CRM PERFORMANCE MEASUREMENT USING AHP AND CRM SCORECARD. Hana Pechová, Markéta Zajarošová

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1 CRM PERFORMANCE MEASUREMENT USING AHP AND CRM SCORECARD Keywords Hana Pechová, Markéta Zajarošová Department of Marketing and Business Faculty of Economics VŠB Technical University of Ostrava Customer relationship management, performance measurement, CRM Scorecard, AHP, B2B organizations Abstract This research examines the application of the AHP (analytic hierarchy process) method for the CRM (customer relationship management) performance measurement in B2B (business to business market). The aim of the paper is to define rank of factors measuring CRM performance. In the first part of the paper, theoretical base is described considering CRM performance measurement issue and the CRM Scorecard description. Research methodology introduces the AHP method. The following part of the paper deals with the application of the AHP method identification of factors measuring CRM performance, calculating coefficients for each factor via group of experts (managers of companies on the B2B market), ranking the factors in accordance with their importance. This study reveals fundamental factors important in the CRM performance measurement models in the B2B market. 1. Introduction According to Blattberg et al. (2001), Customer Relationship Management (CRM), a management philosophy that focuses on the nurturing of customer relationships, emerged as a response to decreasing customer loyalty and increasing competition. An understanding of how to manage relationships with customers effectively has become an important topic for both academicians and practitioners in recent years. CRM is the principle of relationship marketing that is necessary for contemporary markets, especially services. There are several concepts of CRM that companies use for collecting information about their customers. According to using CRM software companies get to know consumer behaviour of customers, demographic and other characteristics. Companies manage their own customers databases to segment and target to the best groups of customers. They can differentiate communication with each segment and adapt the offer. In the case of important customers we can talk about individual marketing. Its goal is to create closer relationship between customer and company (one-to-one marketing). Companies expect their own

2 benefit, for example increase of revenue, market share or customers loyalty, due to investments into CRM system. Richards and Jones (2006) identified the main benefits of CRM implementation for company. They conducted the managerial opinions and attitude to CRM systems analysis and defined the benefits as: Improved ability to target profitable customers, Integrated offerings across channels, Improved sales force efficiency and effectiveness, Individualized marketing messages, Customized products and services, Improved customer service efficiency and effectiveness, Improved pricing. Lots of contemporary companies in the B2B market use a CRM system for communication with their customers or suppliers. Companies try to calculate CRM implementation success, quantify the level and performance of the CRM concept, for example in comparison with competition or in time. Must say, there is no one and only concepts that is the best for every company. CRM systems are designed mostly individually and that s why their performance measuring differs too and is very flexible. Different authors describe several methods or possibilities how to measure CRM performance, we have to adapt in the concrete situation, market. The aim of the paper is to define rank of factors measuring CRM performance. This research is based on application of the AHP method for modelling CRM Scorecard in the B2B market. We use CRM Scorecard model for its coherence and possibilities of adapting to any company. The AHP method was chosen for its detailed procedure of ranking the factors and future development of our research. In the beginning of the paper, the theoretical base of CRM performance measurement is described, including the CRM Scorecard model. Research methodology introduces the AHP method. Application of the AHP in the CRM Scorecard was achieved by cooperation with experts (managers of companies in B2B). Due to their views and estimations the coefficients of the factors was calculated. The factors were ranked based on the importance in the CRM performance measurement process. In the conclusion, results and future research steps are summarized. 2. Theoretical background of the CRM performance measurement CRM performance measurement is important for comparison in time, verifying estimated development and achieving stated effects or for comparison among competition. In accordance to long term measurement, managers can determine the successful and the unsuccessful procedures, detect strengths and weaknesses in the communication process with customers as well. There are several methods how the CRM performance can be measured. The choice of one of the methods depends mostly on the experience of the company. These methods are for example Balanced Scorecard (Kaplan & Norton, 1992) or CRM Scorecard (Kim & Kim, 2008), CRACK model (Chlebovský, 2005), cost-oriented approach (Moedritscher & Mussnig, 2005), CRM Maturity model (Sohrabi, Haghighi, & Khanlari, 2010) or quantification of monitored factors without using any concrete model.

3 Kaplan and Norton (1992) designed the Balanced Scorecard model (BSC) that serves as a coherence tool for CRM performance measurement in the companies. They identified the factors influencing CRM performance, these factors are divided into four groups: Financial perspective value for shareholders, Customer perspective value for customers, Internal business perspective efficiency and effectiveness in business processes, Innovation and learning perspective sustaining and continuous innovative and learning process. Wang, Lo, Chi, and Yang (2004) used BSC for customer behaviour analysis and measuring relationship between customers and company. Izquierdo, Cillán, and Gutiérrez (2005) applied BSC for marketing activities performance measurement and measuring results in economic output of the company. Bohling et al. (2006) focused their research to identification factors that influencing successful CRM system implementation. This BSC concept is defined mostly for companies strictly oriented on their financial outcomes, the Company-Centric BSC. Nowadays the relationship company-customer is prefered, so this concept doesn t fit into contemporary market conditions. Kim, Suh, and Hwang (2003) modified the concept to Customer-Centric BSC and applied this new concept for the Korean online shopping centre. The factors measuring CRM performance are divided into four groups: Customer value lifetime value, customer loyalty, Customer satisfaction level of satisfaction with products or services, Customer interaction operational excellence, channel management of customer services, management processes, Customer knowledge status of the customer segment, customer data management. Table 1 shows comparison of factors influencing CRM performance measurement between Company-Centric BSC and Customer-Centric BSC. Company-Centric BSC Financial perspective Customer perspective Internal business perspective Innovation and learning perspective Customer-Centric BSC Customer value Customer satisfaction Customer interaction Customer knowledge Table 1 Comparison of Company-Centric BSC and Customer-Centric BSC; Source: Kim, Suh, and Hwang (2003) Kim and Kim (2008) developed BSC into the CRM Scorecard model. The CRM Scorecard model evaluates contemporary CRM performance level and readiness of the company for future implementations. The model operates with factors influencing CRM performance and factors influencing successful CRM system implementation. This model is more comprehensive, it is focused on the customers and company s CRM strategy. There is a synthesis of the Company- Centric BSC and the Customer-Centric BSC. Factors are divided into four groups:

4 Infrastructure perspective o IT - CRM technology, o Human capital employee behaviour, employee satisfaction, management attitude, o Organizational alignment training, reward system, organizational structure, o Culture partnership, market orientation, explicit goal, Process perspective customer acquisition, customer retention, customer expansion, Customer perspective customer loyalty, customer satisfaction, customer value, Organizational performance perspective shareholder value, profitability, customer equity. Each group of factors contains several concrete measurable variables, by which managers can evaluate the CRM performance level. By applying for example the AHP method, we can calculate importance indexes of the factors and we can modify the CRM Scorecard model for the individual company. Kim and Kim (2008) applied the CRM Scorecard model for the Korean bank institution. Authors Shafia et al. (2011) modified the concept as fuzzy CRM Scorecard model for Iranian industrial market. Soeini, Jafari, and Abdollahzadeh (2012) adjusted the factors into the specific CRM Scorecard model for Iranian government corporation. 3. Research methodology To calculating the importance indexes of the CRM Scorecard factors we conducted Analytic Hierarchy Process (AHP) analysis. AHP is a multi-criteria decision making method (MCDM) that uses pairwise comparison of the factors or groups of factors (Ishizaka & Labib, 2011). AHP was applied in CRM performance measurement for example by authors Kim and Kim (2008). Our AHP application included three steps: the hierarchy compilation as the CRM Scorecard model (Kim & Kim, 2008), the importance weights determination via experts, calculating the coefficients for each group of factors and for each factor. For evaluating the importance of the factors, there are several proposed scales that can be used (Ishizaka & Labib, 2011), we chose the linear scale with parameters 1-9 (Saaty, 1977). Value 1 means that the importance of two factors is equal, value 9 means the highest difference of the importance between the factors. We used normalized weights w i that we calculated by weighted average of the row geometric mean: w = =,, where s i,j are elements of Saaty s matrix S and s i,j = 1 and, (1) s, =,s, 1 9;9. (2), The consistency ratio (CR) verifies the consistency within expert s views. Critical values of the CR is number 0,1, the values CR 0,1 are considered as consistent. The consistency ratio is calculated by the formula CR=!" = " where λ max is maximum eigenvalue and can be calculated as follows:, (3)

5 λ $%& = ' (S.w+ ' /w, (4) where w is a vector and (S.w) i is an i-element of the vector. The random index (RI) value is determined in Table 2. N RI 0,00 0,00 0,58 0,9 1,12 1,24 1,32 1,41 1,45 1,49 Table 2 Random Index; Source: Saaty (1977) 4. Applying AHP within CRM Scorecard We conducted the research among academics, business managers and marketing managers from different B2B organizations. We obtained 6 valid datasets (2x2x2) due to explorative research. In Figure 1 you can see the scheme of the hierarchy process within the CRM Scorecard model (Kim & Kim, 2008). First line defines the objective that is CRM performance measuring. The second line is represented by four fundamental groups of factors in CRM performance measurement organizational performance, customer perspective, process perspective, infrastructure. Each group contains further specific factors that can be measured by concrete variables and indexes. Figure 1 Hierarchy of the CRM Scorecard model; Source: Kim and Kim (2008) In Table 3 the results of AHP are shown. Values of the consistency ratio were lower than 0,1 in all cases of comparing. In accordance to experts, the most important group of factors in CRM performance measurement is Customer perspective (41,2 %). Less important are Organizational

6 performance (25 %), Process perspective (21,2 %) and Infrastructure (12,5 %). We calculated local weights of the factors in each group and global weights for comparison the factors independently. Customer perspective factors are the most important in CRM performance, especially customer satisfaction (63,5 %). Customer satisfaction was perceived as the prerequisite of customer loyalty and customer value either. When the customer is satisfied, his value for the company grows and he tends to be more loyal. Perspective Measuring factors Local weights Global weights Organizational performance (0,250) Customer perspective (0,412) Process perspective (0,212) Infrastructure (0,125) Shareholder value 0,342 0,086 Profitability 0,292 0,073 Customer equity 0,366 0,092 Customer loyalty 0,088 0,036 Customer satisfaction 0,635 0,261 Customer value 0,278 0,114 Customer acquisition 0,266 0,056 Customer retention 0,488 0,104 Customer expansion 0,247 0,052 Employee behaviour 0,081 0,010 Employee satisfaction 0,069 0,009 Management attitude 0,301 0,038 Partnership 0,091 0,011 Market orientation 0,153 0,019 Explicit goal 0,125 0,016 IT 0,117 0,015 Organizational alignment 0,062 0,008 Table 3 Results of AHP; Source: authors Organizational performance factors were almost equal, the coefficients of each factor are close to value 0,3. However, we can identify customer equity (36,6 %) and shareholder value (34,2 %) more important than profitability (29,2 %). The perceived value of the company for shareholders and customers is more relevant than whether the company is profitable or not. Process perspective factors were determined by customer retention (48,8 %), customer acquisition (26,6 %) and customer expansion (24,7 %). Customer retention was the most important factor that may be related to customer satisfaction. Experts agreed that customer acquisition and customer expansion can be influenced by type of the product (FMCG versus durable goods).

7 Infrastructure factors were difficult to compare in some cases because not all factors were comparable among themselves. The most important factor was management attitude (30,1 %) that was perceived in most cases as a precondition to success of the other factors. The second and third most important factors were market orientation (15,3 %) and explicit goal (12,5 %) that determines proper company management in the market. IT (11,7 %) represents software reliability necessity. Partnership (9,1 %), employee behaviour (8,1 %), employee satisfaction (6,9 %) and organizational alignment (6,2 %) reflect the importance of teamwork, morale and good relationships in the workplace. All coefficients of global weights are displayed in Figure 4 in the last column. Hundred percent was divided among all factors considering the importance coefficient of the group of factors. For CRM performance measurement are the most important factors: customer satisfaction (26,1 %), customer value (11,4 %), customer retention (10,4 %), customer equity (9,2 %), shareholder value (8,6 %), profitability (7,3 %). Factors such as customer acquisition (5,6 %), customer expansion (5,2 %), management attitude (3,8 %), customer loyalty (3,6 %) are less important for CRM performance measurement. Infrastructure factors, besides management attitude, are the least important factors. 5. Conclusion The aim of the paper was to define rank of factors measuring CRM performance. This research was based on application of the AHP method for modelling CRM Scorecard in the B2B market. The hierarchy structure was compiled as the Korean CRM Scorecard model. The importance weights were determined via experts (academicians, business and marketing managers). The coefficients for each group of factors and for each factor (local and global weights) were calculated. For CRM performance measurement are the most important the factors regarding customer (customer satisfaction, customer value, customer retention, customer equity). The least important factors are infrastructure factors (internal processes). Proposed further research could be comparing the results using different scales, for example root square, inverse linear, logarithmic etc. Compare the results in B2C organizations or among different product markets. The research can be developed by applying further methods ANP (Franek & Kresta, 2013) or DEMATEL. Acknowledgement This paper is financed by Student Grant Competition of the Faculty of Economics, VŠB-Technical University of Ostrava; project s registration number is SP2014/126. All support is greatly acknowledged and appreciated.

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