Automated Market Analysis by RFMx Encoding Based Customer Segmentation using Initial Centroid Selection Optimized K-means Clustering Algorithm

Market analysis including customer segmentation
is one of the most important approaches utilized by business
owners to analyze customer behavior. Such analysis can
provide significant insights and decision support for
businesses. Multiple research effort was conducted for
market analysis including the Recency, Frequency and
Monetary analysis (RFM) in addition to many variations
including RFD, RFE, RFM-I and RFMTC. In this research a
methodology is proposed to utilize the intermediate vector
representation of the introduced RFMx for machine learning
toward high precision automatic customer segmentation. In
this methodology there’s no need to calculate the actual final
RFMx score. The RFMx technique introduces a multimonetary model where each monetary value is assigned
different weight to suite the business targets of business
owners. The proposed model allowed for finely tuned market
analyses on product type or service type level. The results
showed significant clustering results that lead to automatic
customer segmentation without the need to calculate the final
RFMx score.