With competitive pressure growing in online markets, many Internet stores provide various customer aid functions such as personalized pages to help customers shop more effectively and efficiently. Evaluating such customer aid functions is usually costly because it requires full or partly-working systems and many human testers. In order to address this problem, this research presents a novel app...

RFM stands for Recency, Frequency and Monetary value. RFM analysis is a marketing technique used for analyzing customer behavior such as how recently a customer has purchased (recency), how often the customer purchases (frequency), and how much the customer spends (monetary). It is a useful method to improve customer segmentation by dividing customers into various groups for future personalizat...

I is well known that transaction-specific investments (TSIs) made in customers by account managers makes them vulnerable to opportunism by customers (i.e., the targets of the investments). The present research shows that TSIs made in customers by account managers can also lead them to be concerned about internal opportunism by nontargets of the investments (e.g., information technology or finan...

This study aims to investigate the impact of desired self-identity, green perceived value and altruistic values on brand loyalty towards brands (i.e., electric hybrid cars) mediating role customer engagement behavior these relationships. Further, this proposes that greenwashing perception, which can be defined as consumers perceiving organizations dishonest about their environmental claims, mod...

In today’s competitive climate, customer relationship management (CRM) has become an essential component in airline business strategies. CRM in the airline industry would be based on analyzing customer data in order to understand preferences and behavior. In this paper, we apply data mining techniques to real airline frequent flyer data in order to derive CRM recommendations and strategies. Clu...

Recent sensor technologies have enabled the capture of users’ behavior data. Given the large amount of data currently available from sensor-equipped environments, it is important to attempt characterization of the sensor data for automatically modeling users in a ubiquitous and mobile computing environment. As described herein, we propose a method that predicts a customer model using features b...

Available online 5 May 2011