Customer satisfaction and loyalty analysis with classification algorithms and Structural Equation Modeling

The model proposed bridges the gap in post analysis of customer satisfaction (CS) and customer loyalty (CL) research.An integrated approach is presented using data mining and structural models together.We contribute to customer classification literature by adapting decision tree results to CS-CL matrix.CS-CL matrix is created by integrating group and criteria evaluation results using the advantage of structural models.A customer strategy development tool is offered according to matrix-based model outcomes. Businesses can maintain their effectiveness as long as they have satisfied and loyal customers. Customer relationship management provides significant advantages for companies especially in gaining competitiveness. In order to reach these objectives primarily companies need to identify and analyze their customers. In this respect, effective communication and commitment to customers and changing market conditions is of great importance to increase the level of satisfaction and loyalty. To evaluate this situation, level of customer satisfaction and loyalty should be measured correctly with a comprehensive approach. In this study, customers are investigated in 4 main groups according to their level of satisfaction and loyalty with a criteria and group based analysis with a new method. We use classification algorithms in WEKA programming software and Structural Equation Modeling (SEM) with LISREL tools together to analyze the effect of each satisfaction and loyalty criteria in a satisfaction-loyalty matrix and extend the customer satisfaction and loyalty post-analysis research bridging the gap in this field of research. To convert developed conceptual thought to experimental study, white goods industry is exemplified. 15 criteria are used for evaluation in 4 customer groups and a satisfaction-loyalty survey developed by experts is applied to 200 customers with face-to-face interviews. As a result of the study, a customer and criteria grouping method is created with high performance classification methods and good fit structural models. In addition, results are evaluated for developing a customer strategy improvement tool considering method outcomes.

[1]  Evangelos Grigoroudis,et al.  A survey of customer satisfaction barometers: Some results from the transportation-communications sector , 2004, Eur. J. Oper. Res..

[2]  C. Fornell,et al.  The American Customer Satisfaction Index: Nature, Purpose, and Findings , 1996 .

[3]  Dianne M. Finkelstein,et al.  A Beginner's Guide to Structural Equation Modeling , 2005, Technometrics.

[4]  Evangelos Grigoroudis,et al.  Preference disaggregation for measuring and analysing customer satisfaction: The MUSA method , 2002, Eur. J. Oper. Res..

[5]  Shu-Ching Chen.  The customer satisfaction–loyalty relation in an interactive e-service setting: The mediators , 2012 .

[6]  C. Fornell.  A National Customer Satisfaction Barometer: The Swedish Experience: , 1992 .

[7]  P. Bentler.  MULTIVARIATE ANALYSIS WITH LATENT VARIABLES: CAUSAL MODELING , 1980 .

[8]  Nigel Hill,et al.  Handbook of customer satisfaction and loyalty measurement , 2000 .

[9]  Hsiu-Yuan Hu,et al.  Preliminary research on customer satisfaction models in Taiwan: A case study from the automobile industry , 2011, Expert Syst. Appl..

[10]  Toshinori Chikara,et al.  Research of measuring the customer satisfaction for information systems , 1997 .

[11]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[12]  Venkatesh Shankar,et al.  Customer value, satisfaction, loyalty, and switching costs: An illustration from a business-to-business service context , 2004 .

[13]  E. Anderson,et al.  Strengthening the Satisfaction-Profit Chain , 2000 .

[14]  Colin Jevons.  Improving Customer Satisfaction, Loyalty, and Profit , 2001 .

[15]  P. Ngobo,et al.  Decreasing Returns in Customer Loyalty: Does It Really Matter to Delight the Customers? , 1999 .

[16]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[17]  P. Bentler,et al.  Significance Tests and Goodness of Fit in the Analysis of Covariance Structures , 1980 .

[18]  Mark S. Johnson,et al.  The Different Roles of Satisfaction, Trust, and Commitment in Customer Relationships , 1999 .

[19]  Vipin Kumar,et al.  Introduction to Data Mining, (First Edition) , 2005 .

[20]  Chiao-Yun Connie Chang.  The Handbook of Customer Satisfaction and Loyalty Measurement, 3rd edn , 2007 .

[21]  Andreas Ritter,et al.  Structural Equations With Latent Variables , 2016 .

[22]  A. Gustafsson,et al.  Improving Customer Satisfaction, Loyalty, and Profit : An Integrated Measurement and Management System , 2000 .

[23]  V. Kumar,et al.  Revisiting the Satisfaction-Loyalty Relationship: Empirical Generalizations and Directions for Future Research , 2013 .

[24]  Christian Homburg,et al.  How Organizational Complaint Handling Drives Customer Loyalty: An Analysis of the Mechanistic and the Organic Approach , 2005 .

[25]  C. Ranaweera,et al.  The influence of satisfaction, trust and switching barriers on customer retention in a continuous purchasing setting , 2003 .

[26]  Min Xie,et al.  Ranking of customer requirements in a competitive environment , 2008, Comput. Ind. Eng..

[27]  K. Kristensen,et al.  Customer satisfaction measurement at Post Denmark: Results of application of the European Customer Satisfaction Index Methodology , 2000 .

[28]  Athakorn Kengpol,et al.  The expert system for assessing customer satisfaction on fragrance notes: Using artificial neural networks , 2006, Comput. Ind. Eng..

[29]  B. Byrne.  Structural Equation Modeling with LISREL, PRELIS, and SIMPLIS: Basic Concepts, Applications, and Programming , 1998 .

[30]  Xianyi Zeng,et al.  A fuzzy model of customer satisfaction index in e-commerce , 2008, Math. Comput. Simul..

[31]  Rex B. Kline,et al.  Principles and Practice of Structural Equation Modeling , 1998 .

[32]  Li Xiu,et al.  Application of data mining techniques in customer relationship management: A literature review and classification , 2009, Expert Syst. Appl..

[33]  Ian Witten,et al.  Data Mining , 2000 .

[34]  Jens J. Dahlgaard,et al.  ECSI – Customer Satisfaction Modelling and Analysis: A Case Study , 2007 .

[35]  Geoffrey Randall.  Principles of Marketing , 1993 .

[36]  Ali Turkyilmaz,et al.  Development of a customer satisfaction index model: An application to the Turkish mobile phone sector , 2007, Ind. Manag. Data Syst..

[37]  R. Wood,et al.  Managing customer value : creating quality and service that customers can see , 1994 .

[38]  Carole Driver.  Handbook of Customer Satisfaction Measurement , 1999 .

[39]  Peter M. Bentler,et al.  Estimates and tests in structural equation modeling. , 1995 .

[40]  Steven A. Taylor,et al.  Measuring Service Quality: A Reexamination and Extension , 1992 .

[41]  V. Žabkar,et al.  Managing customer loyalty through the mediating role of satisfaction in the DIY retail loyalty program , 2009 .

[42]  A. Kara,et al.  Supermarket self-checkout service quality, customer satisfaction, and loyalty: Empirical evidence from an emerging market , 2014 .

[43]  T. J. Gerpott,et al.  Customer retention, loyalty, and satisfaction in the German mobile cellular telecommunications market , 2001 .

[44]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[45]  L. Dubé,et al.  Building Customer Loyalty— , 1999 .

[46]  J. J. Cronin,et al.  Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments , 2000 .

[47]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[48]  Zhaohua Deng,et al.  Understanding customer satisfaction and loyalty: An empirical study of mobile instant messages in China , 2010, Int. J. Inf. Manag..

[49]  Karl G. Jöreskog,et al.  Lisrel 8: Structural Equation Modeling With the Simplis Command Language , 1993 .