Shopping hard or hardly shopping: Revealing consumer segments using clickstream data

Research output: Contribution to journalArticlepeer-review

Abstract

The recent rise of big data analytics is transforming the apparel retailing industry. E-retailers, for example, effectively use large volumes of generated as a result of their day-to-day business operations data to aid operations and supply chain management.Although logs of how consumers navigate through an e-commerce website are readily available in a form of clickstream data, clickstream analysis is rarely used to derive insights that can support marketing decisions, leaving it an under-researched area of study. Adding to this research stream by exploring the case of a UK-based fast-fashion retailer, this study reveals unique consumer segments and links them to the revenue they are capable of generating. Applying the partitioning around medoids algorithm to three random samples of 10,000 unique consumer visits to the e-commerce site of a fast-fashion retailer, six consumer segments are identified. This study shows that although the ‘Mobile Window Shoppers’segment consists of the largest consumer segment it attracts the lowest revenue. In contrast,‘Visitors with a Purpose’, although one of the smallest segments, generates the highest revenue. The findings of this research contribute to marketing research and inform practice,which can use these insights to target customer segments in a more tailored fashion.
Original languageEnglish
Number of pages12
JournalIEEE Transactions on Engineering Management
Early online date4 May 2021
DOIs
Publication statusE-pub ahead of print - 4 May 2021

Keywords

  • apparel retailing
  • big data
  • clickstream data
  • consumer segmentation
  • online purchase
  • business
  • machine learning
  • navigation
  • market research
  • industries
  • tools

Fingerprint

Dive into the research topics of 'Shopping hard or hardly shopping: Revealing consumer segments using clickstream data'. Together they form a unique fingerprint.

Cite this