

Share-of-Wallet

Size-of-Wallet

Machine Learning

Latent variable

Pogit model

Customer Analytics 

Section on Statistics in Marketing