Rough Set Application in Customer Classification

2011 ◽  
Vol 267 ◽  
pp. 46-49 ◽  
Author(s):  
Ju Li ◽  
Wen Bin Xu ◽  
Wei Yuan Tu ◽  
Xing Wang ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Decision Making ◽  
Rough Set ◽  
Customer Relationship Management ◽  
Decision Rules ◽  
Relationship Management ◽  
Experimental Results ◽  
Customer Relationship ◽  
Decision Table ◽  
Final Decision ◽  
Customer Classification

Based on the study of customer relationship management. First, we got the data from the database, transformed the corresponding decision table, then got the data in decision-making table for further simplification, generated the final decision rules. and got good results, experimental results showed that the method provided some practical value.

Customer Relationship Management at Harrah's Entertainment

2011 ◽  
pp. 157-172 ◽  
Author(s):  
Hugh J. Watson ◽  
Linda Volonino
Keyword(s):  
Decision Making ◽  
Customer Relationship Management ◽  
Business Strategy ◽  
Data Warehousing ◽  
Relationship Management ◽  
Leadership Role ◽  
Customer Relationship ◽  
Gaming Industry ◽  
New Markets ◽  
Specific Target

Data warehousing has significantly changed how decision making is supported in organizations. A leading application of data warehousing is customer relationship management (CRM). The power of CRM is illustrated by the experiences at Harrah’s Entertainment, which has assumed a leadership role in the gaming industry through a business strategy that focuses on knowing their customers well, giving them great service, and rewarding their loyalty so that they seek out a Harrah’s casino whenever and wherever they play. In 1993, changing gaming laws allowed Harrah’s to expand into new markets through the building of new properties and the acquisition of other casinos. As management thought about how it could create the greatest value for its shareholders, it was decided that a brand approach should be taken. With this approach, the various casinos would operate in an integrated manner rather than as separate properties. Critical to their strategy was the need to understand and manage relationships with their customers. Harrah’s had to understand where their customers gamed, how often and what games they played, how much they gambled, their profitability, and what offers would entice them to visit a Harrah’s casino. Armed with this information, Harrah’s could better identify specific target customer segments, respond to customers’ preferences, and maximize profitability across the various casinos.

ENTERPRISE CUSTOMER RELATIONSHIP MANAGEMENT BASED ON BIG DATA MINING

2018 ◽  
Vol 48 (3) ◽  
pp. 163-168
Author(s):  
X. T. LI ◽  
F. FENG
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Potential Core ◽  
Big Data Mining ◽  
Customer Classification ◽  
Classification Index

Based on the customer relationship management in the context of big data, focusing on B2C e-commerce companies, this paper constructs a customer classification index system, uses a factor analysis and Bagging model to study the sales data of an e-commerce business, and demonstrates the specific operation of customer relationship management under the background of big data. This paper finds that through the classification of past consumer behavior data, managers can distinguish between potential, core, and lost customers. The bagging model can predict the type of customer and guide the administrator to perform differentiated customer relationship management.

scholarly journals The study of customer relationship management in Thai airline industry: A case of Thai travelers in Thailand

2017 ◽  
Vol 7 (1) ◽  
pp. 13 ◽  
Author(s):  
Colin Law
Keyword(s):  
Customer Relationship Management ◽  
Airline Industry ◽  
Distribution Channel ◽  
Relationship Management ◽  
Sampling Technique ◽  
Customer Relationship ◽  
Final Decision ◽  
Loyalty Program ◽  
Customer Preference ◽  
Customer Services

Purpose: This paper offer marketing strategy suggestion to the airlines operating within the Thai aviation market. It identifies the recommended motivational factors that influence the airline customers’ decision to their airline choices. Airlines use different customer relationship management programs to attract returning customers.  This paper suggested the most attractive motivation factors for Thailand's air travel market.Design/methodology/approach: This research paper is an attempt to study and identify the factors, including loyalty program, distribution channel, customer services, promotions and other influence causes that affected the customer preference in the airline ticket purchasing behavior in Thailand.A questionnaire survey was conducted with the sample identified through unrestricted non probability sampling technique at four major airports in Thailand. The data collected are analyzed to identify the favorable drivers that lead to customer decision on airline choice.Findings: The result from the study has demonstrated that price, and promotion has a significant impact on customer preference and positively leads to repurchase intention for their future travel. Moreover, flight schedules are also a main factor influencing the travelers’ final decision on airline choice. However, when the promotional strategies and schedule are comparable between airlines, customers are attracted by the airline amenities and services. Loyalty program (frequent-flier program flier program) is showing a less attractive motivator while distribution channel demonstrates the least important affecting the travelers’ choice of airline.Originality/value: The paper begins with an overview of previous research in the area of airlines customer relationship management and then moves on to what is currently being implemented by the airlines. The authors then propose several customer relationship strategies and identify the most attractive one that motivates the Thai consumers.

AN INTELLIGENT CLASSIFICATION METHOD IN BANK CUSTOMER RELATIONSHIP MANAGEMENT

2007 ◽  
Vol 03 (01) ◽  
pp. 111-121
Author(s):  
JIE ZHANG ◽  
JIE LU ◽  
GUANGQUAN ZHANG
Keyword(s):  
Customer Relationship Management ◽  
Hybrid Genetic Algorithm ◽  
Relationship Management ◽  
Comprehensive Analysis ◽  
Customer Segmentation ◽  
Customer Relationship ◽  
Classification Method ◽  
Static Characteristics ◽  
Static Data ◽  
Customer Classification

Customer classification is one of the major tasks in customer relationship management. Customers often have both static characteristics and dynamic behavioral features. Using both kinds of data to conduct comprehensive analysis can enhance the reasonability of customer classification. In the proposed classification method, customer dynamic data is clustered using a hybrid genetic algorithm. The result is then combined with customer static data to give reasonable customer segmentation supported by neural network technique. A bank dataset-based experiment shows that applying the proposed method can obviously improve the accuracy of customer classification comparing with the traditional methods where only static data is used.

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