Application of K-Means Algorithm for Efficient Customer Segmentation: A Strategy for Targeted Customer Services

Chinedu Pascal Ezenkwu · Simeon Ozuomba · Constance Kalu

65 citations0/0 files storedINTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ARTIFICIAL INTELLIGENCE

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Customer churn and segmentation Advanced Clustering Algorithms Research Data Mining and Machine Learning Applications

Abstract

The emergence of many business competitors has engendered severe rivalries among competing businesses in gaining new customers and retaining old ones. Due to the preceding, the need for exceptional customer services becomes pertinent, notwithstanding the size of the business. Furthermore, the ability of any business to understand each of its customers’ needs will earn it greater leverage in providing targeted customer services and developing customised marketing programs for the customers. This understanding can be possible through systematic customer segmentation. Each segment comprises customers who share similar market characteristics. The ideas of Big data and machine learning have fuelled a terrific adoption of an automated approach to customer segmentation in preference to traditional market analyses that are often inefficient especially when the number of customers is too large. In this paper, the k-Means clustering algorithm is applied for this purpose. A MATLAB program of the k-Means algorithm was developed (available in the appendix) and the program is trained using a z-score normalised two-feature dataset of 100 training patterns acquired from a retail business. The features are the average amount of goods purchased by customer per month and the average number of customer visits per month. From the dataset, four customer clusters or segments were identified with 95% accuracy, and they were labeled: High-Buyers-Regular-Visitors (HBRV), High-Buyers-Irregular-Visitors (HBIV), Low-Buyers-Regular-Visitors (LBRV) and Low-Buyers-Irregular-Visitors (LBIV).

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 65 citations exist upstream.

References

2011Big data: The next frontier for innovation, competition, and productivity 2011The basis of market segmentation: a critical review of literature

Cited by

2021Customer Segmentation Using Machine Learning 2020Using Unsupervised Machine Learning Techniques for Behavioral-based Credit Card Users Segmentation in Africa 2022Customer Mobile Behavioral Segmentation and Analysis in Telecom Using Machine Learning 2021Geo-Marketing Segmentation with Deep Learning 2022Automatic and Adaptive Segmentation of Customer in R framework using K-means Clustering Technique 2021A Survey on Customer Segmentation using Machine Learning Algorithms to Find Prospective Clients 2017Survey of machine learning methods for big data applications 2025Discovering customer segments through interaction behaviors for home appliance business

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.