Improving customer segmentation via classification of key accounts as outliers

Jan Michael Spoor ()
Additional contact information
Jan Michael Spoor: Karlsruhe Institute of Technology

Journal of Marketing Analytics, 2023, vol. 11, issue 4, No 16, 747-760

Abstract: Abstract Customer segmentation and key account management are important use cases for clustering algorithms. Here, a data set of a Portuguese wholesaler for food and household supplies is used as an exemplary application. To increase the quality of the analysis, a two-stage approach is proposed. First, key accounts are filtered by a density-based outlier detection. Second, a Gaussian Mixture Model (GMM) is applied to cluster smaller customers. This two-stage approach is aligned with the business implications of key accounts as outstanding and very differently behaving customers as well as with the core idea of an ABC analysis. Also, the exclusion of key accounts corresponds to the definition of outliers as the results of a different underlying mechanism. Using this two-stage approach shows better clustering results compared to using a one-stage approach applying only a GMM. Therefore, it is concluded that density-based detection of key accounts followed by a clustering using a GMM is beneficial for customer segmentation within B2B applications.

Keywords: Clustering algorithms; Anomaly detection; Customer segmentation; Marketing management (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/s41270-022-00185-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jmarka:v:11:y:2023:i:4:d:10.1057_s41270-022-00185-4

Ordering information: This journal article can be ordered from
http://www.springer. ... gement/journal/41270

DOI: 10.1057/s41270-022-00185-4

Access Statistics for this article

Journal of Marketing Analytics is currently edited by Maria Petrescu and Anjala Krishnen

More articles in Journal of Marketing Analytics from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().