Automatic Customer Segmentation
for Social CRM Systems
Adam Czyszczoń and Aleksander Zgrzywa
Politechnika Wrocławska, Faculty of Computer Science and Management, Institute of Informatics,
Wybrzeże Wyspiańskiego 27, 50370 Wrocław, Poland
Abstract. This paper attempts to address the problem of the automatic customer segmentation by processing data collected in Social Customer Relationship Management (Social CRM) systems using Kohonen networks. Presented segmentation approach comprises classic loyaltyproﬁtability link model that is explicit for CRM, and new social media components direct to Social CRM. The result of presented approach is an analysis tool with data visualization for managers which signiﬁcantly improves the process of customer segmentation. Presented research is supported by implementation of proposed approach by which experiments were conducted. Additionally, the experimental results showed that proposed method performed very close to k-means algorithm which indicate the correctness of the proposed approach.
Keywords: customer segmentation, CRM, Social CRM, clusterization, SOM, unsupervised learning, ANN, data mining.
To acquire competitive advantage many companies use the strategy of Customer Relationship Management (CRM) what can be observed in growing interest in this domain. However, in recent years new element of strategic importance appeared called social media. In order to meet the changing expectations of customers needs, Social CRM (SCRM) systems represent new branch of CRM systems which is oriented on the use of social media.
With the emergence of a new family of CRM systems, there has arisen the need for developing tools supporting these systems. Although both CRM and SCRM systems have many analytical tools, still a lot of them impose the necessity of extensive data management and using external software packages . This in turn causes that many analyses are carried out semi-automatically or even manually. Such a situation results in not only a loss of valuable time, but also a lack of focus on the most important components of customer relationship management systems and their beneﬁts. This comes down to the fact that the management is unable to keep up with the rapidly changing customers trends, particularly in the area of social networks.
A. Kwiecień, P. Gaj, and P. Stera (Eds.): CN 2013, CCIS 370, pp. 552–561, 2013. c Springer-Verlag Berlin Heidelberg 2013
Automatic Customer Segmentation for Social CRM Systems
The aim of this work is to propose an approach to solve the problem of automatic segmentation of customers in the SCRM systems. The purpose of the method is to support the CRM strategy by providing applicable tools of data analysis for managerial staﬀ.
Presented segmentation approach is based on well-known model, linking customer proﬁtability and loyalty, which are also the two most important components of CRM strategy. Moreover, the presented approach has been extended to include elements related to social media, which are crucial to SCRM systems. It was also assumed that each of the main segmentation components can be composed of many features. In addition, an adequate representation of analyzed data that provides management with clear results in form of diagrams is required. Therefore, for customer segmentation the Self Organizing Maps (SOM) algorithm is proposed which is commonly used for clusterization and visualization of high-dimensional data.
This paper is a continuation to our research on intelligent tools supporting CRM systems using their information potential. The research presented in  includes deﬁnitions of some indicators, which were also used in this paper. This includes RFM (Recency Frequency Money), LTV (customer LifeTime Value), and NPP (Next Purchase Probability) used for customer loyalty estimation. The theoretical...
References: 1. Tsiptsis, K., Chorianopoulos, A.: Data Mining Techniques in CRM: Inside Customer Segmentation. Wiley Publishing (2010)
prediction in social CRM systems. Computer Science 13(4) (2012)
Wiley InterScience (2005)
approach. Decis. Support Syst. 37(2), 215–228 (2004)
rules from imbalanced data. J. Intell. Inf. Syst. 39(2), 335–373 (2012)
on business intelligence tools. Expert Syst. Appl. 29(1), 145–152 (2005)
and k-means algorithm for market segmentation. Comput. Oper. Res. 29(11),
Academy and Society, WSEAS (2006)
13. Wagner, S., Wagner, D.: Comparing Clusterings – An Overview. Technical Report
2006-04, Universität Karlsruhe (TH) (2007)
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