Data-driven decision making in online and offline retail/

.Retail operations have experienced a transformational change in the past decade with the advent and adoption of data-driven approaches to drive decision making. Granular data collection has enabled firms to make personalized decisions that improve customer experience and maintain long-term engagement. In this thesis we discuss important problems that retailers face in practice, before, while and after a product is introduced in the market. In Chapter 2, we consider the problem of estimating sales for a new product before retailers release the product to the customer. We introduce a joint clustering and regression method that jointly clusters existing products based on their features as well as their sales patterns while estimating their demand. Further, we use this information to predict demand for new products. Analytically, we show an out-of-sample prediction error bound.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Singhvi, Divya.

Advisor dc:contributor.advisor

• Georgia Perakis.

• Operations Research Center.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/129361

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/129361