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Does Customer Analysis Affect Firm Performance? Quantitative Evidence from Polish Insurance Market

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)

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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.

 

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Internet-Based Customer Portfolio Building

 

Paper accepted for 10th International Conference Marketing Trends (Paris, January 20th-20nd 2011).

Download the full pdf version of Internet-Based Customer Portfolio Building.


Abstract

This paper presents a conceptual model of Internet-based customer portfolio building. The proposed model refers to the concept of value exchange between a company and a customer, as well as to the concept of delivering values to customers. The model is made up of five stages: defining, creating, communicating and delivering values, as well as generating values for a company.

Key Words

Customer portfolio, customer relationship management, CRM, e-CRM, customer value management, customer relationships, online, Internet, e-commerce, e-business, value proposition, customer value, customer loyalty, customer trust, value exchange.

Introduction and Objectives

Contemporary companies compete in many markets. The most important of these is the market of customers. Customers provide companies with many values, among them revenues. Revenues attract participants of other markets such as shareholders, suppliers or potential employees. The area which contributes to value creation in companies migrates from fixed assets and production, to customer relationships and other market-based assets. Therefore, customers in most markets become an asset of great value to companies. Successful development of this asset through long-term and profitable customer relationships contributes to the growth of companies’ value and profits.

Changes in the theory and practice of management are accompanied by social and technological transformations. The Internet and IT have added a new dimension to customer relationships. Contrary to traditional media, the Internet has become an environment that facilitates multi-sided communication, searching for information, conducting transactions and even co-creating values. Hence, companies are able to develop to greater extent Internet-based relationships with customers, as it allows companies to grasp such benefits as cost reduction, increasing values for customers, or the acquisition of new customers.

The combination of these two areas, customer relationships and the use of the Internet, creates a great challenge for marketers and entrepreneurs. The purpose is to create long-term and profitable customer relationships, often in markets hardly accessible through traditional channels. This challenge requires a new orientation adapted to the online environment. The experiences of companies from the period of “the Internet bubble” prove also that the question of how to develop customer relationships on the Internet should still be raised.

This paper is an attempt to answer this question by proposing a model of Internet-based customer portfolio building. The proposed model refers to many marketing concepts such as value exchange perspective, market-based assets and competences, trust building, customer portfolio segmentation and customer switching costs.

Literature review

The customer relationship is presented as value exchange both in classical (Bagozzi 1975, McCarthy and Brogowicz 1981), and more recent marketing approaches (Miller and Lewis 1991, Jackson 2007, Cheng 2009). In the process of value exchange companies provide customers with values, receiving in return other values from customers. The concept of “values for customers” appears in many marketing and managerial publications (Drucker 1954, Porter 1980, Payne and Holt 2001).

The concept of values for customers has been a premise for creating models of delivering values to customers (Szymura-Tyc 2005; Kordupleski and Simpson 2003; Baker 2003). The concept of creating values for customers has also been raised in Peppers’ and Roger’s publication, in which they stress the need for individualized and cooperation-based dimensions in the relationship (1997). Cooperation with customers within the process of co-creating values may be the basis for a competitive advantage (Prahalad, Ramaswamy 2004, Tapscott, Williams 2006)

Relationships with customers should be viewed from the perspective of creating values for shareholders. This position is shared by Doyle, who in Value-based marketing subordinates marketing efficiency to maximizing return for shareholders (2004). The approach to customer relationships as to managing company’s assets has been described by Blattberg, Getz and Thomas in Customer Equity: Building and Managing Relationships as Valuable Assets (2001). Gupta and Lehman in Managing Customers as Investment proved the relationship between customer value and corporate valuation (2005). This position finds its reflection in the fifth stage of the proposed model, which is generating values for a company.

The concept of customer portfolio relatively rarely appears in marketing literature. In the article Customer Portfolio Management: Toward a Dynamic Theory of Exchange Relationships Selnes and Johnson exhibit dynamics of relationships between various segments of customers and their influence on marketing efficiency (2004). Portfolio dynamics have also been investigated by Homburg et al (2009).

In the area of developing customer relationship in the online environment there is still a lack of publications explaining this process. Amit and Zott show the source of value creation in e-business (2001). Verona and Prandelli investigate affiliation and lock-in as the source of competitive advantage of Internet companies (2002).

The concepts of value exchange and customer portfolio

In the process of value exchange companies provide a customer with values (a set of values, composition of values), receiving in return other values from a customer. The values for customers can be differently categorized. The simplest categorization includes functional and symbolic values. Xu and Cai built an e-commerce value model based on three components: the outcome value, the process value, and the shopping enjoyment (2004). Cheng et al. while researching perceived customer values in e-commerce used a modified categorization of Sheth et al. (1991), which included functional, social, emotional and epistemic values (2008).

The author proposes the following categories of values for customers delivered in the online environment: value of purpose, convenience, individualization, communications, community and safety (Figure 1.). Value of purpose results from the company’s competence to satisfy customer needs according to his or her expectations. Value of convenience refers to the customer’s perceived easiness to initiate and continue a relationship with a company. Value of individualization describes the customer’s ability to customize the received values to his or her needs. Value of communications refers to the company’s ability to provide a customer with information, which he or she currently expects, as well as the ability to listen to a customer. Value of community is the benefit resulting from communicating or interacting in a different way with other company’s customers (e.g. users of the same product). Value of safety refers to the low level of risk related to a relationship with a company.

Viewing the value exchange from the perspective of values delivered to a company by customers, the author distinguished the values in the following dimensions: revenues; data, information and knowledge; values for another group of customers (on a multi-sided market); contribution to co-creating a value process; word of mouth; company’s image; economies of scale; network effects.

Fig. 1. Conceptual model of a value exchange between a customer and a company on the
Internet

Fig. 1. Conceptual model of a value exchange between a customer and a company on the Internet

value-exchange

Among the most common types of value exchange the author distinguishes: initial, non-monetary and monetary. The essence of the initial value exchange is providing potential and current customers with information on a company and its products, trust building and enabling contact with a company. This pattern of value exchange is followed by many companies that use their websites mainly for the purpose of customer acquisition, and later develop customer relationships through traditional channels. Within the non-monetary value exchange the company offers customers some free values (e.g. articles, podcasts, videos) or services (e-mail, search engine, content publishing). The company displays advertisements to its users, and in consequence it is able to create value for another group of customers, who are advertisers and who provide the company with revenues. The monetary exchange consists of delivering values to customers, which they pay for. Companies that participate in the monetary value exchange are not only online stores, but also companies offering paid content (e.g. newspaper archives, music files) or rendering paid services (e.g. banks, financial brokers).

A customer can be defined as a company’s stakeholder who participates in an exchange of values with a company in order to have his or her needs satisfied. Consequently, a customer may not necessarily provide a company with revenues, which is exhibited by the non-monetary value exchange.

Customer portfolio is the sum total of any given company’s relationships with its customers. Customer portfolio can be described with various variables. Probably the most important is its value, which is the discounted value of benefits, which the company may derive from it, reduced by costs related to it. The other important variable is the structure of the portfolio. The structure of the portfolio can be viewed from such perspectives as values expected from, or values delivered to, a company.

From managerial perspectives information concerning customers’ responses to company’s activities is important, such as customer satisfaction, loyalty, trust or involvement. Information on the course of relationships with customers is also important (durability, frequency of purchasing or consuming values from a company). Another important category of information is the total risks resulting from customer relationships and incurred by a company (delays in payments, higher costs, revealing confidential information).

Customer portfolio building is the total of a company’s actions aimed at increasing values derived from the portfolio and resulting in values for stakeholders. Customer portfolio building should usually guarantee diversified streams of values in the long term, as various values are necessary for different operations of a company. Revenues have an effect on profitability and liquidity and as a consequence enable companies to deliver values for other stakeholders. Customers also provide a company with data, information and knowledge, which make it possible for the company to understand better their needs and thus develop innovations aimed at increasing values for customers or decreasing company’s costs. Customers recommendations passed in informal communication (word of mouth) are also important, as well as the positive image values resulting from a better perception of a company due to providing service to a prominent customer.

 

 

The model of Internet-based customer portfolio building

 

The first stage in the process of customer portfolio building is defining values, when the value proposition and target group are selected (Figure 2.). The essence of the next stage, which is creating value, is to provide the company with assets, competences and other managerial solutions necessary for the offering the chosen value proposition. Communicating values is the third stage in the process of Internet-based customer portfolio building. It consists of attracting customers to the company and trust building. Delivering values is a continuous stage including customer segmentation, value exchange, involvement and loyalty building. Customer segmentation aims at distinguishing groups of customers who share common or similar characteristics and who react to the company’s actions much the same way. The value exchange is a transactional part of the relationship during which customers receive values from the company and in return deliver other values to it. Involvement building consists of increasing the scope of values which are the subject of exchange. Loyalty building is understood as increasing customer willingness to continue the relationship with the company. The final stage in the proposed model is generating value for the company, which underlines the need to create values for shareholders based on marketing activities.

Fig. 2. Conceptual model of Internet-based customer portfolio building

 

internet-customer-portfolio

Defining values

Defining values is the first stage in the process of customer portfolio building, when decisions regarding the value proposition and the target group selection are taken. The purpose of defining value is to achieve a sustainable distinctive advantage, which is described by Doyle and Stern as a “perceived difference that leads customers in the target segment to prefer one company’s offer to those of others” (2006).

In order to select a target group selection, market segmentation should first be performed. Remaining within the concept framework of the value exchange, dimensions of segmentation can be described as values expected by customers, values delivered by customers to a company and the use of the Internet. The first dimension is closely related to the customer’s needs and expectations, the second to their potential for the company. Many studies show that the use of the Internet and adoption of Internet-related services such as e-commerce or social networking is diverse within the population of Internet users (Monroe, Sinclair, Wachinger 2009, Forrester Research 2010). Therefore, the need to take into consideration this dimension also arises.

In companies that develop customer relationships through traditional channels, adding the Internet as another marketing channel usually does not exert a breakthrough influence on the target group selection. The target group is then broadened by new customers, who, due to various constraints, have not taken advantage of company’s offer through traditional channels. These new customers might not have chosen the company’s offer before due to unfavourable location, or lack of time or mobility.

Companies usually develop relationships with customers who purchase products or services and thus provide companies with revenues and other values. This is the basic model of the value exchange between companies and their customers. The target group selection becomes more complex when a company operates on a multi-sided market, and in the process of the value exchange needs two or more distinct groups of customers (Evans 2003). There are many examples of companies acting on multi-sided markets. Internet portals deliver value to both users and advertisers. Online auctions develop a relationship with both sellers and buyers. Producers of operating systems need hardware producers, software developers and final users.

In the traditional economy a common rule of the value proposition formulation is combining a price level with values for customers. According to this rule, companies offering inferior values charge customers lower prices than companies offering superior values. Hence, several strategies concerning the value proposition can be distinguished (e.g. inferior value – low prices, superior value – high prices). The application possibilities of this rule on the Internet are limited. The rule explains well the strategies of companies taking part in a monetary value exchange, such as online stores. Among them there are companies selling a product with inferior customer service and charging a low price, as well as other companies that enrich the same product with greater customer service and expect higher prices for it.

Referring values to price level may not always be used on the Internet for several reasons. In the online environment many companies offer superior customer values for free. Examples of these companies include newspapers which publish their content free of charge and companies which offer communications services on the Internet, such as e-mail or instant messaging providers. Moreover, according to Kim, on the Internet the strategy of offering superior values and charging high prices is rarely adopted (2004). In the traditional economy this strategy is most often used when marketing high quality, well-branded products to affluent customers.

The value proposition on the Internet is influenced by: product virtualization including partial or entire digitalization and product enrichment in information (Doligalski 2006); network effect, which refers to an increase in values for a customer (utilities) when the number of other customers (users) of that product increases. (Tellis, Yin, Niraj 2009); co-creating values with customers (Prahalad, Ramaswamy 2004) and experience-based consumption (Jiang, Benbasat 2004-5). The author distinguishes strategies of the value proposition on the Internet into the following five: strategy of efficiency, free values, complete customer solutions, unique values and value co-creation. It is worth mentioning that these strategies have been formulated according to different criteria and may be merged (Doligalski 2010).

The efficiency strategy consists of offering values to customers, which are to lower their transactional and other costs, and in this way allow savings of time and money. The examples of companies adopting this strategy are online auctions. Due to supply aggregation they offer a wide range of products, which leads to lowering customer transactional costs within offer search and analyses. Many Internet companies apply the efficiency strategy while offering values related to communications. Solutions such as e-mail services, instant communicators and social networking websites also reduce the transactional costs of a customer.

The free value strategy is based on offering values to customers for which they are not charged. This strategy has been popular since the early years of commercial use of the Internet. Consequently many companies (e.g. newspapers), which usually charge customers in traditional channels, offer the same or similar values for free in the online environment, which in turn leads to problems with generating income. The strategy of free values can be a part of a broader business strategy assuming revenue generation. This can be performed twofold: revenues can be generated by another group of customers on a multi-sided market or the company can charge customers for premium values (the so called freemium strategy).

The strategy of complete customer solutions relies on offering a broad scope of values from certain categories. Internet technologies enable the presentation of a high number of products in online stores, which results from low technological constraints. Therefore, online stores often shape their offer according to the long tail rule, which assumes offering both best-sellers, as well as niche products. Moreover, many companies offer values based on economies of scope. This concept consists of offering products from different categories. An often-quoted example of a complete customer solution is Amazon.com. The company offers a wide range of products (the long tail) including niche products, and at the same time offers products from other categories such as household electronics (the economies of scope).

The next strategy formulated is the strategy of unique values. A company follows this strategy if it offers scarce values on the market. This situation is very attractive, as it allows companies to charge high prices and therefore take advantage of a high margin. The greatest disadvantages of this strategy are difficulties in creating scarce values and then sustaining the scarcity in long term. The adoption of the unique values strategy may result from innovations, privileged access to resources or operating in a niche.

The strategy of value co-creation assumes that customers actively participate in shaping the value proposition, which will be delivered to themselves or to other customers. According to Prahalad and Ramaswamy, this is the value co-creation with customers that is the essence of competition in the modern economy (2004). The scope of the strategy is broad. It includes a situation, in which a customer co-creates the composition of values with a company for him or herself. A customer may also create values orientated not at him or herself, but at other customers. In the first case, customer’s activity can be described as mass customization. According to Kleeman and Voss mass customization refers to “isolated activity of individual customers (..), not to the collective activity of many individuals” (2008). Within mass customization, the value exchange can be described as one-to-one and the participation of other customers is not required. The value co-creation orientated at other customers assumes that a customer actively participates in activities aimed at creating values for other customers. The range of these activities is also wide. They include creating and publishing content on the Internet, interaction and communication with other customers on social networking websites or software development in the Open Source movement.

Creating Values

The essence of creating value is the next stage in providing a company with assets, competences and other managerial solutions necessary for the offering the chosen value proposition.

Firm-specific assets are always closely related to its field of activity. Therefore, it is difficult to list the most common assets. However, one can try to list the Internet-specific assets, which are helpful or necessary in the customer portfolio building process:

  • brand;
  • domain;
  • reputation;
  • opinions of a company or its products published on blogs, chats, forums;
  • hyperlinks leading to a company’s website;
  • informational resources created by a company or its customers;
  • technological infrastructure;
  • relationships with supplier;
  • partnerships with other websites, portals etc.

Internet-specific assets may overlap or include some sub-assets (e.g. brand, reputation, opinions on a company). Internet-specific assets can be perceived from different perspectives such as brand equity, search engine positioning or social media marketing. From a brand equity perspective, there are assets and liabilities that add to or subtract from the value provided by a product or service (Aaker 1991). In search engine positioning, which is aimed at getting the highest position in search engines listings, other assets become crucial. They are links leading to a website, valuable domain, information resources on a website and the proper construction of a website. From the view of social media, marketing essential assets include opinion leaders who promote the company and people who join the company’s profile on a social networking website.

It is difficult to list the most common competences needed for Internet-based customer portfolio building as they are closely related to a company’s core activities. They can be categorized according to the stage of the process. Hence, the competences can be seen from the perspectives of:

  • defining values – understanding customer needs and choosing the appropriate value proposition;
  • creating values – acquisition of assets and competences required to offer the value proposition;
  • communicating values – reaching potential customers with the proper message resulting in their visit to a company’s website, as well as trust building;
  • value delivery – customer service aimed at customer satisfaction, involvement and loyalty;
  • value generation for the company – ability to derive financial benefits from customer relationships.

A similar approach is shared by Zhu, who distinguished four dimensions of e-commerce capabilities (competences), which are: information, transaction, customization and back-end integration (2004). Zhu found that in companies conducting online sales, the e-commerce competences, together with a complimentary factor which is IT intensity, positively mediate firm performance measures such as sales generation, cost reduction, asset return and inventory turnover.

Competences can be perceived also from the perspective of organizational structure. On an abstract level the competences of the Internet pure players, companies which operate only in the online environment, can be described as abilities to deliver values to customers and to satisfy their needs online. In the case of brick-and-click companies, which are multichannel players active both on the Internet and in traditional channels, an essential competence becomes the ability to manage the customer relationships through different channels. According to Lindstroem, merging different channels in the process of customer service has become a major challenge for contemporary enterprises (Lindstroem 2001).

The major assets of Amazon.com include brand, and related to it domain, technology allowing mass customization and seamless order placement, hyperlinks pointing at the company’s websites, customers who trust the company, recommend it and write reviews and the base of tens of millions of reviews written by more than five million customers (Ante 2009). Thanks to this asset, Amazon.com is not only a place where products are sold, but also where decisions on products are taken. Core competences of the company include abilities to provide customers with detailed product information, as well as the proposition of similar products to purchase, abilities to enable a seamless placement of orders, and to deliver products to customers, which requires management of sophisticated logistical processes. Thanks to these assets and competences Amazon.com was able to widen the range of products sold from books to various categories and therefore take advantage of economies of scope. Competences even on a much lower level than those of Amazon.com are not easy to build nor to acquire. This is well exhibited by the sector of Polish online groceries, some of which successfully compete on the Internet against traditional retail chains, mainly of French or German origin. The latter take advantage of their assets, such as a well-recognized and trusted brand, developed logistics infrastructure and a wide range of products. What traditional retail chains usually miss are the competences in the field of online customer service and delivery of purchased products to the customer’s location within the chosen time frame. This example shows that competences of pure players and traditional companies may be very distinct from each other.

Communicating Values

Communicating values is the third stage of the process of Internet-based customer portfolio building. It consists of attracting customers to a company and trust building. The process of attracting customers to the company is usually based on informing them of the value proposition and convincing them to visit the company’s website, or contact the company in a different way.

Within communicating values strategies of push and pull can be distinguished. The push strategy includes direct activities of a company, which are to influence potential customers to purchase a product or change the perception of a company. In the pull strategy it is the customers who initiate actions aimed at developing the relationship with a company (Kumar, Shah 2004). There is a common belief that the Internet is a pull medium. The pattern of consumer behaviour, in which customers initiate relationships by searching for information and placing enquires etc. takes place on the Internet, however this is not the only one. While increasing the Internet’s potential as an advertising medium, resulting from the growing number of its users, development of new promotional forms, targeting possibilities and more in-depth research, the Internet offers advertisers the possibility of reaching a wide audience with their messages in the push concept.

Promotion on the Internet also allows various types of message targeting to specific groups of customers as well as minimizing contact with Internet users who do not belong to the target group. Targeting may take place by considering placement of advertisements as well as based on users’ profiles or consumer behaviour, etc.

Informal communication (word of mouth, viral marketing) plays an important role in attracting customers. This phenomenon is the exchange of information concerning companies or their products, conducted by people not related to these companies. The Internet facilitates the informal communication by enabling the finding, publishing and exchanging of information. Information itself in the online environment may be transmitted through different channels such as: blogs and microblogs, forums, social networking websites, e-mail services, online communicators, video-sharing websites etc. Informal communication is also the essence of the current trend: the popularity of social networking websites.

When developing relationships with customers in the online environment, the significance of trust increases. The high level of risk perceived by customers is typical not only for beginners, but also for advanced Internet users (Forsythe 2006; Schlosser, White, Lloyd 2006). Negligence in trust building may result in a lack of it, which is often the main reason why customers tend to withhold from placing orders online, the use of e-services or submitting vulnerable information (Wang, Beaty, Foxx 2004). Erkki Liikanen, a member of the European Commission responsible for the Information Society, explained the role of trust in e-commerce without ambiguity saying “No trust, no transaction” (Kossecki, Świerczyńska-Kaczor 2004). Obłój and Capron showed that, as far as Internet auction are concerned “the ability of a reputable seller to command a price premium increases with the size of the reputation gap between the focal seller and its matched competitor” (Obłój, Capron 2002). Hence, the reputation, which is closely related to the concept of trust, allows selling at higher prices. Dellacoras presents the conclusions of much research, which shows that when it comes to online auctions the seller’s reputation positively mediates the price and the possibility of purchase (Dellacoras 2002).

Delivering values

Due to the wide scope of values that are the subject of exchange on the Internet, relationships between a company and its customers may differ from each other. Hence, the need for customer portfolio segmentation (customer profiling) arises, which allows companies to distinguish groups of customers who share similar or common characteristics and who react to company’s actions similarly.

There is a principal difference between market segmentation conducted while defining values and profiling customers. Market segmentation is aimed at the identification of groups of potential customers who the value proposition will be addressed to. Customer portfolio profiling means operational attributing current customers to segments in order to achieve better satisfaction of their needs and higher concentration on the most valuable customers. Customer portfolio segmentation has been described profoundly by many scholars (Storbacka 1997; Zeithaml, Rust, Lemon 2001; Reinartz, Kumar 2002). Customer portfolio segmentation can be based on such dimensions as values generated by customers for a company, and values expected from a company.

Value exchange is the transactional part of the relationship, during which customers receive values which are to satisfy their needs and provide companies with other values. The value exchange may take place not only on a company’s website, but also on a social networking website, an online auction, through instant communicators or e-mail.

Involvement building is understood here as an increasing in the scope of values which are the subject of exchange. Involvement building can be conducted twofold: the types of values exchanged remain the same, however the intensity of the exchange grows and the scope of values is broadened. A non-trivial task within involvement building in an online store is convincing its visitors, of whom usually only a small fraction are buyers, to make purchases and – of smaller importance – to publish their product reviews.

The last activity within value delivery is loyalty building. Customer loyalty on the Internet is usually not mediated by loyalty programs, but by the proper satisfying of customer needs. This is confirmed by conclusions in the publication 2006 Walker Loyalty Report for Online Retail (Walker Information 2006). A few loyalty leaders among e-commerce companies from various categories were identified, which were: Amazon.com, eBay, iTunes, L.L.Bean, Lands’ End, QVC, Victoria’s Secret and Walgreens. These companies outperformed the industry average in all areas including: look and feel, being trusted and safe, ease of use, personalization, uniqueness of items, display and description of items, response time and speed of site, recommendation and reviews. The concept of customer switching costs is useful in understanding customer loyalty on the Internet. These are the real or perceived costs that have to be incurred by the customer if he or she wants to change provider and do not have to be incurred if the customer stays with the provider. These costs usually arise with the customer’s growing involvement in the process of value exchange. Consequently, of the growth of customer involvement, there is higher loyalty (Chen, Hitt 2002).

Once the value exchange takes place and companies perform their cross-selling and loyalty building activities, customers’ needs and expectations may change, hence there is a need for another segmentation of customer portfolio, which may result in attributing customers to different segments. This in turn may lead to offering them a modified composition of values.

 

Value Generating for a Company

The last stage in the model of the customer portfolio building is generating values for a company. In the initial value exchange, in which the Internet is used to start the relationship, which later develops through traditional channels, companies derive benefits from customer acquisition. The benefits may result from acquisition of customers in new markets or from higher efficiency in current markets. Due to the fact that it is the customer who initiates the relationship with the company, there is customer self-selection in regard to matching the company’s value proposition to customer needs.

In a non-monetary exchange, the company provides customers with values for which they do not have to pay. The values that customers deliver to company in return are usually appreciated by another group of customers, who are the advertisers. This model is very common on the Internet, as many companies offer some values, such as content or services without charging the customer, for which they display advertisement. However, starting already from the 1990s this concept has been criticized for not being profitable enough, which is also raised in the current debate concerning free access to the press online.

Probably the most traditional model is based on monetary exchange. Customers pay for values received from companies. Online stores, paid databases and some e-services operate within this concept. Despite the simplicity of this model, there are many online stores in Poland which do not generate profits (Internet Standard 2009). In the case of paid e-services, the challenge becomes the ability to create such a value proposition, for which customers will be willing to pay, knowing that there are other free services available in the same category.

Independently from revenues, customer portfolio building on the Internet enables the acquisition of data, information and knowledge of customers, all of which are often difficult to achieve through other channels.

Research and managerial implications

The proposed model of Internet-based customer portfolio building is an integrative framework presenting the interdependence of various marketing actions orientated at the development of customer relationships. The model itself, as well as all of its stages, requires further research. The most interesting aspects – in the author’s opinion – are presented below.

What is the right approach to creating a superior value proposition on the Internet? In which regard do the online superior value propositions differ from the superior offline ones? In which way is the superior online proposition influenced by customization, network effects and digitalization?

When should customers be value co-creators? Is value co-creation a proper strategy for all companies? When do customers prefer to be co-creators and when only consumers of values?

Viewing the customer portfolio from brand and relational perspectives, which of the two interdependent assets is more important on the Internet: brand equity or customer equity? Which is more crucial in creating online brands: delivering superior values to customers or effective mass communication? Is customer equity an antecedent of brand equity in the online environment? If yes, how should it influence the marketing strategies of online companies? Compared to traditional companies, should online companies concentrate more on delivering superior values and less on mass communication, relying more on customer acquisition through positive word of mouth communication instead?

The issue of trust building on the Internet has been thoroughly researched in literature (Bart et al. 2005; Schlosser, White, Lloyd 2006; Lee, Turban 2001). Still, some questions require more research. Which factors influence a potential customer to initiate a relationship with a company? To what degree does the successful building of trust allow an increase in prices? In other words, what are trust-price strategies?

A very common challenge in customer relationships is achieving an optimum balance between customer acquisition, cross-selling (additional selling) and loyalty building in terms of a company’s expenditures, time spent etc. (Blattberg, Getz, Thomas 2001). Internet technologies are often characterized by high fixed and low marginal costs. Hence, the question arises, how these factors influence optimum balance between customer acquisition, cross-selling and loyalty building in the online environment compared to traditional business? According to Kumar, companies should concentrate more on customer profitability than on the number of its customers or their loyalty (Kumar 2008). The suggestion is then to keep rather a smaller portfolio including the most precious customers. Does the technological environment change this and allow companies to keep a big portfolio deriving benefits also from the least profitable customers?

The marketing efficiency and shareholders perspective on the customer portfolio is also of great importance. Adopting Srivastava, Shervani and Fahey’s (1998) approach, there is a need to research how various elements of the proposed model mediate the shareholder value by accelerating and enhancing cash flows, lowering the volatility and vulnerability of them and increasing the residual value.

The measurement of the customer portfolio and customer lifetime values has been comprehensively described in literature (Berger, Nasr 1998; Bauer, Hammerschmidt and Braehler 2003). Gupta and Lehman showed even direct relationships between the value of customer relationships and the company’s valuation based on examples of Internet companies (Gupta, Lehmann 2003). Still, there is a need for more studies on the dependence between the Internet-related metrics (such as unique visitors, page views etc.) and the value of the customer portfolio. Remaining in the field of marketing efficiency, a useful tool could be metrics for assessing the activities within certain stages of the model. Such metrics could help managers in assessing their activity e.g. in the area of defining values.

Finally, another field of research could be a study examining to what extent the proposed model provides insight into the process of customer portfolio building through traditional channels.

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