Demand Estimation under Uncertain Consideration Sets

88 Pages Posted: 27 Jun 2019 Last revised: 13 Oct 2022

See all articles by Srikanth Jagabathula

Srikanth Jagabathula

New York University (NYU) - Department of Information, Operations, and Management Sciences

Dmitry Mitrofanov

Boston College, Carroll School of Management

Gustavo Vulcano

Universidad Torcuato Di Tella - School of Business

Date Written: October 12, 2022

Abstract

To estimate customer demand, choice models rely both on what the individuals do and do not purchase. A customer may not purchase a product because it was not offered, but also because it was not considered. To account for this behavior, existing literature has proposed the so-called consider-then-choose (CTC) models, which posit that customers sample a consideration set and then choose the most preferred product from the intersection of the offer set and the consideration set. CTC models have been studied quite extensively in the marketing literature. More recently, they have gained popularity within the Operations Management literature to make assortment and pricing decisions. Despite their richness, CTC models are difficult to estimate in practice because firms do not observe customers' consideration sets. Therefore, the common assumption in operations has been that customers consider everything on offer, so the offer set is the same as the consideration set. This raises the following question: when firms only collect transaction data, do CTC models offer any predictive advantage over the classic choice models? More precisely, under what conditions do CTC models outperform (if ever) classic choice models in terms of prediction accuracy?

In this work, we study a general class of CTC models. We propose techniques to estimate these models efficiently from sales transaction data. We then compare their performance against the classic approach. We find that CTC models outperform standard choice models when there is noise in the offer set information and the noise is asymmetric across the training and test offer sets, but otherwise offer no particular predictive advantage over the classic approach. We demonstrate the benefits of using the CTC models in real-world retail and online platform settings. In particular, we show that CTC models calibrated on transaction data are better at long-term and warehouse level sales forecasts. We also find that offer sets are difficult to accurately define in online platform settings because customers usually only consider a small subset of all the available listings in the platform. In fact, CTC models significantly outperform standard choice models in online platform settings.

Keywords: consider-then-choose, consideration set, choice model, car sharing, retail, assortment optimization

Suggested Citation

Jagabathula, Srikanth and Mitrofanov, Dmitry and Vulcano, Gustavo, Demand Estimation under Uncertain Consideration Sets (October 12, 2022). NYU Stern School of Business, Available at SSRN: https://ssrn.com/abstract=3410019 or http://dx.doi.org/10.2139/ssrn.3410019

Srikanth Jagabathula

New York University (NYU) - Department of Information, Operations, and Management Sciences ( email )

44 West Fourth Street
New York, NY 10012
United States

Dmitry Mitrofanov (Contact Author)

Boston College, Carroll School of Management

257 Beacon Street
Chestnut Hill, MA 02467
United States

Gustavo Vulcano

Universidad Torcuato Di Tella - School of Business ( email )

Avda Figueroa Alcorta 7350
Buenos Aires, CABA 1428
Argentina

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
386
Abstract Views
1,700
rank
113,572
PlumX Metrics