Integrating datasets: Segmenting the fash ion mar ket using risk aver sion

Martin Paul Block*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


A marketing segmentation can often be improved with the addition of variables which are often found on different datasets. Using a classification regression tree (CRT) methodology with predictor variables shared across datasets, the terminal node identification equations can be used to estimate the variables on a different dataset. The use of CRT allows the inclusion of categorical variables, such as marital status and ethnicity, as well as continuous variables, such as age and education. Three datasets were integrated and a chi-square automatic interaction detector (CHAID) tree is then used to segment the women’s clothing fashion market by demographic and reward and aversion variables. The analysis suggests possible marketing strategies targeting high-spending segments as well as media strategies.

Original languageEnglish (US)
Pages (from-to)302-313
Number of pages12
JournalApplied Marketing Analytics
Issue number3
StatePublished - Dec 1 2022


  • data integration
  • decision tree
  • fashion
  • segmentation

ASJC Scopus subject areas

  • Strategy and Management
  • Statistics, Probability and Uncertainty
  • Marketing


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