Customer Relationship Management And Satisfaction Of Commercial Banks’ Account Holders In Nairobi City County, Kenya

ABSTRACT

Customer satisfaction is a dynamic parameter for measuring business success. As a modern measure for service quality, it ensures investment in development of customer centred management strategies such as Customer Relationship Management (CRM). Globally, CRM has been applied in the banking sector to enhance service quality and the resultant customer satisfaction which increases competitiveness, customer retention and profitability. Despite implementation of CRM programmes by commercial banks to increase satisfaction of account holders, customers continue to be dissatisfied with banking services. This study aimed at establishing the effect of CRM on satisfaction of commercial banks‟ account holders in Nairobi City County in Kenya. The specific objectives of the study were to establish the effect of value based CRM, customer centred organizational configuration and technology based CRM on satisfaction of commercial banks' account holders and to assess the moderating effect of demographic characteristics and mediating effect of service quality on the relationship between CRM and satisfaction of commercial banks account holders. The study was anchored on CRM theory and was guided by positivism philosophy. It utilized a cross-sectional research design involving descriptive and explanatory research techniques. The target population was 34,649,583 account holders from commercial banks in Kenya. A sample of 400 respondents was selected using mixed sampling techniques. Primary data was collected using a 5 point likert scale using both open and closed-ended questionnaire guided by the researcher. Reliability of the research instrument was determined by conducting a pilot test which yielded Cronbach‟s Alpha coefficient of 0.9. Construct validity was determined by use of confirmatory factor analysis while content and face validity was done by experts. Quantitative data was analyzed using both descriptive and inferential statistics and qualitative data was analyzed through content analysis with the aid of SPSS version 19. Descriptive statistics such as mean, frequency distribution and standard deviation were used to describe the characteristics of the variables. Diagnostic tests for normality, outliers, linearity, multicolinearity and homogeneity of variance were carried out to ensure multiple linear regression assumptions were met. Simple linear and multiple linear regression were conducted to assess the relationships between independent, mediating, moderating and dependent variables. The hypotheses were tested using a P-value