Measuring customer loyalty using an extended RFM and clustering technique

Customer Clustering using RFM analysis

RFM (Recency, Frequency, Monetary) analysis is a method to identify high-response customers in marketing promotions, and to improve overall response rates, which is well known and is widely applied today. Less widely understood is the value of applying RFM scoring to a customer database and measuring customer profitability. RFM analysis is considered significant also for the banks and their spe...

Customer Data Clustering using Data Mining Technique

Classification and patterns extraction from customer data is very important for business support and decision making. Timely identification of newly emerging trends is very important in business process. Large companies are having huge volume of data but starving for knowledge. To overcome the organization current issue, the new breed of technique is required that has intelligence and capabilit...

Customer Loyalty

The purpose of the research is to find out the impact of economic recession on customer loyalty to banks in the UK. This aim has been reached using methods of statistical analysis and through investigation of primary and secondary data. The methods of analysis that were implemented in the research included regression and correlation analysis of variance as well as chi-square tests. The data has...

Customer Loyalty

Measuring and Predicting Customer Lifetime Value in Customer Loyalty Analysis: A Knowledge Management Perspective (A Case Study on an e-Retailer)

Modern business organizations have appreciated the significance of having competitive advantage through the delivery of continuous improvement towards the customers, and being knowledge-oriented. Indisputably, Knowledge Management (KM) plays a key role in the success of Customer Relationship Management (CRM). In this regard, Customer Knowledge Management (CKM) is a newly developed concept that ...