Determination of Customer Satisfaction using Improved K-means algorithm

Effective management of customer’s knowledge leads to efficient Customer Relationship Management (CRM). To accurately predict customer’s behaviour, clustering, especially K -means, is one of the most important data mining techniques used in customer relationship management marketing, with which it i...

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Vydáno v:Soft computing (Berlin, Germany) Ročník 24; číslo 22; s. 16947 - 16965
Hlavní autoři: Zare, Hamed, Emadi, Sima
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2020
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Brand loyalty
Cluster analysis
Clustering
Computational Intelligence
Control
Customer relationship management
Customer satisfaction
Customer services
Customers
Data mining
Datasets
Engineering
Indicators
Marketing
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Methods
Optimization algorithms
Robotics
Vector quantization
means
Customer life cycle value
Data mining
Customer satisfaction
Customer relationship management
ISSN:1432-7643, 1433-7479
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Shrnutí:Effective management of customer’s knowledge leads to efficient Customer Relationship Management (CRM). To accurately predict customer’s behaviour, clustering, especially K -means, is one of the most important data mining techniques used in customer relationship management marketing, with which it is possible to identify customers’ behavioural patterns and, subsequently, to align marketing strategies with customer preferences so as to maintain the customers. However, it has been observed in various studies on K -means clustering that customers with different behavioural indicators in clustering may seem to be the same, implying that customer behavioural indicators do not play any significant role in customer clustering. Therefore, if the level of customer participation depends on behavioural parameters such as their satisfaction, it can have a negative effect on the K -means clusters and has no acceptable result. In this paper, customer behavioural features—malicious feature—is considered in customer clustering, as well as a method for finding the optimal number of clusters and the initial values of cluster centres to obtain more accurate results. Finally, according to the organizations’ need to extract knowledge from customers’ views through ranking customers based on factors affecting customer value, a method is proposed for modelling their behaviour and extracting knowledge for customer relationship management. The results of the evaluation of the customers of Hamkaran System’s Company show that the improved K -means method proposed in this paper outperforms K -means in terms of speed and accuracy.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-020-04988-4