Purpose – The purpose of this paper is to determine and characterize groups of retail customers, based on their perception of commitment to the retailer and the degree of use of its technological equipment.Design/methodology/approach – A CHAID algorithm is performed and differences between the resulting segments are tested through the analysis of variance.Findings – Four segments are obtained that differ significantly in commitment to the retailer and level of use of the store's information and communication technology (ICT) facilities, as well as in other variables related to the retailer‐consumer relationship. In particular, customer segments differ in their use of retailers' payment facilities.Practical implications – The study provides a typology of consumers that enable retailers to take efficient decisions regarding their technology investments and to design effective marketing strategies.Originality/value – Although literature concerning interorganizational relationships has reported that commitmen...
Now a day's commercial marketing growth is improved by customer segmentation model. Literatures use the data mining technology to review the customer segmentation and sound effectives. Stages of CRM have been used in most of the cases. Based on RFM, demographic and LTV data the paper is prepared using the data mining tools for the new customer segmentation. Customer segmentation has been done by RFM and LTV method. By using K-means clustering and demographic data, the customers are clustered into different categories. LTV profile of the customer is finally ready. This method has been followed by various other industries too.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.