Volume 5, Issue 4 (10-2021)                   EBHPME 2021, 5(4): 267-275 | Back to browse issues page

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Dehghani Ghale R, Karimi F, Ghorbani Dinani H. Customer Lifetime Value of Supplementary Health Insurance: An Analytical Model. EBHPME. 2021; 5 (4) :267-275
URL: http://jebhpme.ssu.ac.ir/article-1-343-en.html
Department of Management, Mobarakeh Branch, Islamic Azad University, Mobarakeh, Isfahan, Iran
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Background: With the number of insurance customers growing, insurance companies are trying new ways to retain customers and streamline communication channels to avoid loss of revenue. The present study set out to develop a model for a reliable analysis of customer lifetime value.
Methods: The present study was exploratory mixed method in design. The study took place in Jundishapur University of Medical Sciences, located in Ahvaz, Iran. A total of 402 insurance experts and university staffers participated in the study. A cross-sectional data collection was done using semi-structured interviews (n = 22) and a questionnaire (n = 380). The latter was validated via a panel of content area experts, criterion-dependent validity (second-order confirmatory factor analysis), and divergent validity (cross-sectional load test and Fornell-Laker). Cronbach's alpha and combined reliability were - 0.8 and 0.8, respectively. A structural equation approach was employed to analyze data using Smart PLS software.
Results: Customer loyalty with an impact factor of 0.60 and T-statistic of 5.79, profitability with an impact factor of 0.55 and T-statistic of 3.75, customer co-creation with an impact factor of 0.28, and T-statistic of 2.7 have been identified as dimensions of customer lifetime value.
Conclusion: Measuring customer lifetime value to implement various strategies requires a deep understanding of such value dimensions as loyalty, profitability, and value creation.
Key words: Customer lifetime value, Supplementary health insurance, Customer loyalty, Customer profitability, Customer value co-creativity
Providing supplementary health insurance coverage is a top priority for healthcare systems across the world to protect patients from Catastrophic Health Expenditure (CHE) (1). The right to receive healthcare services was identified by the World Health Organization (WHO) as a fundamental human right in 1948. The organization reaffirmed the opportunity for all the people to access health services of sufficient quality without financial barriers (2). Health care systems are under considerable pressure to maintain the quality of medical care and reduce expenditures. In particular, chronic diseases, population growth, patient expectation, and shortage of skilled medical personal account for the unprecedented financial and logistical pressure on health systems. Healthcare systems have faced numerous challenges in fulfilling their mission (3). In developing countries, Out-of-Pocket (OOP) payment is the main source of health financing, pushing patients and their families toward a CHE situation (4). Hence, insurance is a mechanism that, via protecting patients and their families from CHE, provides access to a wide range of covered medical services, especially for hospitalization (5).
There are few ways to cover medical expenses. First, individuals must choose between using private health insurance (supplementary treatment) or public health insurance (basic treatment).  This is the case in countries such as Australia, Ireland, and Spain. A second option is to complement public insurance plans with private ones. Finally, private health insurance is purchased to cover expenditures not covered by premiums, such as dental services and physiotherapy procedures. Countries such as the Netherlands, France,
and Luxembourg can be subsumed under this category (6).
With the number of insurance customers growing, insurance companies are trying new ways to retain customers and streamline communication channels to avoid the loss of revenue. It is more profitable for insurance firms to keep their existing customers rather than enrolling the new ones on average, insurers lose as much as 15 percent of their customer base per year. Reducing the rate to 10 percent increases the average growth to as high as 50 percent (7). Therefore, companies achieve competitive advantage and profitability by establishing more communication and interaction with their current customers (8). To evaluate and assess customer value, organizations concentrate on customer behavioral characteristics and
treat each customer uniquely to achieve competitiveness, maximize organizational profitability, and keep their communication channels with customers. Insurance companies are considered active and profitable institutions in many countries, playing a compensatory and protective role in the, so much so that their flourishing   will spill over to other businesses (9). According to cutler, marketing is about managing a beneficial relationship with the customer. On the one hand, the goal is to attract new customers and promise them more value, and at the same time, satisfy the existing customers, and on the other hand, to profit off of them (10). On the one hand, allocating  resources  based on financial criteria and calculating the level of sales (portfolio), by creating unhealthy competition between insurance companies and even the branches of their company (11), and, on the other hand, problems such as the lack of clarity between the obligations of the first (basic treatment) and second (supplementary treatment) insurer, lack of scope, lack of comprehensive insurance coverage in the face of a wide variety of diagnostic and treatment practices, the unavailability of insurance services in contract centers, unfamiliarity with the rules and regulations of supplementary health insurance, and lack of customer's awareness to purchase this type of insurance, increases the premium by insurance companies (12). Traditional marketing metrics, such as brand awareness and even sales, are no longer sufficient to demonstrate return on investment because it is likely for marketing measures to improve sales or stocks to seriously damage the brand's profitability in the long run. Financial metrics such as stock price and total profit of the firm, or a business unit also do not resolve this problem in terms of limited recognition ability (6). One way is to identify profitable customers and allocate resources which commensurate with profitability (13). Customer value is discussed under various entries such as lifetime value (LTV), Customer Lifetime Value (CLV), customer capital (CE), and Customer Value (CV) by Singh (14), Gupta et al. (15), Ferrentino (16), Siddharth (17) and Wang Chang (18) have been studies. Gupta and et al.  (15) introduced customer lifetime value as an indicator that allows the evaluation and measurement of customer value over time in terms of the expected profit between the company and the customer . According to the above mentioned, the customer's life time value with a superficial and shallow look and misunderstanding, in the form of concepts such as, customer's past share (SOW model), customer's past performance and profitability (PCV model), Return on investment time (ROI model), repetition rate, frequency and financial volume of the purchase (RFM model) have been examined (19). Therefore, the purpose of this study was to develop and present a comprehensive and specialized model for measuring this concept in the supplementary health insurance, provide a correct understanding of the dimensions and indicators of customer life time value, use the existing capacities in this field, and establish a win-win relationship between the insurance company and the insured people.
Materials and Methods
This was a qualitative-quantitative exploratory study. The statistical population of the study (n = 7,000) was staffers having the supplementary health insurance at Ahvaz Jundishapur University. The statistical sampling method was a simultaneous combined approach. According to the type of research plan used (data theory of the foundation), statistical samples included two sections: Managers and experts of insurance companies located in Khuzestan (Ahvaz), which were selected using purposive sampling (n = 22) and 380 staffers of supplementary health insurance based on probability random sampling method, who were counted using Morgan table. To collect information, first, the conceptual review method was used which included: 1- Identifying the research question (what are the factors related to the customer lifetime value of supplementary health insurance customers?) 2- Search strategy: Based on the research question, the researcher first searches for keywords in the period between   August 26, in 2019 to August 25, in 2020, in databases of Irandoc, Civilica, Google Web, SID, Scholar Scopus, Emerald, Elsevier, and related articles were extracted from 2015 to 2020. 250 articles were excluded, and 30 were grey literature (e.g., books and dissertations). 3- Selection: First, the abstracts and articles that met the inclusion criteria were included in the study. Customer profitability and customer co- creativity have been the value of the customer, at which point duplicates were removed by EndNote software for 48 articles. 4- Organization: Finally, the data of the articles were used to design the protocol and interview questions as well as the final writing of this research.
Due to the exploratory nature of the research and the non-response of databases and search engines for all variables, the search for information and gray texts in Irandac, Civilica, Google Web, SID, Scholar Scopus, Emerald, Elsevier, etc. has been done in pairs (a variable dependent on each of the independent variables).
First, by asking general questions about the customer lifetime value, the opinions of experts were received in several stages, and then, the above coded views were opened by the data foundation (inductive) method. These codes are then categorized in the axes obtained from the  literature on the subject through semi-structured interviews with experts. Finally, in order to determine the general framework of the study, create a flow from external auditors, have theoretical foundations, etc., use techniques such as two people conducting interviews separately, use structured processes from convergent interviews, the reliability and questions related to measuring the opinion of sample people were observed after specifying the indicators and dimensions of the subject (customer lifetime value) from a semi-structured interview, a questionnaire appropriate to the two sections of the demographic characteristics of people (age, gender, etc. ) was designed, in which 21 questions were for customer loyalty, 10, for customer profitability, 13 questions were for customer value creation. 13 questions were designed to determine the validity of the questionnaire regarding content validity (expert opinion), dependent validity (through second-order confirmatory factor analysis) and two types of divergent validity, that is cross-sectional load test and Fornell-Larker, Cronbach’s alpha )0.8) and combined reliability (0.8) were used to measure the resilience. These concepts have been analyzed using structural equation modeling in Smart PLS software. During the distribution of the questionnaire, the individuals were assured that their information was confidential and if the interviewees did not want to continue their cooperation, they could announce their withdrawal from the process at any stage of the research. This research was approved by research ethics committee of Mobarake Isfahan University under the code 19021254971004. The required permissions for distributing the questionnaire were obtained from the Department of Welfare and Physical Education of Ahwaz Jundishapur University, and the necessary coordination was conducted with the provincial health system.
Most of the respondents were married women with 52 bachelor's degrees, and their average age was about 45. Table 1 shows their demographic characteristics.
Table 2, presents the output results of the convergent validity of all variables of the AVE model ≥ 0.4), which suggests the appropriateness of the convergent validity of the model variables.
According to the criteria in Table 3, coefficient of significance is (T-values = 1.96), coefficient of determination, (R2 = 0.10), predictive power coefficient, (Q2 = 0.99), and good fitting of the model is (GOF = 0.46).
As can be seen in Table 4, customer satisfaction with an impact factor of 0.44 and T-4.50 statistics, brand preference with an impact factor of 0.44 and T-statistics 8.78, customer trust with an impact factor of 0.29 and T-statistics 3.57, repurchase intention with the impact factor of 0.38 and the T-statistic of 8.50, recommending others with an impact factor of 0.35 and the T-statistic of 2.58, among which the statistics of all aspects of customer loyalty are higher than 1.96 and are verifiable. Financial practice have an impact factor of 0.39 and a
T-statistic of 11.40, buying pattern, an impact factor of 0.65 and T-statistic of 19.78, and service delivery pattern have an impact factor of 0.21 and a T-statistic of 4.01, which are verifiable. Search for information with an impact factor of 0.25 and a T-statistic of 2.58, share with an impact factor of 0.28 and a T-statistic of 4.59, co-production with an impact factor of 0.38 and a T-statistic of 7.05, feedback with an impact factor of 0.18 and a T-statistic of 2.92, help with an impact factor of 0.44 and a T-statistic of 6.20, and tolerance with an impact factor of 0.31 and a T-statistic of 5.29, among which statistics of all dimensions of customer value co-creativity are higher than 1.96 and are verifiable. Customer loyalty with an impact factor of 0.6 and T-statistics of 5.79, profitability with an impact factor of 0.55 and T-statistic of 3.75, customer value co-creativity with an impact factor of 0.28 and T-statistic of 2.70, among which the statistics of all aspects of customer life are higher than 1.96which can be verified.

Table 1. Demographic characteristics of staff members from Jundishapur University of Medical
Sciences in Ahvaz
  Frequency Grouping in the study
(in years)
Under 35
Above 55
Marital Status 8
Education Level 5
Associate Degree
(n = 380 = 100 %)

Table 2. Convergent validity of research variables
Mean extraction variance
(AVE ≥ 0/4)
latent variables
0.57 Customer Lifetime Value
0.54 Information Sharing
0.69 Customer Trust
0.60 Service Delivery Pattern
0.59 Purchase Pattern
1.00 Feedback
0.58 Tolerate
0.69 Brand Preference
1.00 Advise Others
0.61 Co-Production
0.63 Search For Information
0.60 Customer Satisfaction
0.64 Behavioral Mood
0.64 Financial Procedure
0.56 Profitability
0.67 Repurchase Intention
0.61 Customer Loyalty
0.62 Help

Table 3. R-values and Q2, (T-values)
Customer lifetime value Customer value co-creativity Customer profitability Customer loyalty  
0.99 0.99 0.99 0.99 R2
0.25 0.13 0.23 0.10 Q2
1.96 ----- ----- ---- T-values
0.46 ----- ----- ---- GOF
Table 4. Test of research model hypotheses

In today's highly competitive world, massive customer loss can have a significant impact on a company's cash flow, and can lead to problems such as reducing short-term cash flow or
even bankruptcy (6). Not all customers are
equally profitable. Therefore, it may be desirable to exclude some customers, or to allocate
different resources to different customer groups
In this research, a specific and practical model in measuring the value of customers in the supplementary health insurance sector was presented. With a simple and, at the same time, multidimensional approach, to accurately predict the needs and wants of the current customers, provide insurance services tailored to customer needs at the time and place they need, create appropriate value for policyholders, increase insurance penetration, and finally, create satisfaction in them. Therefore, by creating the above conditions, the CLV for the insurance company can be increased.
In this study, (CLV) has been investigated by data base method and identifying 14 indicators in the form of three dimensions of customer profitability, customer loyalty, co-creativity value ,and the validity of the concepts were confirmed by Structural Equation Modeling (SEM).
These findings were in line with customer profitability which were consistent with the studies by Echkakoi (19), Bonach et al.  (20), Siddhart et al. (17) Profitability was addressed in the CLV.
The general difference between the studies mentioned and the present study in the discussion of customer profitability is that these researches are too focused on indicators and aspects such as income, types of expenses, profit, etc.
Therefore, in this study, it is suggested that researchers and decision makers of insurance companies use the factors such as customer financial procedure in the purchase, buying pattern and service delivery pattern for calculating customer profitability to fill the gap in this area.
Findings of this study considering the relationship and the impact of customer loyalty in measuring the concept of CLV, are in line with the following studies: Weng Chang (18), Shu-Qing Chen (21), Wu Li (22), Flint et al. (23). In these studies, some indicators of customer loyalty such as (brand, satisfaction, trust, purchase intention) on the CLV have been studied. The difference between the researches mentioned and the research findings can be not using the concept of customer loyalty with comprehensive indicators, as an independent dimension in models and topics related to measuring the customer lifetime value.
In this study, it is suggested that researchers and decision makers of insurance companies, in order to better measure the customer lifetime value of the supplementary health insurance, consider the dimensions and identification criteria in this study.
According to the experimental findings of the research on the relationship and the extent of the effect of value co-creativity with customer lifetime value, it can be claimed that the two concepts are closely related and important to each other.
Due to the lack of application of this dimension in research and pre-measurement models of customer lifetime value, researchers are suggested to consider this dimension along with other dimensions and indicators identified (in the development model) to localize and personalize supplementary health insurance coverage.
The limitations and problems of this research include the impossibility of examining the role of moderators (in terms of complexity, frequency of variables and also the lack of research in this field), the specificity of the research model (only supplementary health insurance) and the impossibility of some interviews. Due to the prevalence of Coronavirus disease (COVID-19).
 The most important finding of this study was to identify the dimensions of measuring customer lifetime value which includes: Customer loyalty, customer profitability, and co-creativity of customer value along with sub-indicators of each of these dimensions, which prevent superficiality in determining customer lifetime value by  accurate measurement of all effective dimensions and indicators, and ultimately increasing the accuracy of the company's predictions, and adopting strategies to eliminate, attract or retain current customers to make it more productive. Implementing the present research model in other industries with the aim of identifying the dimensions of measuring customer lifetime value, comparing factors of measuring customer lifetime value in different industries of the country, identifying the required competencies of managers in insurance companies, reviewing policies, processes and work procedures required in insurance companies and identifying moderating variables in the relationships between the concepts introduced in the present research model can enhance the richness of the model presented and increase generalizability.
Measuring customer lifetime value to implement various strategies requires a deep understanding of such value dimensions as loyalty, profitability, and value creation. Therefore, the dimensions of customer loyalty and customer value co-creation should be taken into consideration as effective dimensions in predicting and measuring the concept.
The authors would like to express their gratitude to the faculty members and managers of the insurance industry who have assisted us in this research.
Conflict of interests   
Authors declared no conflict of interests.
Authors' contributions
Karimi F, Dehghani Ghale R, and Ghorbani Dinani H designed research; Dehghani Ghale R conducted research; Dehghani Ghale R analyzed data; and Dehghani Ghale R wrote the manuscript. All authors read and approved of the final manuscript.
Non applicable.
Type of Study: Applicable | Subject: Healt care Management
Received: 2021/05/15 | Accepted: 2021/10/22 | Published: 2021/10/22

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