Enhancing Customer Segmentation: A Novel Approach using Machine Learning Algorithms

Customer segmentation is needed for every
business to survive in the field. Grouping up of customers can
be done by finding a similar pattern among customers by
analyzing the sales dataset. This research introduces a novel
methodology designed to enhance customer segmentation
significantly, achieved through the integration of cutting-edge
machine learning algorithms, explicitly focusing on clustering
and classification techniques, to overcome the inherent
limitations and static nature of traditional segmentation
methods. The proposed approach uses the well-established
RFM model for feature engineering, enabling a comprehensive
representation of customer behaviour and value, subsequently
employing advanced clustering algorithms to identify distinct
customer segments based on these RFM features. To further
refine the segmentation, a weighted lifetime value calculation
is introduced, serving as a critical classification criterion
within each identified cluster, thereby facilitating a more
nuanced understanding of customer potential by integrating AI
techniques with RFM analysis, the identification of sustainable
consumer behaviours is improved, offering more granular
insights into consumer behaviour and its impact on
sustainability.