Summary of An Exploration Of Clustering Algorithms For Customer Segmentation in the Uk Retail Market, by Jeen Mary John et al.
An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Market
by Jeen Mary John, Olamilekan Shobayo, Bayode Ogunleye
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Applications (stat.AP); Computation (stat.CO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the pressing need for more accurate and efficient customer segmentation in the retail industry, driven by the rise of online purchases and the resulting high volume of customer data. The authors propose a customer segmentation model to improve decision-making processes, leveraging a UK-based online retail dataset with 541,909 customer records and eight features. They adopt the RFM framework to quantify customer values and compare various clustering algorithms, including K-means, GMM, DBSCAN, agglomerative clustering, and BIRCH. The results show that GMM outperforms other approaches, achieving a Silhouette Score of 0.80. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how businesses can better group their customers based on their buying habits. Imagine you’re shopping online and you get personalized ads or recommendations because the website knows what you like to buy. This happens because companies are trying to figure out which customers are similar and which ones are different. The authors use a big dataset of customer information from the UK to test different methods for grouping customers, called clustering algorithms. They find that one method, called GMM, does the best job of grouping customers based on how often they buy, what they buy, and when they buy it. |
Keywords
* Artificial intelligence * Clustering * K means
