Search published articles


Showing 2 results for Customer Lifetime Value (clv)
Measuring and Predicting Customer Lifetime Value in Customer Loyalty Analysis: A Knowledge Management Perspective (A Case Study on an e-Retailer)
A. Aghaie,
Volume 20, Issue 1 (5-2009)
Abstract
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 deals with knowledge from customers rather than knowledge about customers. However, little research has been done on the application of CKM in e-business. In this paper, after an overview of the literature, an application of CKM in Customer Lifetime Value (CLV) measurement is studied in an e-retailer case where Corporate Image and Reputation are taken into consideration.
Combining data mining and group decision making in retailer segmentation based on LRFMP variables
Amin Parvaneh, Mohammadjafar Tarokh, Hossein Abbasimehr,
Volume 25, Issue 3 (7-2014)
Abstract
Data mining is a powerful tool for firms to extract knowledge from their customers’ transaction data. One of the useful applications of data mining is segmentation. Segmentation is an effective tool for managers to make right marketing strategies for right customer segments. In this study we have segmented retailers of a hygienic manufacture. Nowadays all manufactures do understand that for staying in the competitive market, they should set up an effective relationship with their retailers. We have proposed a LRFMP (relationship Length, Recency, Frequency, Monetary, and Potential) model for retailer segmentation. Ten retailer clusters have been obtained by applying K-means algorithm with K-optimum according Davies-Bouldin index on LRFMP variables. We have analyzed obtained clusters by weighted sum of LRFMP values, which the weight of each variable calculated by Analytic Hierarchy Process (AHP) technique. In addition we have analyzed each cluster in order to formulate segment-specific marketing actions for retailers. The results of this research can help marketing managers to gain deep insights about retailers.
Page 1 from 1