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Article Excerpt
The need to understand the relationships among customer metrics and profitability has never been more critical. These relationships are pivotal to tracking and justifying firms' marketing expenditures, which have come under increasing pressure. The objective of this paper is to integrate existing knowledge and research about the impact of customer metrics on firms' financial performance. We investigate both unobservable or perceptual customer metrics (e.g., customer satisfaction) and observable or behavioral metrics (e.g., customer retention and lifetime value). We begin with an overview of unobservable and observable metrics, showing how they have been measured and modeled in research. We next offer nine empirical generalizations about the linkages between perceptual and behavioral metrics and their impact on financial performance. We conclude the paper with future research challenges.
Key words: customer satisfaction; service quality; customer lifetime value; customer retention; customer equity; profitability; firm value
History: This paper was received August 19, 2004, and was with the authors 14 months for 2 revisions; processed by William Boulding.
1. Introduction
Customers are the lifeblood of any organization. Without customers, a firm has no revenues, no profits and therefore no market value. This simple fact is not lost on most senior executives. In a worldwide survey of 681 senior executives conducted by The Economist during October-December 2002, 65% of the respondents reported customers as their main focus over the next three years compared to only 18% who reported shareholders as their main focus (The Economist 2003). Oddly enough, while senior executives recognize the importance of customers, they still rely heavily on financial measures because customer metrics are not clearly defined (Ittner and Larcker 1996).
In this paper, we review and integrate existing knowledge on customer metrics (e.g., customer satisfaction, retention) and provide several generalizations about their impact on the financial performance of firms. As marketing strives for greater accountability, it is critical that we understand how customer metrics link to profitability and firm value. This paper has three objectives: (a) to provide a review of key customer metrics and the measurement and modeling issues related to them, (b) to highlight generalizable findings about the links between customer metrics and financial performance of a firm, and (c) to suggest areas for future research.
Customer metrics include a variety of constructs. We categorize them into observable or behavioral and unobservable or perceptual measures. Observable measures involve behaviors of customers that typically relate to purchase or consumption of a product or service. From a customer's perspective, these include decisions of when, what, how much, and where to buy a product. From a firm's perspective, this translates into decisions about customer acquisition, retention, and lifetime value. Unobservable constructs include customer perceptions (e.g., service quality), attitudes (e.g., customer satisfaction), or behavioral intentions (e.g., intention to purchase). In economists' terminology, unobservable constructs are stated preferences, while observable constructs are revealed preferences.
Intuitively, unobservable constructs are related to observable behavior, which leads to financial gains. Satisfaction, for example, is expected to lead to repurchase behavior, which translates into increased sales and profits. In Figure 1, we suggest a simple framework to link what firms do (i.e., their marketing actions), what customers think (i.e., unobservable constructs), what customers do (i.e., behavioral outcomes), and how customers' behavior affects firms' financial performance (i.e., profits and firm value). (1) Most research studies on these topics either investigate relationships in one of the boxes, or at best link relationships between constructs in two of the boxes. For example, some studies have established a link between unobservable constructs (e.g., satisfaction) and firm value, but do not consider intervening behavioral outcomes. Several researchers have also established a direct link between marketing actions and firms' financial performance (e.g., Joshi and Hanssens 2005) without examining antecedents in the black box, the term used by many researchers for the unobservable constructs. Given the vast literature in this field, we will focus on three links: (a) impact of unobservable constructs on financial performance (e.g., link between satisfaction and profitability), (b) impact of unobservable constructs on observable constructs (e.g., link between satisfaction and retention), and (c) impact of observable constructs on financial performance (e.g., link between retention and profitability).
[FIGURE 1 OMITTED]
The paper is organized to reflect relationships indicated in Figure 1. In [section] 2, we begin by describing key unobservable and perceptual customer metrics. For each construct, we briefly discuss how it has been defined and measured. In [section] 3, we describe key observable customer metrics and the modeling issues surrounding them. Section 4 describes main findings from research that links unobservable metrics to financial performance. Research results about the link between unobservable and observable metrics are discussed in [section] 5. Section 6 discusses findings that focus on linking observable metrics to financial performance. In [section] 7, we identify unresolved issues and suggest directions for future research. We conclude in [section] 8.
2. Unobservable or Perceptual Customer Metrics
Concepts in the black box--the unobservable concepts--have been studied extensively for many reasons. First, because they are collected almost exclusively through surveys, they have been relatively easy to obtain and share. Methodologies and best practices were developed in companies and in marketing research organizations. During the 1990s, for example, all of the major marketing research suppliers had units or practices in customer satisfaction, and the American Marketing Association sponsored an annual Customer Satisfaction Congress that often drew close to 1,000 registrants from companies. Second, using these metrics as dependent variables allowed companies to diagnose key attribute drivers that could then be addressed by specific marketing and operational strategies within a company. Third, the measures helped companies track performance over time, benchmark against competitors' offerings, and compare performance across different parts of an organization (e.g., branches, units, territories, countries).
Of all the unobservable metrics, customer satisfaction has been the most widely studied by researchers and used by firms because the construct is generic and can be universally gauged for all products and services (including nonprofit and public services). Even without a precise definition of the term, customer satisfaction is clearly understood by respondents and its meaning is easy to communicate to managers. Other unobservable measures--such as service quality, loyalty, and intentions to purchase--have also had widespread use in companies and been examined extensively in academic research. To a far lesser extent, constructs such as commitment, perceived value, and trust have made their way into company measurement systems and academic research. Other possible measures, such as product quality, have not been measured consistently enough to be linked to behaviors or financial performance in studies. We focus on the metrics of customer satisfaction, service quality, loyalty, and intentions to purchase in this paper because of their prevalence in use and maturity in measurement. For a variety of reasons, we chose to eliminate perceived value, trust, and commitment from this discussion.
Perceived value was excluded because it is the most ambiguous and idiosyncratic customer metric. While it can be defined in a general sense, operationalizing and measuring the construct has proven difficult. Most definitions state that perceived value is the consumer's objective assessment of the utility of a brand based on perceptions of what is given up for what is received (e.g., Zeithaml 1988). However, this definition itself is so broad and vague that the construct is virtually impossible to measure with validity, reliability, and consistency. In many academic and company studies, perceived value has been measured with a single item or a small number of items (Bolton and Drew 1991), but these measures leave to the customer the precise meaning of the term. Researchers have developed complex conceptualizations and measures (Sirdeshmukh et al. 2002), but these measures are not used in any consistent manner across studies and within companies.
We also eliminated commitment as a metric in this paper. Commitment is a construct that has been proposed as an alternative to customer satisfaction because it signifies a stronger attachment to a product or company. Moorman et al. (1992, p. 316) define commitment as "an enduring desire to maintain a valued relationship." A small number of studies have measured commitment in business-to-business (B2B) contexts (Gruen et al. 2000, Morgan and Hunt 1994), consumer contexts (Verhoef et al. 2002), and in the context of relational ties among channel members (Kim and Frazier 1997, Kumar et al. 1995). Many researchers in marketing have viewed commitment as a unidimensional concept and measured it simply, but others have elaborated dimensions and attributes (Garbarino and Johnson 1999, MacKenzie et al. 1998, Morgan and Hunt 1994). The inconsistent conceptualizations, particularly among components of commitment, have led to myriad ways to measure the concept. Because the research on commitment has rarely been linked to the behavioral or financial variables we emphasize in this paper, we eliminated commitment from our study.
2.1. Customer Satisfaction
Customer satisfaction has been defined in many different words, but essentially as the consumer's judgment that a product or service meets or falls short of expectations. Research has typically portrayed the evaluation of customer satisfaction as disconfirmation of expectations (see Oliver 1997 or Yi 1990 for a full review). This view holds that a consumer compares what is received with a preconsumption standard or expectation.
One of the pivotal definitional issues in the literature is whether satisfaction is best conceived as a transaction-based evaluation or as an overall, cumulative evaluation similar to attitude. Traditionally, satisfaction was viewed as transaction specific, an immediate post-purchase evaluative judgment or affective reaction (Oliver 1993). Reflecting the more global perspective, studies such as Anderson and colleagues (1994) consider satisfaction to be an "overall evaluation based on the total purchase and consumption experience with a good or service over time" (p. 54).
Both in practice and in academic research, customer satisfaction has been measured at the transaction level (as in trailer or event-triggered surveys) and at the overall level (as in the American Customer Satisfaction Index). In early studies, academics often focused on measuring confirmation or disconfirmation, and expectations, and the nature and type of expectations varied considerably from predictive expectations (Oliver 1997, Tse and Wilton 1988), to desires and experience-based norms (Cadotte et al. 1987). Applied marketing research tends to measure satisfaction at the transaction level but more recently as an overall evaluation, a cumulative construct that is developed over all the experiences a customer has with a firm.
2.2. Service Quality
Perceived service quality is the degree and direction of discrepancy between customers' service perceptions and expectations (Sasser et al. 1978, Zeithaml and Parasuraman 2004). While multiple interpretations of expectations have emerged in service quality research as they have in customer satisfaction research, the notion that service quality is a comparative process is one of the basic building blocks in the field.
The dominant measurement approach for quantitative assessment of service quality is SERVQUAL, a multiple-item measure first developed in the 1980s, then tested and refined throughout the 1990s (see a review in Zeithaml and Parasuraman 2004). Researchers first operationalized the service quality gap as the difference between two scores--customer expectations and perceptions of actual service performance for the perceptual attributes that respondents indicated were pivotal. Through this early research five dimensions of service quality were derived as factors: reliability, responsiveness, assurance, empathy, and tangibles (Zeithaml and Parasuraman 2004). Refinement and assessment of SERVQUAL over two decades indicate that it is a robust measure of perceived service quality. However, concerns about SERVQUAL have been raised and debated, including the interpretation of and need to measure expectations, the appropriateness of measuring service quality using difference scores, and the generalizability of the five dimensions across all service contexts.
2.3. Loyalty and Intentions to Purchase
Behaviorally, consumers can be defined as loyal if they continue to buy the same product over some period of time. Jacoby and Chestnut (1978), however, took exception to this simple definition and were the first researchers to view loyalty psychologically rather than behaviorally. They recognized that behavioral loyalty could be spurious because it could be based on convenience and switching costs, or misleading if consumers were multibrand loyal. In a representative definition that combines both the behavioral and attitudinal perspectives, Oliver (1997, p. 392) defines loyalty comprehensively as
a deeply held commitment to rebuy or repatronize a preferred product/service consistently in the future, thereby causing repetitive same-brand or same brandset purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior.
Consumer loyalty is indicated by an intention to perform a diverse set of behaviors that signal a motivation to maintain a relationship with the focal firm, including allocating a higher share of the category wallet to the specific service provider, engaging in positive word of mouth, and repeat purchasing (Zeithaml et al. 1996).
Loyalty has been measured behaviorally as repeat purchase frequency or relative volume of purchasing (Tellis 1988); and attitudinally as repurchase intentions (e.g., Reynolds and Arnold 2000), intention to recommend to others (e.g., Mattila 2001), and likelihood of switching and likelihood of buying more (e.g., Seines and Gonhaug 2000). Zeithaml et al. (1996) combine these different aspects of loyalty and develop a behavioraMntentions battery with four factors--loyalty, propensity to switch, willingness to pay more, and external response to service problems-comprising 14 specific behavioral intentions likely to result from perceived service quality.
In a departure from the rigor of academic research, some scholars and practitioners claim that complex measurements are unnecessary to capture loyalty. Notably, Reichheld (2003) claims that the only number a company needs is one that reflects customers' intention to recommend the firm to others. Reichheld suggests using a net promoter score, defined as the percentage of respondents answering 9 and 10 on a 10-point willingness-to-recommend scale, minus the percentage of respondents answering through 6. Reichheld argues that companies commonly get net promoter scores that range from 10% to 16%, and that the best companies get scores between 75% and 80%. Because of its simplicity and ease of measurement, the index has gained popularity with many companies. General Electric (GE) CEO Jeffrey Immelt encourages his executives to use net promoter scores in all of GE's divisions. The Wall Street Journal, Symantec, and Intuit are other proponents of the net promoter score.
3. Observable or Behavioral Customer Metrics
The implicit assumption in using unobservable customer metrics is that they anticipate or predict observable behavior such as retention or increased consumption. In the 1990s, companies began to question whether the relationship between unobservable measures such as customer satisfaction and observable behavior such as purchasing was sufficiently strong to justify its use as the primary unobservable predictor. Additionally, as database management and customer relationship management have evolved, researchers and companies find that they can bypass unobservable metrics and directly link a firm's actions to customers' observable behavior and the firm's financial performance.
In this section, we discuss behavioral and observable outcome metrics. These metrics include customer decisions of what, when, how much, and where to purchase products or services. A vast literature on choice models attempts to elucidate the impact of marketing variables on such consumer decisions (e.g., Guadagni and Little 1983, Gupta 1988). The equivalent decisions from a firm's perspective are which customers to acquire, how to retain them, and how to cross-sell different products and services to them. Research in customer relationship management (CRM) uses this terminology (e.g., Kamakura et al. 2005). Even though the terminology used by these two streams is different, they are effectively capturing similar aspects of consumer purchase behavior. For example, models of cross-category purchase (e.g., Manchanda et al. 1999, Iyengar et al. 2003) can also be used for the purpose of cross-selling. We will adopt the terminology and the metrics used in CRM in this paper because they have direct implications for firms' financial performance. Specifically, we will focus on customer acquisition, retention, and cross-selling, which in turn determine customer lifetime value (CLV) and customer equity (CE).
3.1. Customer Acquisition
Customer acquisition refers to the first-time purchase by new or lapsed customers. The basic model for customer acquisition is a logit or a probit. Specifically, customer j buys at time t (i.e., [Z.sub.jt] = 1) as follows:
[Z.sup.*.sub.jt] = [[alpha].sub.j][X.sub.jt] +...
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