Abstract
Purpose: The purpose of this research paper is to segment the best contributed retail management customers of a super store and corresponding least contributed customers for a given transaction period based on the customer purchase behavior.
Design/Methodology/Approach: Theoretically Market segmentation is determined based on geographical segmentation and RFM analysis is adopted to be implemented for obtaining customer segmentation. Data mining technique called clustering technique is used to implement RFM analysis in order to identify the highly profitable, high-valued customers and low-risk customers. The secondary file containing secondary data is obtained from the source web site called Github.
Findings:(i) Segmenting customers based on their geographical residence is easy to work using Python (ii) segmenting customers based on their purchase behavior patterns of grocery items is easy to find using Python programming kind of innovative information technologies with visualization. Though there are prior researches conducted using Python libraries, however our approach is an effort of reducing the gap between the theoretically coding the RFM model and its close implementation in Python based on the same RFM coding.
Research Limitations/Implications: The development of modern patterns of grocery stores data analysis is limited to the case of Britain and has been described and analyzed through Python programming implementation. Though it is not possible to obtain patterns for all countries globally, however it is possible to obtain such patterns of some of the leading countries like UAE, Saudi Arabia and some countries from Europe etc.
Practical Implications: Customer Segmentation through RFM analysis technique implementation in Python provides an opportunity to analyze not only the Britain region but also for different countries is possible. RFM analysis technique that is mentioned theoretically can be well programmed with possible visualization using Python coding and programming libraries. But one must be familiar with having awareness of how to code in Python programming in order to work towards obtaining geographical grouping and customer grouping using visualization techniques through graphs and charts.
Social Implications: It helps grocery stores management in identifying profitable customers and thus can campaign various customer profitable offerings as a part of loyalty programs. Also, it is possible to focus on planning for customer retention programs.
Originality/Value: The value of this research paper lies in realizing through mapping the theoretical value of RFM analysis to practically visualizing the customer segmentation in the form of grouping best customers and low-risk customers using Python implementation. Such value can be found in following the research methodology steps while developing the content of the paper as well as showing practically obtaining results through dealing with data-based case study as a part of serving itself as a testing the proposed hypotheses.