Retail analytics: Market segmentation through transaction data

Cheng Bang Chen, Deepak Agrawal, Soundar Kumara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


In recent years, the growing competition has motivated companies to find out useful data mining methods to convert massive data into valuable information. How to retrieve meaningful messages from data effectively and efficiently is always the key. Market segmentation is the key to better marketing and customer relationship management. In this research, we propose an effective method to retrieve consumer shopping behavior patterns from the real transaction data. We first transform the transaction data into an easy to represent format that can be used to efficiently represent dynamic customer behaviors; subsequently powerful hierarchical and non-hierarchical clustering algorithms are applied to segregate the overall customer population. These clusters consist of shopping behaviors which are significantly different from each other. These results can be used to better target the customers and develop better promotional strategies.

Original languageEnglish (US)
Title of host publicationIIE Annual Conference and Expo 2015
PublisherInstitute of Industrial Engineers
Number of pages9
ISBN (Electronic)9780983762447
StatePublished - 2015
EventIIE Annual Conference and Expo 2015 - Nashville, United States
Duration: May 30 2015Jun 2 2015

Publication series

NameIIE Annual Conference and Expo 2015


OtherIIE Annual Conference and Expo 2015
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Retail analytics: Market segmentation through transaction data'. Together they form a unique fingerprint.

Cite this