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Evaluation of customers' perception of CRM in UK retail banking industry

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https://www.eduzhai.net/ Journal of Money, Investment and Banking ISSN 1450-288X Issue 26 (2012) © EuroJournals Publishing, Inc. 2012 http://www.journalofmoneyinvestmentandbanking.com An Evaluation of Customer Perceptions on CRM within the UK Retail Banking Hussein A. Abdou Correspondence Author Reader in Finance & Banking Salford Business School, University of Salford, Salford, Greater Manchester,M5 4WT, UK E-mail: h.abdou@salford.ac.uk Tel.: +44 1612 953001 Fax: +44 1612 955022 Enimini I. Idiongette The University of Salford Business School, Salford Greater Manchester, UK, M5 4WT James Mulkeen The University of Salford Business School Salford, Greater Manchester, UK, M5 4WT Abstract This paper aims to investigate customer perceptions of the concept of CRM and its relevance to customer satisfaction and loyalty in the UK retail banking industry. It also seeks to identify the critical factors that can impact on a consumer’s choice of bank. The main research instrument is a questionnaire. 240 questionnaires are randomly distributed. Two statistical modelling techniques namely multiple regression and multinomial regression are used to determine characteristics of customers’ satisfaction and loyalty and to identify key factors in managing customer relationships. Our findings indicate that, in order of priority, the three primary factors that significantly affect customer loyalty are the opportunity to evaluate service offerings; IT and Tele-banking services and opening and closing hours. Participants identified eight significant factors that affect their satisfaction. The three primary factors, in order of priority, are overall satisfaction; the speed of service and IT and Tele-banking services. The most influential factors in choosing a bank, in order of importance, are a bank’s opening and closing times; IT and Tele-banking services; account and transaction accuracy; customer loyalty and overall satisfaction. In conclusion, our results suggest that customers’ satisfaction and loyalty can be jointly predicted by these key CRM factors. Although customer satisfaction and loyalty has been the subject of much discussion and further research, this paper has developed CRM by applying multinomial regression to identify the most influential factors for choosing a bank. Keywords: CRM; Customer loyalty; Customer satisfaction; Relationship management attributes; Retail banking; Service quality. JEL Classification: C40; G21 Journal of Money, Investment and Banking - Issue 26 (2012) 75 1. Introduction The significance of customers to business success and the consequent need for businesses to manage customer relationships in order to satisfy their needs and encourage them to repeat purchase can be traced to the pre-industrial era, (Parvatiyar and Sheth 2001/2002, Jobber, 2004). However, what is significant in evolving systems of CRM is their need to reflect the competitive business environment which is characterised by rising standards; a faster pace of evolutionary change; customers who are more demanding; the need for organisations to be more socially and resource responsible and where the struggle for business survival is getting more intense (Nguyen et al, 2007). Consequently, organisational success is, in part, dependent on its ability to not only assemble relevant data on the perception and the requirements of their customers but also to be committed to position customer satisfaction in the heart of their corporate objectives so that the organisation can identify opportunities, discover and analyse problem areas and implement strategic adaptations (Wu and Wang, 2012; Temtime, 2004). Overview of customer relationship management: To date, attempts to establish a universal definition of CRM have failed. The primary reasons given for the variation in definitions are based on the different backgrounds and orientations of relationship marketing scholars, who tend to adopt either a technological or marketing perspective (Roos and Edvardsson, 2008; Nadiri et al., 2008). Another factor that may account for the variations in definition was proposed by Zablah et al (2004) who claim that, while some authors view CRM as a process or strategy others consider it to be a philosophy. Banks in the UK are currently struggling to cope with the effects of the severe global financial crisis, which ensued in 2007, and this has given rise to a number of structural changes across major financial markets in the world, including the UK. The magnitude of the financial crisis and many other factors have caused the banks to rethink existing financial management frameworks; one of which is redirecting its central focus from product/service sales volumes and marketing activities to customercentric strategy. Again this strategy is not new in times of increased global completion and deregulation (Bull 2003, Stefanou et al., 2003 and Alexander 2004). The success of such customer-centric strategies rests on the bank, or other financial institution, developing a level of differentiation that is perceived by the customer in order for a loyal customer base to be maintained (see for example, Vesel and Zabkar, 2010; Proenca and Rodrigues, 2011). Developing this view, King (2005) claimed that customer retention programmes can be a powerful tool in the arsenal of CRM because the cost of acquiring a new customer is far greater than the cost of maintaining a relationship with a current customer. Ahmed and Buttle (2002) also highlight that the practice of customer retention has become a widespread strategic and managerial issue and it is viewed as a key objective in the concept of customer relationship management. Other researchers such as Bahia and Nanatel (2000) and Jamal and Naser (2002) equally acknowledge that banks and other businesses are turning to relationship management as a means of promoting loyalty and retention in the current hyper competitive business environment. To this effect, efforts have been made to establish a link between customer retention, satisfaction and the business theory of relationship marketing. For example, Jones and Farquhar (2003) suggested that relationship management is centred on gaining deeper understanding to be able to effectively satisfy customers’ needs; Buttle (2004) observed that retention can yield several economic benefits and Ang and Buttle (2006) concluded that “a 5% increase in customer retention can generate an increase in customer net present value of between 25% and 95% across a wide range of business environment”. Also, besides serving this important customer segment, Ang and Buttle (2006) maintain that retention programmes equally allow firms to gather relevant data on customers who could be suitably understood, targeted and effectively related with. This reflects the view proposed by Raines (2005) that effective CRM regimes should not only be committed to serving its profitable customers but should also adopt approaches that encourage other customers to climb the ‘loyalty ladder’. How CRM impacts satisfaction and loyalty: Many authors including Bolton, 2004 and Zineldin, 2005 assert that the primary advantage of CRM is the edge it offers organisation in discovering more about their customers and the way in which they interact with the organisation. Through CRM, firms 76 Journal of Money, Investment and Banking - Issue 26 (2012) can concentrate on their core competencies to enhance the delivery of high customer value services (Geib et al 2006). For financial organisations this enables them to integrate different products and services in order to provide customers with support for their far-ranging financial requirements and desire for a “one-stop finance” solution. Consequently, CRM provides the opportunity to stimulate sales referrals and presents opportunities for product up-selling or cross selling which should ensure a positive relationship in the cost-revenue ratio of organisations, (Bose, 2002; Lambert, 2010). Xu and Walton (2005) highlighted another benefit of CRM as including the provision of realtime data on customers’ purchasing pattern, pre and post sales behaviour. As such it enables instant market research which enables an organisation to react instantly and to make informed decisions on product/service performances (Gifford, businessballs.com). For Cravens and Piercy (2006) this represents a valuable tool for service providers as it enables them to provide the best service not only to meet customers’ expectations but also to exceed expectation and delight customers. Competitive advantage can therefore be developed through the strategic management of customer relationships that focuses on long-term profitability and sustainability as well as building and nurturing long-term relationships with customers. This implies that organisations need to understand that relationship management practices can have either a negative or positive impact on organisational performance and it helps the organisation to cultivate and nurture effective and profitable relationship management strategies, (Bull, 2003, Choy et al. 2003, Ranjan, 2009; Akdag and Zineldin, 2011). Raines (2005) suggests that an effective CRM regime is not only committed to serving its profitable customers, but to also adopt approaches that will encourage other customers to climb the ‘loyalty ladder’. In accepting that customer loyalty is a key driver of corporate success, Kumar and Rajan (2009, p.1) maintain that “loyal customers cost less to serve, pay more than other customers and attract more customers through word of mouth”. Wong and Zhou (2006) confirm that CRM offer invaluable benefits of customer loyalty and word-of mouth advertising opportunities. 2. Methodology Our main research questions are as follows: 1. Is CRM relevant to customer satisfaction and loyalty? 2. What are the key factors that affect consumers’ choice of bank? In this paper, a questionnaire is the main research instrument. Given that bank customers were highlighted as major stakeholders in this study, 240 questionnaires were distributed randomly to aid the evaluation of the concept of CRM from a customer perspective. A response rate of 87.5% is achieved (i.e. 210/240). The questionnaire comprises two sections: the first section focuses on investigating the major factors that influence customers’ choice of banks and the reasons for defection while the second section aims to measure satisfaction and loyalty levels among customers across a variety of relationship management attributes. The questionnaire structure allows for factors that affect customer relationships with their banks to be identified and evaluated. The questionnaire is designed to capture general service experiences and customers’ perceptions of relationship management they have experienced. As such, respondents are not required to recall specific service experiences, as these could be impaired due to passage of time. A pilot test was conducted to ensure that ambiguities and the tendency for misinterpretation of questions were reduced to a minimum. 2.1 Sample Selection and Convenience In order to validate the theoretical findings in this paper, data is gathered from commuters across the UK. To investigate customers’ perceptions of relationship management practices in banks, a sample selection approach is adopted. Respondents came from a wide range of demographic divisions representing the views of people across gender as well as working status, which included; full-time Journal of Money, Investment and Banking - Issue 26 (2012) 77 workers, part-time workers, students, people not working and those who are retired. Table 1 gives a breakdown of the categorical data of the respondents. Table1: Gender * Work Status Cross-tabulation Gender/Work Status Male Female Total Full-Time 100 60 160 Part-Time 8 12 20 Student 8 16 24 Not Working 0 4 4 Retired 2 0 2 Total 118 92 210 2.2 Statistical Method Employed in this Paper Two modelling techniques, namely, multiple regression and multinomial regression, are used in this paper. 2.2.1 Multiple Regression The multiple regression technique allows for the prediction of a score on one variable (dependent) on the basis of the scores on several other variables (independent). Below is the equation for multiple regression analysis where, = Intercept, a measure of the mean for the responses when all predictor variables are at value 0 (zero), = delta function or slope measuring the rate of change in Y (the dependent variable) given the change in X (the predictor variable) and the analysis. …… and are the predictor variables used in 2.2.2 Multinomial Regression This form of regression is able to analyse variables from several categories and has a high likelihood of demonstrating relationships across categories irrespective of demographic influences such as age, gender, and work status etc. Multinomial regression formula is as follow: where, 1 is the usual indicator function; α and are the model parameters; 1, 2 … n are the probabilities of various attributes, respectively; Xi are the covariates of the i th attribute; y1i is an indicator variable which is 1 if the i th attribute is of type 1, or 0 otherwise, etc. 3. Results and Discussion To test the questionnaire validity and reliability, firstly, the questionnaire was sent to a number of academics and practitioners in the area to test its validity. Secondly, Cronbach’s Alpha is used to test the reliability of research questions, the results of which are shown in Table 2. Table 2: Reliability statistics Item Relationship management attributes Overall, excluding demographic open-ended questions Overall Cronbach’s Alpha .892 .797 .751 No of items 18 37 41 78 Journal of Money, Investment and Banking - Issue 26 (2012) 3.1 Customers’ Perception of CRM and its Importance The results revealed that each of the UK retail banks investigated in this paper had good relationships with their customers. Respondents provided a number of reasons to describe the good relationship they have with their banks. Prominent amongst them are account and transaction accuracy, friendliness and helpfulness of staff members, IT and Tele-banking services as well as efficiency in correcting mistakes. The results show that 83% of respondents consider themselves loyal to their banks, implying that the development, acceptance and application of CRM programmes in UK banking has had a positive impact. This supports the conclusions of Ryan and Polyhart (2003) who observed that customer care regimes have become one of the major business practices in retail financial services, as CRM has had a positive impact on satisfaction, retention and loyalty. Table 3: Customers Perception of CRM Factors Importance to customer satisfaction and loyalty (%) Range of services Speed of services Account and transaction accuracy Problems are resolved quickly Efficiency in correcting mistakes Service quality 79% (n=166) 70% (n=148) 89% (n=186) 72% (n=152) 70% (n=148) Average 76% Opening and closing times Friendly and helpful staff members IT and Tele-banking services Opportunity to evaluate service offerings Delivery systems 70% (n=148) 83% (n=174) 80% (n=168) 56% (n=118) Average 72% Ease in loan negotiation Rate of interest charged Rate of interest paid Availability and Suitability of products 41% (n=86) 34% (n=72) 31% (n=66) Average 35% Happy with bank’s reputation Level of loyalty Will recommend bank to others Customers’ commitment to banks 74% (n=156) 83% (n=174) 76% (n=160) Average 77% Table 3 presents the percentages of customer satisfaction on the various CRM factors that are investigated. These factors have also been classed into broad service attribute categories such as service quality, service delivery system, and availability and suitability of financial products and service charges. The categories are adopted and modified from previous work and such categorisation is considered necessary, as it helps to highlight and explain customers’ perceptions of the individual CRM factors, which in turn will reveal their respective level of importance to customer satisfaction and loyalty (Zineldin, 2002) In addition, there is evidence of statistically significant differences (applying Chi2 test at Pvalue of 0.000, in relation to relationship management factors) between different respondents’ responses i.e. strongly agree, agree, neutral, disagree and strongly disagree, in regard to factors affecting customer loyalty, at the 99% confidence level, as shown in Table 4. Table 4: Chi square test results Attributes I am happy with the services from my bank If I have any problems, they are quickly resolved Chi2 value DF 85.883 4 48.990 4 P-value 0.000 0.000 Journal of Money, Investment and Banking - Issue 26 (2012) 79 Table 4: Chi square test results - continued I feel comfortable to question my bank on any issue I am happy with the location of my bank I am satisfied with the range of services offered I am satisfied with the speed of service processes I am satisfied with accounts and transaction accuracy I am satisfied with the efficiency in correcting I am satisfied with the rate of interest charged I am satisfied with the rate of interest paid I am satisfied with the flexibility in loan negotiation I am happy with the bank’s opening/closing hours The staff members are very friendly and helpful I am satisfied with the IT and Tele-banking services I get the opportunity to evaluate service offerings I am happy with the reputation of my bank I consider myself loyal to my bank I will be happy to recommend my bank to others 85.592 4 88.311 4 74.621 4 47.825 4 119.18 4 72.412 4 32.812 4 41.431 4 63.802 4 62.118 4 47.255 3 72.902 4 55.745 4 56.431 4 85.549 4 60.157 4 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 3.2 Regression Models Outcomes 3.2.1 Factors Affecting Customers’ Loyalty As earlier mentioned, certain statistical techniques are employed in this paper to support the discussion of the findings presented above, to provide an answer to our research questions, and to enhance the validity of the findings. A stepwise regression1 analysis is used to ascertain the factors affecting customer loyalty. The factors outlined in Table 5 are significant to customer loyalty when a regression analysis is run using “I consider myself loyal to my bank” as the dependent variable against all other predictor factors. Table 5 also provides summary of the multiple regression analysis, demonstrating that three attributes (out of 18) are statistically significant at least at the 95% confidence level. The predictive ability implies that, a unit change, for instance in the first attribute (I am happy with bank’s opening and closing hours) will result in an increase in the significance level of the dependent variable2. Therefore other predictor variables which are not significant, such as problem resolution and rate of interest charged should be given greater attention as they could potentially have a damaging effect on customer loyalty. Table 5: Factors affecting customer loyalty (step-wise regression) Factor Constant I am happy with bank’s opening and closing hours I am satisfied with IT and Tele-banking services I get the opportunity to evaluate service offerings F-ratio ANOVA P-value R2 R2 Adj. Note: Dependent Variable: I consider myself loyal to my bank. B 0.621 0.224 0.249 0.337 - P-value 1.558 0.024 0.020 0.002 - Model - 4.176 0.000 0.443 0.337 This result supports the work of Raines (2005) who observed that any effective CRM regime is not only committed to serving its profitable customers but also adopting approaches that will encourage other customers to become loyal. Bahia and Nanatel (2000) and Jamal and Naser (2002) equally acknowledge that banks and other businesses are turning to relationship management as a means of promoting loyalty and improving retention in the current hyper competitive business 1 It should be emphasised that correlation is investigated between various factors and results showed that all are in an acceptable range i.e. >= 0.50. 2 Same comment applies to the third attribute (I get the opportunity to evaluate service offerings). 80 Journal of Money, Investment and Banking - Issue 26 (2012) environment. It therefore implies that factors such as opening and closing hours of bank branches, IT and Tele-banking services as well as opportunity to evaluate service offerings are perceived by respondents as crucial issues in committing their loyalty to banks. 3.2.2 Factors Affecting Customers’ Satisfaction The following factors are reported as being significant to customer satisfaction when a stepwise regression analysis is run using “I am happy to recommend my banks to others” as the dependent variable, against all other attributes, as the predictor variables. In this context, “I am happy to recommend my bank to others” is used as a measure of satisfaction following the work of Molina et al (2007, p. 256) who described positive word of mouth as “relating pleasant, vivid or novel experiences; recommendation to others and even conspicuous displays” and agree that it impacts significantly on business performance. From the results in Table 6, it can be concluded that satisfaction with overall service offered by the bank is the most important attribute, and it is statistically significant at the 99% confidence level. Other attributes namely, speed of service processes; account and transaction accuracy; opening and closing hours and IT and Tele-banking services are important and statistically significant at the 95% confidence level. Finally, at the 90% confidence level, the following attributes are statistically significant: the range of services offered; friendliness and helpfulness of staff and the reputation of my bank. Table 6: Factors affecting customer satisfaction (stepwise regression analysis) Factor B P-value Constant -0.281 0.425 I am happy with services from my bank 0.420 0.001 I am satisfied with the range of services offered 0.239 0.069 I am satisfied with the speed of service processes 0.271 0.015 I am satisfied with account and transaction accuracy 0.288 0.033 I am happy with bank’s opening and closing hours 0.200 0.022 The staff members are very friendly and helpful 0.207 0.071 I am satisfied with IT and Tele-banking services 0.222 0.018 I am happy with the reputation of my bank 0.105 0.072 F-ratio ANOVA P-value R2 R2 Adj. Note: Dependent Variable: I will be happy to recommend my bank to others (as a measure of satisfaction). Model - - 9.989 0.000 0.655 0.590 Our findings support the idea advanced by Temtime (2004), Xu et al (2003) and Zineldin (2005) who all maintained that for firms to ensure customer satisfaction, loyalty, and to remain competitive in the global market, adequate concern must be given to factors like, ensuring product/service quality, attracting and retaining customers, product/service innovation and improvement. Consequently, customers should be viewed as strategic element and firms should gather relevant information on their needs in order to identify opportunities, discover and analyse problem areas as well as implement strategic adaptations (Temtime, 2004; Xu et al, 2003; Zineldin, 2005). It should be emphasized that regression models can provide an answer to the first research question3. 3.3 Multinomial Regression Outcomes Multinomial regression is considered suitable since it is able to analyse relationships between variables in several categories. Therefore, category ‘most influential factor in choosing a bank’ is regressed against category ‘relationship management attributes’. In general terms, using the model fitting 3This stated “Is CRM significant in customer satisfaction and loyalty?”. Journal of Money, Investment and Banking - Issue 26 (2012) 81 information, there is a relationship between the combinations of variables from both categories; and this judgement is made based on the statistical significance of the regression model shown in Table 7. Table 7: Multinomial regression model fitting information Factor Likelihood Ratio Tests Chi2 DF P-value Intercept 25.765 7 0.001 I am happy with the services from my bank If I have any problems, they are quickly resolved I feel comfortable to question my bank on any issue I am happy with the location of my bank 33.052 7 14.141 7 4.711 7 14.298 7 0.000 0.049 0.695 0.046 I am satisfied with the range of services offered I am satisfied with the speed of service processes 29.048 7 20.161 7 0.000 0.005 I am satisfied with accounts and transaction accuracy I am satisfied with the efficiency in correcting mistakes I am satisfied with the rate of interest charged I am satisfied with the rate of interest paid I am satisfied with the flexibility in loan negotiation 36.271 7 12.509 7 7.664 7 19.134 7 10.951 7 0.000 0.085 0.363 0.008 0.141 I am happy with the bank’s opening/closing hours The staff members are very friendly and helpful 40.623 7 6.533 7 0.000 0.479 I am satisfied with the IT and Tele-banking services I get the opportunity to evaluate service offerings 37.769 7 5.012 7 0.000 0.659 I am happy with the reputation of my bank 26.170 7 0.000 I consider myself loyal to my bank I will be happy to recommend my bank to others 34.629 7 18.631 7 0.000 0.009 Model Intercept only Final Psaudo R2 Overall classification accuracy Fitting Criteria (-2 Log Likelihood) 389.809 169.027 220.782 126 90.70% 67.30% 0.000 Note: The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0. In our analysis, the probability of the model is statistically significant at the 99% confidence level (with a chi2 value of 220.782). Once again, this result infers that CRM is highly significant to customer satisfaction and loyalty, and thus it can further support conclusions relating to the first research question. Table 7 equally reports the significant factors in the multinomial regression model and can provide an answer to the second research question4. Also, it can be observed that ten attributes are statistically significant at the 99% confidence level; two attributes are statistically significant at the 95% confidence level and one attribute is statistically significant at the 90% confidence level5. For more details regarding multinomial regression parameters estimates relating to category ‘most influential factor in choosing a bank’ and associated factors from category ‘relationship management attributes and factors’, the reader is referred to our Appendix. This result support the work of Bolton (2004), Bose (2002) and Zineldin (2005) who all agree that customer relationship management is a means of promoting loyalty and satisfaction for customers. 4. Conclusions and Policy Recommendations This paper provides an evaluation of customers’ perceptions of relationship management practices within UK retail banking. The findings indicate that the most influential factors affecting customers’ loyalty, in order of importance, are the opportunity to evaluate products and services offered by the 4 This stated “What are the key factors that affect consumers’ choice of bank?”. 5 Details regarding multinomial regression parameters estimates relating to category ‘most influential factor in choosing a bank’ and associated factors from category ‘relationship management attributes and factors’, are available on request. 82 Journal of Money, Investment and Banking - Issue 26 (2012) bank; IT and Tele-banking services and opening and closing hours. Whilst participants identified eight significant factors that affect their satisfaction, the three primary factors, in order of priority, are overall satisfaction; the speed of service and IT and Tele-banking services. In addition our results suggest that the most influential factors in choosing a bank, in order of importance, are a bank’s opening and closing times; IT and Tele-banking services; account and transaction accuracy; customer loyalty and overall satisfaction. These conclusions reflect the significance placed on identifying customer needs and expectations which in turn serve as a guide for managers in the allocation of resources to effective relationship management activities. This paper recommends that banks may consider the development of a more effective strategy to enhance access to banking services and personnel through physical and remote access routes, accuracy and speed of service and the customer perception that their expectations are sought and reflected in the products and services offered by the bank. By paying greater attention to these crucial parameters and viewing them as pivotal to contemporary dynamics of customer relationship management practices banks will benefit in terms of their reputation, operational activities and profit levels. This paper has developed CRM by applying multinomial regression to identify the most influential factors used by an individual to choose a bank and to determine their level of satisfaction and loyalty. This paper significantly contributes to bank managers understanding of factors associated with CRM practices. Future research would aim to extend our analysis to cover a larger sample across various sectors and countries. This would enable an investigation into the extent to which cultural and other factors affect CRM practices. Reference [1] Akdag, H. C. and Zineldin, M. (2011), “Strategic positioning and quality determinants in banking services”, The TQM Journal, Vol. 23 No. 4, pp. 446-457. [2] Ang. L. and Buttle, F. 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