Semantic Customers’ Segmentation

2019 ◽  
pp. 318-325 ◽  
Author(s):  
Jocelyn Poncelet ◽  
Pierre-Antoine Jean ◽  
François Trousset ◽  
Jacky Montmain
Keyword(s):  
Customers Segmentation

Customer Behavior Prediction using K-Means Clustering Algorithm

2016 ◽  
pp. 256-267 ◽  
Author(s):  
Juhi Singh ◽  
Mandeep Mittal ◽  
Sarla Pareek
Keyword(s):  
Association Rules ◽  
Clustering Algorithm ◽  
A Priori ◽  
Clustering Algorithms ◽  
Customer Behavior ◽  
Purchase Behavior ◽  
Behavior Prediction ◽  
Customer Data ◽  
Customers Segmentation ◽  
Insight Into

Due to the increased availability of individual customer data, it is possible to predict customer buying pattern. Customers can be segmented using clustering algorithms based on various parameters such as Frequency, Recency and Monetary values (RFM). The data can further be analyzed to infer rules among two or more purchases of the customer. In this chapter we will present a clustering algorithm, enhanced k- means algorithm, which is based on k- means algorithm to divide customers into various segments. After segmentation, each segment is mined with the help of a priori algorithm to infer rules so that the customer's purchase behavior can be predicted. From large number of association rules with sufficient coverage, the customer's purchasing pattern can be predicted. Experiment on real database is implemented to evaluate the performance on effectiveness and utility of the approach. The results show that the proposed approach can gain a well insight into customers' segmentation and thus their behavior can be predicted.

The Research of Improved Apriori Mining Algorithm in Bank Customer Segmentation

2013 ◽  
Vol 760-762 ◽  
pp. 2244-2249 ◽  
Author(s):  
Gong Xin Yang
Keyword(s):  
Customer Service ◽  
Customer Segmentation ◽  
Decision Makers ◽  
Mining Algorithm ◽  
Optimal Value ◽  
Segmentation Accuracy ◽  
Traditional Algorithm ◽  
Customer Classification ◽  
Customers Segmentation ◽  
Service Strategies

The This paper studies bank customers segmentation problem. Improved Apriori mining algorithm is a kind of data mining technology which is an important method in bank customers segmentation. In practical application, the traditional algorithm has shortcomings of the initial values sensitive and easy to fall into local optimal value, which will lead to low accuracy rate of silver class customer classification. According to the shortcomings of traditional algorithm, this paper puts forward a bank customer segmentation method based on improved Apriori mining algorithm in order to improve the bank customer segmentation accuracy. Experimental results show that the algorithm can effectively overcome the traditional algorithms shortcomings of easy to fall into local optimal value, improve the customer classification accuracy, make mining results more reasonable, lay down different customer service strategies for different client base, improve effective reference opinions of bank decision makers, and bring more benefits for the bank.

Analysis of Communication Enterprises' Customers Segmentation Based on Data Mining

2014 ◽  
Vol 543-547 ◽  
pp. 4464-4467
Author(s):  
Jing Zhao ◽  
Nan Wei ◽  
Hui Li ◽  
Yue Ling Liu
Keyword(s):  
College Students ◽  
Data Mining ◽  
Behavioral Characteristics ◽  
Mobile Service ◽  
Market Shares ◽  
Product Service ◽  
Consumption Data ◽  
Cluster Analysis Method ◽  
Customers Segmentation ◽  
Product Service Systems

Application of data mining in mobile communication enterprises can help the enterprises conduct customers subdivision, understand characteristics of consumer behavior and develop appropriate product service systems based on different subdivided groups to occupy more market shares and provide better services for customers. By applying cluster analysis method in data mining and using k-means algorithm, this paper analyzes the collected mobile service consumption data with college students as samples, concludes behavioral characteristics of the mobile service consumption of three type college students and makes proposals from the perspective of operators.

scholarly journals The strategic marketing planning – General Framework for Customer Segmentation

10.26458/1416 ◽  
2014 ◽  
Vol 14 (1) ◽  
pp. 49
Author(s):  
Alina Elena OPRESCU
Keyword(s):  
Strategic Planning ◽  
General Framework ◽  
Business Environment ◽  
Customer Segmentation ◽  
Strategic Marketing ◽  
Sequential Process ◽  
Marketing Activity ◽  
Strategic Resources ◽  
Marketing Planning ◽  
Customers Segmentation

Any approach that involves the use of strategic resources of an organisation requires a responsible approach, a behaviour that enables it to properly integrate itself into the dynamic of the business environment. This articles addresses in a synthetic manner, the issues of specific integration efforts for customers’ segmentation in the strategic marketing planning. The essential activity for any organisation wishing to optimise its response to the market, the customer segmentation will fully benefit from the framework provided by the strategic marketing planning. Being a sequential process, it not only allows time optimisation of the entire marketing activity but it also leads to accuracy of the strategic planning and its stages.    

The Success behind the World’s Best Airport: The Rise of the Changi

2021 ◽  
Vol 15 (1) ◽  
pp. 250905
Author(s):  
Thanavutd Chutiphongdech ◽  
Rugphong Vongsaroj
Keyword(s):  
Business Model ◽  
Descriptive Analysis ◽  
Lessons Learned ◽  
Strategic Partnerships ◽  
International Airport ◽  
Business Model Canvas ◽  
Business Operations ◽  
Value Propositions ◽  
Key Resources ◽  
Customers Segmentation

Singapore Changi International Airport received the World's Best Airport from Skytrax. This was the 8th consecutive year that Changi won this renowned award. This paper aims to investigate Changi’s overall business operations to track its accomplishments in airport development. Examining the lessons learned by using a descriptive analysis under the Business Model Canvas (BMC) framework, the results reveal that Singapore Changi International Airport is a destination in itself. This concept affects customers' segmentation, which links to different value propositions deriving from using key resources and synergy among strategic partnerships. This paper also suggests that sustainability should be added to the BMC framework since it is another component behind the airport’s success. The practical contributions deriving from the lessons learned are presented.

A Fuzzy ANP Based Weighted RFM Model for Customer Segmentation in Auto Insurance Sector

2015 ◽  
Vol 7 (2) ◽  
pp. 71-86 ◽  
Author(s):  
Ahad Zare Ravasan ◽  
Taha Mansouri
Keyword(s):  
Analytic Network Process ◽  
Customer Segmentation ◽  
Insurance Company ◽  
Auto Insurance ◽  
Insurance Sector ◽  
Rfm Model ◽  
Network Process ◽  
Hidden Knowledge ◽  
Value Model ◽  
Customers Segmentation

Data mining has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the raw data. This study has proposed a brand new and practical fuzzy analytic network process (FANP) based weighted RFM (Recency, Frequency, Monetary value) model for application in K-means algorithm for auto insurance customers' segmentation. The developed methodology has been implemented for a private auto insurance company in Iran which classified customers into four “best”, “new”, “risky”, and “uncertain” patterns. Then, association rules among auto insurance services in two most valuable customer segments including “best” and “risky” patterns are discovered and proposed. Finally, some marketing strategies based on the research results are proposed. The authors believe the result of this paper can provide a noticeable capability to the insurer company in order to assess its customers' loyalty in marketing strategy.

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