Bayesian Statistics and Marketing
Marketing Science
Structural Modeling in Marketing: Review and Assessment
Marketing Science
Invited Commentary---Marketing Structural Models: “Keep It Real”
Marketing Science
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Database Paper---The IRI Marketing Data Set
Marketing Science
Tipping and Concentration in Markets with Indirect Network Effects
Marketing Science
Retail Competition and the Dynamics of Demand for Tied Goods
Marketing Science
Structural Estimation of the Effect of Out-of-Stocks
Management Science
Online Demand Under Limited Consumer Search
Marketing Science
A Dynamic Model of Sponsored Search Advertising
Marketing Science
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In this note I overview the data selection and procurement process in the context of structural models. Data selection for structural models presents unique challenges because data and structure often substitute and because it is imperative to consider what information identifies causal effects of interest. I further discuss three types of field data on which to build empirical models: (i) data that are proprietary to firms, (ii) data that can come from the public domain, or (iii) data that can be purchased from private research firms, and I discuss the benefits and limits of each. I then detail a process for obtaining proprietary data and the potential pitfalls inherent in the process.