Customer Value and Customer Loyalty: Comparison and Application

Abstract

There are many researches about customer value. For example, Kucia M et al. [4] proposed to identify the framework of the use of new technologies in customer value management from the perspective of sustainable development in the context of the concept of the engaged customer. Pu et al. [5] proposed an adaptive density peak algorithm based on the Gini coefficient. In their research, they used clustering algorithm evaluation index analysis and visualization analysis experiments. The results show that the model and algorithm of customer classification are more effective and fully reflect customer value. Heldt R et al. [6] proposed an RFM/P model to estimate customer values per product and then aggregate them to obtain the overall customer value. Their model overcomes the defect of traditional FRM that does not take the product perspective into account. Empirical applications demonstrate that RFM/P can combine customer and product perspectives. Additionally, when there are changes in customer purchase behavior regarding recency per product and frequency per product, RFM/P prediction accuracy was better than traditional RFM. Oh H et al. [7] proposed the hospitality and tourism researches on customer satisfaction (CS), service quality (SQ), and customer value (CV) published in mid-15-16 in a number of established hospitality and tourism journals. In their research, each study was categorized according to more than 50 criteria through a comprehensive coding scheme and found that hospitality and tourism studies relied heavily on cross-sectional data obtained through survey methods. In contrast, business studies made more use of experimental designs. Additionally, most studies were not based on a strong theoretical foundation, although CS studies preferred to embed theory. Wu Y L et al. [8] proposed the impact of six marketing-mix components on consumer loyalty through consumer value in social commerce (SC). In their research, through PLS analysis, they found that all components of the SC marketing mix (SCMM) have significant effects on SC consumer value. Additionally, SC customer value positively influences SC customer loyalty (CL). Daniels [9] proposed the concept of CVM and key issues to drive more effective marketing activity. In his research, he points out that there are two complementary approaches to CVM, they ensure that both parties to a business relationship receive added value. The first one attempts to measure and assess the perceived value of goods/services to customers. This information is used as the basis for continuous review and improvement of those goods or services. The second approach measures the value of a specific customer or customer segment to the organization and adjusts marketing activities. In general, an organization needs to evaluate customer value. These different algorithms and models are designed to provide organizations with more accurate and effective data when formulating strategies. However, the perspectives for each research were standing on different perspectives. For example, Kucia M et al. [4] is using technology to evaluate customer value according to engaged customers. Still, the RFM/P model proposed by Roddrigo et al. [6] evaluates the overall customer value according to the customers of each product. Moreover, Oh H et al. [7] designed the coding schema to identify the customer values based on the gathered survey, which is different from the experimental designs from business studies. To measure customer value more effectively, adjust marketing activities and develop marketing strategies, different organizations still need to use more targeted algorithms and models to obtain data according to the situation. 3. CUSTOMER LOYALTY There are also many researches about customer loyalty. For example, Iglesias O et al. [10] examines the influence of corporate social responsibility (CSR) on customer loyalty and investigates the influence of co-creation on customer trust. Structural equation modeling was used to test the hypothesized relationships simultaneously. The results show that CSR influences customer loyalty through co-creation and customer trust. However, the indirect impact is the stronger of the two, implying that embracing co-creation activities and developing customer trust can make it easier for CSR practices to enhance customer loyalty. Chen C F et al. [11] proposes a conceptual model to investigate the relationships among customer participation, co-created values, and customer loyalty in an air transport context, and empirically test the model by using questionnaire survey data collected from Taiwanese airline passengers. The results prove that system satisfaction is related to satisfaction with the company, and both system satisfaction and company satisfaction positively impact customer loyalty. Skačkauskienė et al. [12] create an effective model of loyalty measurement, whose main solutions come from clarifying the role and assessment of customer loyalty. These solutions include selecting an appropriate concept, the loyalty specification, the identification of the necessary period for loyalty measurement, the differentiation of loyalty measurement according to the available data, and the stages of measuring loyalty. The results show that the customers' loyalty of the surveyed service providers is at a moderate level. The study results also show the superiority of the proposed model in measuring the status of customer loyalty and obtaining better solutions to develop customer loyalty in the service sector. Wassouf W N et al. [13] provides a methodology for telecom companies to target different-value customers by appropriate offers and services. Firstly, customers were segmented based on the new approach TFM (Time-frequency-monetary), and the level of loyalty was defined for each segment or group.