Machine learning methods for targeting and new product development

Chapter 1: Market research traditionally relies on interviews and focus groups to identify customer needs. User-generated content (UGC), such as online reviews, social media, and call-center data, provides an opportunity to identify customer needs more efficiently. Established methods are not well-suited for large UGC datasets because much of the content is uninformative or repetitive. We propose a machine learning approach for identifying customer needs from UGC and evaluate the method using a new dataset. Once identified, the needs can be used to inform marketing strategy, brand positioning and new product development. Chapter 2: Targeting policies are used in marketing to match different firm actions to different customers. For example, retailers want to send different promotions to different customers, real estate agents want to show different homes, and car dealers want to propose different prices.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Sloan School of Management

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Timoshenko, Artem.

Advisor dc:contributor.advisor

• John R. Hauser and Duncan Simester.

• Sloan School of Management.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

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/1721.1/123572

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123572