P. Tomczyk, T. Doligalski, P. Zaborek, Does customer analysis affect firm performance? Quantitative evidence from the Polish insurance market, “Journal of Business Research”, vol. 69, iss. 9, p. 3652–3658, September 2016, doi:10.1016/j.jbusres.2016.03.026

The full version of the article is available on the website of the Journal of Business Research

Abstract

The paper explores the relationship between conducting customer analysis and financial performance. The data for the study were collected from 590 small insurance intermediaries in the Polish market. The structural equation modelling indicates that the strongest predictor of financial performance was the use of formalized knowledge processing, followed by the scope of performed customer analysis. Other factors positively correlated with financial standing included earning most revenues from corporate clients (versus consumers) and employing policies aimed at regaining former customers. The study also finds that add-on on selling was not significantly associated with better financial results.

Keywords: customer analysis, customer lifetime value, marketing performance, financial performance, insurance, Poland

1. Introduction

Marketing performance measurement remains at the center of academics’ and practitioners’ attention (Clark, 2000; Kohli & Jaworski, 2009; Gupta & Zeithaml, 2006; Lehmann 2004). Despite the strong theoretical basis, many aspects of the relationship between marketing performance and general firm performance remain unclear, which could be in part ascribed to inherent difficulties with quantifying marketing effort. O’Sullivan and Abela show that the ability to measure marketing performance has a significant impact on firm performance and the relative importance of the function of marketing among other departments of the company (2007). Homburg, Artz and Wieseke found that the relationship of comprehensive marketing performance systems with a company’s performance is conditional in terms of both internal and external influences. They also summarized the studies on general (not only marketing) performance systems to reveal mixed and inconclusive results concerning links between such systems and a firm’s performance (Homburg, Artz & Wieseke, 2012).

A special role in marketing performance measurement lies with the metrics associated with customers, such as customer profitability and customer lifetime value (Gupta & Lehmann, 2003). Knowing them allows to segment customer portfolio and thus take actions aimed at increasing the value of customer segments. The value of customer portfolio is also an important indicator of a company’s valuation (Gupta & Lehman, 2002).

This paper contributes to the research area of marketing performance measurement. Its purpose is to identify the impact of conducting customer analysis on firm performance. The study was conducted in 2012 among Polish insurance agents, who are known for maintaining direct long-term relationships with customers.

 2. Theoretical background

Customer analysis (CA) represents the extent to which companies collect customer data, measure customer profitability, estimate their lifetime value and intangible benefits they generate or analyze other customer metrics. Customer analysis may serve as the basis for customer portfolio segmentation or identification of key accounts. CA is fundamental in several popular marketing concepts, such as customer relationship management (Winer, 2001; Payne & Frow, 2005), customer equity management (Bruhn et al., 2008), interaction orientation (Ramani & Kumar, 2008) and customer value management (Doligalski, 2015; Tomczyk, 2014). Despite the popularity of this concept, there are few studies indicating how conducting a CA influences a company’s financial performance (FP). Akroush et al. (2011) found that there was a weak but statistically significant association between collecting information about key customers by banks and insurance firms on the one hand and their financial performance on the other. According to another study, customer equity analysis (including analyzing customer profitability, customer economic potential and customer behavioral patterns) was a crucial component of how the utility of CRM systems was viewed by managers (Bruhn et al., 2008). In contrast, negligible or no effects on earnings of various aspects of CA were noted by Ramani and Kumar (2008). This ambiguity of conclusions suggests that the question of how performing a CA affects FP is not sufficiently researched. We hope that the current study makes a useful contribution in its attempt to shed some light on this relationship, which is essential for both marketing theory and practice. In the current study, following guidelines from the literature and our own observations, we conceptualized CA as a second order reflective construct with five first-order latent variables derived from the theory of marketing management and consumer behavior. Below we provide a short overview for each of those variables.

As mentioned, a special role in CA is played by the metrics associated with customers, such as customer profitability and customer lifetime value. Both of them are based on calculation or estimation of cash flows related to the customer. Gupta and Lehman define customer lifetime value as the present value of all future profits generated from a customer (Gupta & Lehman, 2003). Customer profitability is the difference between the revenues earned from and the costs associated with a customer relationship during a specified period (Pfeifer, Haskins & Conroy, 2005). Customer Costs (COS) and Revenues and Earnings (REV) are therefore essential components of CA.

Apart from revenues, customers provide the company with Intangible Benefits (INT), such as recommendations, insights, value co-creation, image-associated benefits or positive product reviews (Bauer & Hammerschmidt, 2005). Although expressing these benefits in monetary terms seems rather difficult, they may be of great importance to the company.

Knowledge of cash flows related to customers, as well as of intangible benefits they contribute, allows customer segmentation, i.e. division of customers into groups according to their characteristics (e.g. profitability). Customer Segmentation (SEG) serves as a basis for differentiation of strategies aimed at different customer groups, which may contribute to a better satisfaction of their needs, and – more importantly from the perspective of firm performance – to increasing their value to the company (Reinartz & Kumar, 2002; Zeithaml, Rust & Lemon, 2001; Storbacka, 1997).

Monetary and non-monetary benefits which current customers generate to a company are often not sufficient to determine the long term value of the customer portfolio. In order to supplement the existing customer database, companies need to look at prospective clients in terms of their acquisition probability (Thomas, 2008) and future benefits from retention (Rossetetal, 2003). These aspects of CA were operationalized as the last element of our measurement model labeled Prospective Customers (PRO).

Customer analysis is a part of customer relationship management. Below we present four additional factors which can influence firm performance and serve in our model as mediating and moderating variables. Three of them are activities constituting CRM strategy, the fourth is the type of customer served.

CA is closely interrelated with Formalized Knowledge Processing (FKP). The latter is understood here as a construct representing systematic acquisition, interpretation and utilization of knowledge about customers. It can be argued that FKP supplements CA: while CA captures above all the scope of analysis, FKP implies the quality of information due to the systemic manner of its generation. There are many studies indicating positive influence of customer knowledge on different aspects of firm performance (Fidel, Schlesinger & Cervera, 2015; Salojärvi & Sainio, 2010). In order to make it happen the customer knowledge should be systematically acquired and processed (Reinartz & Kumar 2006, p. 193)

CA resulting in customer profiling may serve as the basis for Add-On Selling (AOS). These practises, including cross- and upselling, are often presented as an effective tool to increase customer equity (Blattberg, Getz & Thomas, 2001, pp. 95-123). Also, educating customers may increase their propensity to use more sophisticated products, thus leading to higher performance. Despite their popularity, research on both add-on selling (Jarrar & Neely 2001) and customer education (Bell, Eisingerich 2006) shows mixed and inconclusive results. Successful add-on selling may however lead to increased customer retention (Kumar & Reinartz, 2006, pp. 59–75), which in turn exerts significant influence on firm performance (Best, 2009, p. 81).

An integral part of CRM strategy is maintaining a register of former customers in order to take actions oriented at regaining them (Kumar et al., 2008). The ex-customers may possess a high bargaining power and demand adjusting the value proposition or expect a more attractive price level than what the current provider offers. Aggressive pricing and high reacquisition cost may make the so called second lifetime value (SLTV) to be negative. Hence Thomas et al. argue that reinitiation efforts require solid analytical justification (2004). On the other hand, successful winning back customers may positively influence a company’s performance (Homburg, Hoyer & Stock, 2007). We introduce such actions to the model as Regaining Former Customers (ROC).

The type of customers served may also influence relationship between strategy and a company’s financial performance. Corporate Customers (CC) usually display more relational behavior and require more sophisticated insurance products than consumers. Managing and maintaining loyal business customers can offer greater revenue for a service provider (Rauyruen & Miller, 2007). Hence, companies may be more willing to develop knowledge of them and to increase their lifetime value (e.g. through add-on selling or regaining former clients). Corporate customers, however, can leverage greater bargaining power and may require more favorable conditions, thus leading to lower profitability

3. Hypotheses

Following the guidelines from our literature review we propose that:

H.1:   Customer Analysis is positively correlated with Financial Performance.
H.2:   Formalized Knowledge Processing is positively correlated with Financial Performance.
H.3:   Higher levels of Customer Analysis coincide with stronger reliance on the activities related to Add-on Selling and Regaining Former Customers.
H.4:   Add-on Selling is positively correlated with Financial Performance.
H.5:   Regaining Former Customers is positively correlated with Financial Performance.
H.6:   Higher levels of Formalized Knowledge Processing are associated with stronger reliance on Add-on Selling and Regaining Former Customers.
H.7:   The incidence of CA, FKP, RFC and AOS is higher among insurance intermediaries with a majority of business customers.
H.8:   Insurance intermediaries who mostly service business customers tend to have higher Financial Performance.

In the next part of the paper we outline the research procedure that was employed to test the hypotheses set.

 4. Research Model

Data for the study were collected from owners of small insurance firms that operated as independent intermediaries in the Polish market. The respondents employed no more than 9 persons and offered coverage to both institutional and individual customers, providing a whole range of popular policies, such as casualty, automobile, life and property, underwritten by large companies. In total, 590 complete questionnaires were obtained through the CAWI method between October and November 2012. The sample was drawn from an extensive database that included insurance agents from across the whole of the country, affording an adequate representation of this part of the insurance industry in Poland.

The questionnaire comprised 18 Likert scale items for identifying various aspects of customer analysis practices. The particular statements were adopted from previous studies on similar topics to account for all crucial aspects of CA that were relevant to insurance intermediaries. The other 9 Likert-type items were designed to represent essential indicators of the RFC, FKP and AOS constructs.

The statistical procedures included exploratory factor analysis and structural equation modeling performed by means of software packages SPSS 22 and AMOS 22

5. Research Outcomes

As a first step, we performed an exploratory factor analysis (EFA) with the maximum likelihood estimation method and oblique rotation of the factor matrix. The resultant EFA solution substantiated our initial theory-driven assumptions that the proposed set of Likert scale items did measure five distinct dimensions of CA (we chose not to provide detailed outputs of the EFA due to the character limit in the paper). These dimensions were labeled: Customer Costs (COS), Revenues and Earnings (REV), Intangible Benefits (INT), Customer Segmentation (SEG), and Prospective Customers (PRO). The specific meaning of each hidden variable can be determined from Table 1 by looking at their associated indicators. The EFA also demonstrated that the other three constructs were adequately reflected in their manifest variables, which all had factor loadings of more than 0.5.

The pattern of hidden and manifest variables, as set out in Table 1, was replicated in the subsequent structural equation model that was used to explore the research hypotheses.

 

Table 1: Operationalization of hidden variables in the model

Hidden variable name	Item designation	Item content
Subconstructs of Customer Analysis
Customer Costs (COS)	COS1	We collect information about costs of acquiring each customer
	COS2	We collect information about costs of servicing each customer
	COS3	We estimate acquisition costs of each customer
	COS4	We estimate future costs of servicing each customer
Revenues and Earnings (REV)	REV1	We gather information about revenues from providing service to each customer
	REV2	We estimate future revenues from selling to each customer
	REV3	We estimate expected profits from each customer
Intangible Benefits (INT)	INT1	We collect information about referrals and recommendations from each customer
	INT2	We collect information about preferences of each customer
	INT3	We try to learn behavioral patterns of our customers
	INT4	We assess the value of information provided by customers
	INT5	We identify the value of image gains from selling to a given customer
Customer Segmentation (SEG)	SEG1	We categorize our customers according to estimated future benefits from cooperation
	SEG2	We make our decisions to cooperate with customers based on estimated amounts of future benefits
	SEG3	We give up servicing those customers who fail to bring in expected benefits
Prospective Customers (PRO)	PRO1	We determine the likelihood of acquiring each customer
	PRO2	We estimate the length of likely cooperation with each customer
	PRO3	We evaluate likely benefits from cooperating with each customer
Other constructs
Formalized Knowledge Processing (FKP)	FKP1	We plan our process of acquiring customer information
	FKP2	We process customer information in a systematic way to obtain various displays, comparisons and summaries
	FKP3	We use specialized software for managing customer relations (CRM)
Add-On Selling (AOS)	AOS1	We look to sell additional insurance products to existing customers
	AOS2	We encourage customers to increase the value of their current insurance products
	AOS3	We actively educate customers about our insurance offer
Regaining Former Customers (RFC)	RFC1	We maintain a register of former customers
	RFC2	We contact our former key customers to convince them to return
	RFC3	We have special offers for former customers who decide to return

Source: Own elaboration

 

The structural equation analysis conceptualized the involvement in customer research as a second order reflective construct expressed through five first order subconstructs, as shown in Table 1. To approximate financial performance of respondents’ companies we used an index variable obtained by taking the arithmetic average of scores on the five Likert-type statements:

• Mean earnings per customer that we have obtained this year are greater than in the previous year.
• We expect that the mean earnings per customer will be higher the next year than in the present one.
• The average monthly commission that we earned this year was greater than last year.
• Our total profits this year were greater than the previous year’s profits.
• In the current calendar year we managed to reach our profit targets.

 

This way of computing FP was grounded in the assumption that financial outcomes were a formative construct, and taking a mean is one of the common ways to incorporate this type of variable in a structural equation model (Temme et al., 2014).

The single exogenous variable in the SEM model was Corporate Customers which was a binary characteristic informing if a company had more than half of revenues from business clients (coded as 1; otherwise it was 0).

The model’s diagram and its standardized parameters are depicted in Figure 1.

Figure 1: Structural equation model of customer information related determinants of financial performance (n=581)

(bold characters represent significant standardized regression weights and correlation coefficients; italics show squared multiple correlations)

 

 

 

 

 

 

 

 

The full resolution figure. Source: Own elaboration.

 

To assess the model fit with empirical data a set of standard diagnostic metrics was obtained and set out in Table 2.

Table 2: Overall diagnostics for the structural model

Metric	Value	Threshold for a well-fitting model
Chi-square/df (relative chi-square)	2.801	<2 for good fit, <3 for acceptable fit
p-value for the model	<0.001	>0.05
GFI (goodness of fit index)	0.904	≥0.9
CFI (comparative fit index)	0.924	≥0.9
AGFI (adjusted goodness of fit index)	0.870	≥0.8
RMSEA (root mean square of approximation)	0.056; HI90=0.060	≤0.05 for good model fit; ≤0.08 for adequate fit; in addition, the upper 90% confidence limit (HI 90) should be no more than 0.08 for a well-fitting model

Source: Own elaboration. Cutoff points based on Garson (2012).

 

The table seems to suggest that according to the majority of the metrics the model fits the empirical data adequately. The only exception being the chi-square test, which is significant and leads to rejecting the null hypothesis of the lack of differences between the observed covariance matrix and the one recreated from the model. However, for large sample sizes, which result in increased sensitivity of all statistical tests, this measure is considered unreliable and could be disregarded if other metrics imply a well-fitting solution, which was the case with the current analysis (Byrne 2010, pp. 76–77; Bowen & Guo 2012, p. 142).

The next table centers on individual first-order constructs and subconstructs and offers insights into their internal reliability (Cronbach’s Alpha), convergent validity (AVE, or average variance extracted) and discriminant validity (MSV, or maximum shared variance).

 

 

Table 3: Reliability and validity measures of component constructs in the structural model

Construct	Cronbach’s Alpha	AVE	MSV
Costs	0.911	0.693	0.436
Revenues and Earnings	0.848	0.537	0.436
Intangible Benefits	0.854	0.544	0.423
Customer Segmentation	0.771	0.550	0.372
Prospective Customers	0.702	0.440	0.314
Regaining Former Customers	0.718	0.513	0.185
Formalized Knowledge Processing	0.796	0.503	0.360
Add-On Selling	0.759	0.520	0.185

Source: Own elaboration.

All hidden variables display sufficient internal validity, with Cronbach’s alphas greater than 0.7 (Malhotra, 2014, p. 287). In terms of convergent validity, seven hidden variables had AVE values above a recommended threshold of 0.5 (Hair et al., 2010). The subconstruct Prospective Customers had the AVE metric lower than 0.5, which suggests that above half of the variance in manifest variables was accounted for by other causes, not included in the model. One such external factor could be the mass-market strategy followed by a company, which might be fairly independent from how companies manage their customer relations. Such a low value of AVE may justify removing the construct from the solution, but seeing that collecting and processing information from would-be clients seems to be a useful complement of the other areas of customer analysis, we decided to keep it. In terms of discriminant validity, which considers if factors were explained better by their own indicators than the indicators for other factors, the metrics do not signal any obvious problems, since MSV values are lower than AVE for each of the constructs (Hair et al., 2010).

It can be noted that the path diagram has three correlated error terms, which were introduced to obtain gains in the model fit. This is an acceptable practice if the linked error terms are for manifest variables within the same construct but not across different constructs (Mulaik, 2009, pp. 342–345).

On the whole, the model seems to be an adequate representation of empirical data, which warrants its further use in hypothesis tests and interpretations

6. Discussion of the findings

The SEM model indicates that the entire range of predictors linked to the various aspects of customer information collection, processing and use accounts for about 25% of variance in the self-reported index of Financial Performance. The strongest factor seems to be the use of Formalized Knowledge Processing, which has a standardized regression weight of 0.24. This variable could be interpreted as a proxy for the quality of customer information, which is determined by how regular and systemic data collection and processing is. Customer Analysis, which represents the scope and extent of collected information, is the second strongest predictor of Financial Performance (beta=0.19). Thus, it seems that quality of information appears to be of more consequence for financial standing than the sheer amount and diversity of acquired data. It should be noted, though, that CA and FKP are correlated (R=0.6), which indicates that those two aspects of information policies are integral parts of any marketing information system, and the firm should develop both of them in parallel for the best effects. The above findings lent support to hypotheses H.1 and H.2.

Add-On Selling and Regaining Former Customers are two activities that are strongly dependent on the amount and quality of insights about customers (this claim is validated by positive and significant correlation coefficients between those variables and both FKP and CA). However, the model shows that only measures aimed at former customers make a positive difference in Financial Performance; it seems that more forceful Add-On Selling is not significantly associated with better financial results, meaning that the effort spent on fielding such methods could be wasted. In terms of the research hypotheses, these results were somewhat mixed, failing to support H.4 but validating H.3, H.5 and H.6.

The last element of the model is a binary variable that differentiates between insurance intermediaries that mostly serviced companies (coded as 1) and those whose customers were in majority individuals (0). This attribute was positively linked to CA, CKM and RFC, meaning that providing services to businesses coincided with higher levels of each of these variables, ceteris paribus. On the other hand, regardless of the type of customers, the insurance intermediaries seemed to show similar reliance on AOS. Most importantly, firms that sold insurance mostly to business customers tended to be better off financially (beta=0.12). As such, it appears that H.8 was fully supported, but H.7 only partially so (excluding the lack of positive link between the Corporate Customers variable and AOS).

Our research indicates direct, as well as FKP mediated, influence of performing customer analysis on financial outcomes. This is an important finding, especially when set against previous research indicating mixed empirical evidence on the relationship between measurement systems and firm performance. The pertinent previous studies often concern big companies, with complex organizational structures and functions, operating in mass markets, and focused on market share rather than on developing customer relationships. (Homburg, Artz & Wieseke, 2012; O’Sullivan & Abela, 2007; Fleming, Chow & Chen, 2009). Conversely, the present research looks at the problem from a different perspective, as the investigated firms were different from previous studies because they encompassed small entities, usually microenterprises with less than 9 employees, operating in business models of low complexity based on selling services. Many of typical marketing decisions (such as branding or product innovations) were not in their power as they were taken at a higher level of insurance companies. The vast majority of surveyed firms offered similar range of products and did not benefit from product superiority. Thus, they were highly concentrated on developing close relationships with their customers.

Customers in the Polish insurance market are rather price sensitive, especially in popular categories which are highly commoditized (e.g. car or property insurance). Their profitability may vary, though, according to the quality of the relationship and their bargaining power. Arguably, these market conditions make performing customer analysis more viable as a marketing tool. Knowledge acquired in this way may serve as the basis for customer portfolio segmentation, allowing differentiation of actions aimed at specific customer groups. This in turn could contribute to a better fulfilment of their needs, while increasing their value to the company.

One of our paper’s contributions is to add to the understanding of customer knowledge management. Customer knowledge is a broad term and according to Khodakarami and Chan (2014) includes: knowledge for customers (provided to customers to satisfy their needs), knowledge about customers (referring to their relationships with a company) and knowledge from customers (knowledge of their preferences and attitudes). In our studies, CA represents knowledge about customers, as it refers to interactions between customers and the company. We demonstrate that such knowledge is positively correlated with company’s performance. This finding may be perceived as complementary to research by Salojärvi and Sainio (2009), who found that acquisition and utilization of customer knowledge is positively correlated with key account performance (i.e. satisfaction, profitability, annual sales, share of key account’s total purchases). In their study, the customer knowledge is treated as a whole and the question which types of that knowledge are particularly critical was left unanswered.

Findings of Khodakarami and Chan (2014) indicate that various classes of CRM systems provide high level of support for creation of knowledge about customers. Those computerized systems are highly formalized and tend to provide better quality information than less systematic data-gathering efforts. In our research the metric for the level of formalization and the amount of planning in collecting customer data was the FKP variable. Assuming that higher formalization and more careful planning amount to more accurate, complete and relevant information, this variable can be taken as a proxy for customer knowledge quality, while CA encapsulates the scope and completeness of that knowledge. Our findings indicate that FKP can markedly influence FP. The positive correlation between CA and FKP may point at a feedback loop between the systemic approach and the scope of acquired customer insights; it seems that the more information a company collects the better organized the collection and processing system tend to be, and – on the other hand – better organized systems encourage gathering richer and fuller information. Seeing that the relationship between FKP and CA is probably bidirectional (forming the feedback loop) neither variable is a single or even dominant cause in this association; rather both are sequentially causes and effects in a sort of a self-reinforcing virtuous circle. Considering both positive links between FKP and CA, and FKP and FP, FKP emerged as a single most important information-related factor in determining financial outcomes. This would lend support to a recommendation that small-sized insurance intermediaries should be well served by adopting a CRM system, which the majority of our respondents did not have (65.6%). As shown by Khodakarami and Chan (2014), a well-chosen and correctly implemented CRM system contributes to a wider gathering of transactional and demographic data. Transforming this data into knowledge about customers, which positively improves company’s performance and creates the basis for competitive advantage (Garcia-Murillo & Annabi 2002), could be also relevant in the insurance industry, among small intermediaries offering various standardized insurance products. Such a knowledge management system leads to increased effectiveness of many marketing practices, including actions such as AOS and RFC.

Our findings indicate that in dealing with more precious although less numerous business clients, insurance providers tended to adopt more advanced ways of operating than when dealing with individuals. With businesses client analytics was used more intensely, processing systems were apt to be more deliberate and formalized, and the attempts at regaining former customers were more frequent. Interestingly, the use of add-on selling was no different regardless of the type of customers, both in terms of frequency and financial impacts. Servicing other companies seemed to be more profitable than individuals, as respondents who had a majority of businesses among their clients, on average, reported better financial results.

On a more general note, the research outcomes clearly substantiate the notion of developing a customer-centric performance measurement system. However, even though a certain amount of benefits should be enjoyed by most businesses their exact scale is dependent on a set of contextual factors. As such, the utility of an information system should be greater if (1) the company is focused on the customer rather than the product portfolio, (2) customers can be viably identified and data about their behavior and attitudes reliably recorded, (3) the dominant kind of exchange is relational (as opposed to transactional), (4) customers are diversified in terms of profitability and intangible benefits brought to the company, (5) customers have high bargaining power, and (6) the industry was commoditized (Doligalski, 2015, p. 10–14). Arguably those conditions were met for many members of the Polish insurance industry included in our sample

7. Limitations and suggestions for further research

As with any research project, ours is not without its limitations. The first issue of concern is the used measure of financial performance, which relies on opinions of respondents about several aspects of their business success in the previous year. Although the statements representing those opinions – given earlier in the paper – were quite easy to understand and answer, they do not offer the accuracy of actual, validated financial metrics. Even though, as we strongly believe, the single composite FP index was a good approximation of the relative financial standing of the surveyed firms, its lack of natural measurement units (e.g. dollars or euros) constraints the utility of the findings. For instance, it would be difficult to estimate how exactly the profits would change if a company increased its involvement in CA by, say, 10%. Despite this apparent lack of precision, the directions of relationships and effect sizes discussed earlier seem to be reliable.

Another limitation concerns the scope of the research, which despite employing a large sample size for this type of study concerned only one country, Poland, which is still an emerging economy, has its own specific market structure and dynamics. That could possibly call for caution if generalizations were to be made to other countries where customers are more affluent and more accustomed to purchasing a wider scope of insurance policies. It could be argued, though, that customers in developed countries, who show more appreciation of the value of insurance and display keener interest in more complex and comprehensive products, could offer better chances of forming a productive, long-term relationship (EY, 2014). As such, although the outcomes for a similar study replicated in developed countries may not be the same, we expect that they are likely to show correlations of similar directions but with stronger effect sizes. Considering that the insurance industry shares a lot of similar features as several other types of services (a case in point being banking services) it is reasonable to anticipate similar interdependencies there. Also, the manufacturing industry involved in products with a service component, or the so called Product Service Systems (PSS) (van Halen et al., 2005, p. 21), could follow similar patterns, due to high added value of constructive, individual firm-customer relationships.

For those reasons, it would be interesting to repeat the study in different countries and/or in different industry settings to see how universal are the positive links between acquiring viable customer information and the bottom line.

 

References

1. Akroush M., Dahiyat S., Gharaibeh H., & Abu-Lail B. (2011). Customer Relationship Management Implementation. An Investigation of a Scale’s Generalizability and its Relationship with Business Performance in a Developing Country Context. International Journal of Commerce and Management, Vol. 21 No. 2, 158-191. DOI 10.1108/10569211111144355
2. Bauer M., Hammerschmidt M. (2005). Customer-based corporate valuation. Integrating the concepts of customer equity and shareholder value. Management Decision, Vol. 43 No. 3, 331-348
3. Best R., Market-Based Management, Prentice Hall, 2013, (Chapter 2)
4. Blattberg, R., Getz, G., & Thomas, J. S. (2001). Customer equity: Building and managing relationships as valuable assets. Boston, MA: Harvard Business School Press, (Part 2)
5. Bruhn M., Georgi D., & Handwich K. (2008). Customer Equity Management as Formative Second-order Construct. Journal of Business Research. DOI:10.1016/j.jbusres.2008.01.016
6. Clark B. (2000). Managerial Perceptions of Marketing Performance: Efficiency, Adaptability, Effectiveness and Satisfaction, “Journal of Strategic Marketing”, 8, 3-25
7. Doligalski T. (2015). Internet-Based Customer Value Management, Springer, (Chapter 1)
8. Eisingerich A., Bell S. (2006). Journal of Financial Services Marketing 10.4 , 86-97.
9. Gupta S., Lehmann D. (2003). Customers as Assets, Journal of Interactive Marketing, Vol. 17, No. 1, 9-24
10. Gupta S., Ziethaml V. (2006). Customer Metrics and Their Impact on Financial Performance. Marketing Science, Vol. 25, No. 6, 718-739
11. Homburg C., Hoyer W., & Stock R.M. (2007). How to get lost customers back? Journal of the Academy of . Marketing Science 35, 461–474
12. Homburg Ch., Artz M., & Wieseke J. (2012). Marketing Performance Measurement Systems: Does Comprehensiveness Really Improve Performance?, Journal of Marketing, Vol. 76, No. 3, 56-77.
13. Jarrar Y. , Neely A. (2002) Cross-selling in the financial sector: Customer profitability is key, Journal of Targeting, Measurement and Analysis for Marketing 10, 282–296
14. Kohli A., Jaworski B. (1990). Market Orientation: The Construct, Research Propositions and Managerial Implications. Journal of Marketing, Vol. 54, 1-18
15. Kumar V., Venkatesan R., Bohling T., & Beckmann D. (2008). The Power of CLV: Managing Customer Lifetime Value at IBM. Marketing Science, Vol. 27, no. 4, 585-599
16. Lehmann D. (2004) Metrics for Making Marketing Matter. Journal of Marketing, Vol. 68, 73-75
17. O’Sullivan D., Abela A.V. (2007). Marketing Performance Measurement Ability and Firm Performance. Journal of Marketing, Vol. 71, No. 2, 79-93
18. Payne A., Frow P. (2005) A Strategic Framework for Customer Relationship Management, Journal of Marketing, Vol. 69, 167-176
19. Pfeifer P, Haskins M., & Conroy R. (2005). Customer Lifetime Value, Customer Profitability and the Treatment of Acquisition Spending, Journal of Marketing Issues, Vol. XVII Number 1, Spring 2005, 11-25
20. Pilar F., Schlesinger W., & Cervera A. (2015). Collaborating to innovate: Effects on customer knowledge management and performance, Journal of Business Research, Volume 68, Issue 7, 1426–1428 http://dx.doi.org/10.1016/j.jbusres.2015.01.026
21. Ramani G.,& Kumar V. (2008). Interaction Orientation and Firm Performance. Journal of Marketing, Vol. 72, 27-45
22. Rauyruen P., Miller K., & Barret N. (2007) Relationship Quality as a Predictor of B2B Customer loyalty, Journal of Business Research, 60(1), 21-31, doi:10.1016/j.jbusres.2005.11.006
23. Reinartz W., Kumar V. (2006). Customer Relationship Management: A Databased Approach, Wiley, (Part 1)
24. Reinartz W., Kumar V. (2002). The Mismanagement of Customer Loyality, Harvard Business Review, s. 86-94
25. Rosset S., Neumann E., Eick U., & Vatnik N. (2003). Customer Lifetime Value Models for Decision Support, “Data Mining and Knowledge Discovery”, 7,. 321-339
26. Rust R., Zeithaml V., & Lemon K. (2000). Driving Customer Equity, The Free Press, New York, (Part III)
27. Temme D., Diamantopoulos A., & Pfegfeidel V. (2014). Specifying formatively-measured constructs in endogenous positions in structural equation models: Caveats and guidelines for researchers. International Journal of Research in Marketing, 31, 309-316
28. Storbacka K. (1997). Segmentation based on customer profitability — retrospective analysis of retail bank customer bases, Journal of Marketing Management, 13:5, 479-492
29. Thomas J.S., (2001). A Methodology for Linking Customer Acquisition to Customer Retention. Journal of Marketing Research, Vol. 38, No. 2., 262-268
30. Tomczyk P. (2014).Multi-Method Analysis (in:) Ghorbani A, Takhar A, (Eds). Market Research Methodologies: Qualitative and Multi-Method Approaches, (pp. 184-198) IGI Global, DOI: 10.4018/978-1-4666-6371-8.ch012
31. Winer R.S. (2001). A Framework for Customer Relationship Management, California Management Review, Vol. 43, No. 4, 89-105
32. Garson, G. D. (2012). Hierarchical linear modeling: Guide and applications. Sage.
33. Bowen, N.,&Guo, S. (2012).Structural Equation Modeling: Pocket Guides to Social Research Methods. Oxford University Press, New York.
34. Byrne, B. (2010). Structural Equation Modeling with AMOS: Basic Concepts, Applications and Programming,Second Edition, Routledge, New York.
35. Malhotra, N. (2010).Marketing Research: An Applied Orientation, Prentice Hall, 6th ed.
36. Hair, J., Black, W., Babin, B., &Anderson, R. (2010), Multivariate data analysis (7th ed.). Prentice-Hall, Inc. Upper Saddle River, NJ.
37. Mulaik, S. (2009), Linear Causal Modeling with Structural Equations, Chapman & Hall, CRC Press Taylor and Francis Group, Boca Raton.
38. O’Sullivan, D., & Abela, A. V. (2007). Marketing performance measurement ability and firm performance. Journal of Marketing, 71(2), 79-93.
39. Fleming, D. M., Chow, C. W., & Chen, G. (2009). Strategy, performance-measurement systems, and performance: A study of Chinese firms. The International Journal of Accounting, 44(3), 256-278.
40. Khodakarami, F., & Chan, Y. E. (2014). Exploring the role of customer relationship management (CRM) systems in customer knowledge creation.Information & Management, 51(1), 27-42.
41. Salojärvi, H., Sainio, L. M., & Tarkiainen, A. (2010). Organizational factors enhancing customer knowledge utilization in the management of key account relationships. Industrial Marketing Management, 39(8), 1395-1402.
42. Salojärvi H., Sainio L.-M., (2010) “Customer knowledge processing and key account performance”, European Business Review, Vol. 22 Iss: 3, pp.339 – 352, DOI:10.1108/09555341011041010
43. Cees Van Halen, Carlo Vezzoli, Robert Wimmer (2005).Methodology for Product Service System Innovation. Assen: Uitgeverij Van Gorcum
44. EY. (2014). Reimagining customer relationships Key findings from the EY Global Consumer Insurance Survey 2014. EY. Retrieved from http://www.ey.com/Publication/vwLUAssets/ey-2014-global-customer-insurance-survey/$FILE/ey-global-customer-insurance-survey.pdf