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Characterising Online Purchasing Behaviour

EasyChair Preprint no. 10460

6 pagesDate: June 28, 2023


This paper suggests a system that classifies customers based on their purchase behaviour and examines their patterns using supervised and unsupervised learning techniques. The technology analyses consumer behaviour at the session and user journey levels, anticipating consumer behaviour with excellent accuracy and recall rates. Five main client clusters with different behaviour patterns were identified by the study: "new customers," "active shoppers," "returning decisive shoppers," "comparison shoppers," and "window shoppers." These patterns can offer insightful information for developing personalised marketing tactics, enhancing the entire consumer experience, boosting client retention, and promoting business expansion. The study emphasises the value of client segmentation and its possible commercial advantages.

Keyphrases: AutoML, Clustering, machine learning, Random Forest, supervised learning, unsupervised learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ganesh Gurram and Nikhil Reddy Kodumuru and Mukesh Tripathi},
  title = {Characterising Online Purchasing Behaviour},
  howpublished = {EasyChair Preprint no. 10460},

  year = {EasyChair, 2023}}
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