An intelligent system for customer targeting: a data mining approach
Decision Support Systems
Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms
Management Science
Market Segmentation Within Consolidated E-Markets: A Generalized Combinatorial Auction Approach
Journal of Management Information Systems
Segmentation of stock trading customers according to potential value
Expert Systems with Applications: An International Journal
Visualization method for customer targeting using customer map
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Identifying influential reviewers for word-of-mouth marketing
Electronic Commerce Research and Applications
Customer portfolio analysis using the SOM
International Journal of Business Information Systems
Customer grouping for better resources allocation using GA based clustering technique
Expert Systems with Applications: An International Journal
Dynamic classifier ensemble model for customer classification with imbalanced class distribution
Expert Systems with Applications: An International Journal
Using context to improve the effectiveness of segmentation and targeting in e-commerce
Expert Systems with Applications: An International Journal
Review: Soft computing applications in customer segmentation: State-of-art review and critique
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Most marketers have difficulty in identifying the right customers to engage in successful campaigns. So far, customer segmentation is a popular method that is used for selecting appropriate customers for a launch campaign. Unfortunately, the link between customer segmentation and marketing campaign is missing. Another problem is that database marketers generally use different models to conduct customer segmentation and customer targeting. This study presents a novel approach that combines customer targeting and customer segmentation for campaign strategies. This investigation identifies customer behavior using a recency, frequency and monetary (RFM) model and then uses a customer life time value (LTV) model to evaluate proposed segmented customers. Additionally, this work proposes using generic algorithm (GA) to select more appropriate customers for each campaign strategy. To demonstrate the efficiency of the proposed method, this work performs an empirical study of a Nissan automobile retailer to segment over 4000 customers. The experimental results demonstrate that the proposed method can more effectively target valuable customers than random selection.