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Marketing Performance Measures:History and InterrelationshipsBruce H. ClarkPublished online: 01 Feb 2010.

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Journal of Marketing Management 1999, 15, 711-732

Bruce H. ClarkI

NortheasternUniversity

Introduction

Marketing Performance Measures:History and Interrelationships

This article reviews the history of measuring thepeifonnance of marketing in the fimJ, organisedaround three themes: the movement from financial tononfinancial output measures, the expansion frommeasuring only marketing outputs to measuringmarketing inputs as well, and the evolution fromunidimensional to multidimensional measures ofpeifonnance. Evaluation of this history suggests aneed for the marketing community to develop a set ofmeasures small enough to be manageable but largeenough to be comprehensive. The paper examines theinterrelationships among four important measuresand suggests research issues and approaches to aid inUJistask.

Measuring marketing perfom1ance is attracting academic and managerialattention with an urgency and scope previously unprecedented in the field'shistory. This represents the convergence of four trends. First after a decade ofdownsizing, major corporations are reaching the point of diminishing retums onincreasing profits by reducing headcount and increasing operational efficiency.This has led to a refocusing on marketing as a driver of future sales, andtherefore profit growth (Sheth and Sisodia 1995). Second, there has beenincreasing demand from investors for infonnation related to the quality of themarketing effort which traditionally has been both under- and poorly reported infinn financial statements (Mavrinac and Siesfeld 1997; Haigh 1998). Third,popular new overall conceptions of business perfonnance measurement such asthe Balanced Scorecard (Kaplan and Norton 1992) have attracted attention tothe issue of which marketing measures should be included in overallassessments of business perfonnance. Finally, senior marketing managersthemselves have become fmstrated with traditional perfom1ance measures thatthey believe, undervalue what they do, leading to calls for research from a varietyof quarters (e.g.Marketing Science Institute 1998).

The purpose of this paper is to layout the history of marketing perfom1ancemeasurement at a very broad level, and to suggest that what marketing as a field

1 Correspondence: Bruce Clark, Assistant Professor, Marketing Group, 202 Hayden Hall,Northeastern University, Boston, MA 02115 USA, Phone: 1-617-373-4783, FAX: 1-617-373-8366, e-mail: bclark@cba.neu.edu

ISSN0267-257X/99/080711 +21 $12.00/0 ©Westbum Publishers Ltd.

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712 Bruce H. Clark

needs is fewer measures and more understanding of the interrelationshipsamong those measures. I identify three discernible historical trends and theirconsequences, explore the interrelationships among four key marketingperfonnance measures, and discuss the search for a few good leading indicatorsof marketing perfom1ance, with implications for research and practice.

A History

Marketing perfom1ance measurement has, of course, been practiced and studiedfor decades. A review of this history suggests marketing performance measureshave moved in three consistent directions over the years: first from financial tonon-financial output measures; second, from output to input measures; andthird, from unidimensional to multidimensional measures (see Figure 1 andTable 1 for summary views).

Table 1. Literature Review Summary

Single Financial Output Measures

Profit Goodman (1970, 1972), Sevin (1965)Sales Revenue Feder (1965)Cash Flow Buzzell and Chussil (1985), Day and Fahey (1988)Non-financial Measures

Market Share

Quality ofServicesAdaptability

CustomerSatisfaction

Customer Loyalty

Buzzell and Gale (1987), Jacobson (1988), Szymanski,Bharadwaj, and Varadarajan (1993)Bucklin (1978)

Bhargava, Dubelaar, and Ramaswami (1994), Walker andRuekert (1987)Anderson and Sullivan (1993), Anderson, Farnell, and Rust(1997), Danaher and Matson (1994), Farnell (1992), Farnell,Johnson, Anderson, Cha, and Bryant (1996), Halstead,Harbnan, and Schmidt (1994), Hauser, Simester, andWernerfelt (1994), Oliva, Oliver, and MacMillan (1992),Peterson and Wilson (1992), Piercy and Morgan (1995),Seines (1993), Spreng, MacKenzie, and Olshavsky (1996),Teas (1993), Teas and PalanI997), Voss, Parasuraman, andGrewal (1998), Yi (1990)Anderson and Sullivan (1993), Dick and Basu (1994),Farnell, Johnson, Anderson, Cha, and Bryant (1996), Jonesand Sasser (1995), Oliva, Oliver, and MacMillan (1992),Reichheld (1994), SeInes (1993)

Confd/ ...

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Marketing Performance Measures 713

Non-fmancial Measures Cont'd/ ...

Brand Equity Aaker and Jacobson (1994), Ambler and Barwise (1998),Barwise (1993), Keller (1993, 1998), Haigh (1998), Lassar,Mittal, and Shanna (1995), SeInes (1993), Simon andSullivan (1993)

Piercy (1986), Srivastava, Shervani, and Fahey (1998)Brownlie (1993, 1996), Kotler, Gregor, and Rodgers (1977),Rothe, Harvey, and Jackson (1997)Bonoma (1985, 1986), Bonoma and Crittenden (1988)

Effectiveness

Multivariate~Q!ysis

MarketingImplementationMarketOrientation

Day and Nedungadi (1994), Deshpande and Farley (1998a,1998b), Han, Kim, and Srivastava (1998), Kohli andJaworski (1990), Kohli, Jaworski, and Kumar (1993), Jaworskiand Kohli (1996), Narver and Slater (1990, 1998), Slaterand Narver (1994), Wrenn (1997)

Multiple Measures

Efficiency Bonoma and Clark (1988), Dunn, Norbum, and Birley(1994), Kotler(1977), Sheth and Sisodia (1995), Walker and Ruekert(1987)Bhargava, Dubelaar, and Ramaswami (1994), Spriggs (1994)

Input Measures

Marketing AssetsMarketing Audit

Moving from Financial to Non-Financial Output MeasuresEarly work in the finn-level measurement of marketing perfonnance was

largely directed at examining the productivity of a finn's marketing efforts atproducing positive financial outputs. These studies typically were designed toprovide guidance to managers regarding how to best allocate their marketingresources, drawing on both marketing knowledge and perspectives from financeand accounting.

One branch of this literature developed extensive profitability analyses ofmarketing efforts. Sevin (1965) and Goodman (1970, 1972) are classics in thisfield, laying out in great detail how to relate financial outputs to marketinginputs. Feder (1965) borrowed from the marginal revenues-marginal costsconcept in microeconomics to suggest how to allocate marketing resources mostefficiently.Later work expanded from using profitability as an output to use moresophisticated measures from the finance literature, examining cash flows and thenet present value of different marketing strategies (Buzzell and Chussil, 1985;Day and Fahey, 1988). In their review of finn-level marketing productivity studies,Bonoma and Clark (1988) found that the most frequent measures of outputwere, in order, profit, sales (unit and value), market share, and cash flow.

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714 Bruce H. Clark

Figure 1. The Expanding Domain of Marketing Performance Measures

Non-FinancialMeasures

• CustomerSatisfaction

• Customer Loyalty• Brand Equity

Single FinancialOutput Measures

• Profit• Sales• Cash Flow

Input Measures Multiple Measures

• Marketing Audit • Marketing Audit• Marketing • Efficiency/

Implementation Effectiveness• Market • Multivariate

Orientation Analysis

The 1980s brought an expanded conception of output that included non-financial measures. This was partly driven by the realization that what hassometimes been called "the black box" (e.g. Piercy, 1997) of mediating factorsbetween marketing inputs and financial outputs is itself worthy of study. Bonomaand Clark (1988) uncovered many moderating factors in the marketingproductivity literature, suggesting that the process of transformation betweenmarketing inputs and outputs is highly contingent on other variables.

Market share attracted tremendous attention as an output variable in thisperiod. Work by consultants at the Boston Consulting Group (Henderson, 1973)and academics working on the Profit Impact of Market Strategies (PIMS)project(Buzzell and Gale, 1987) concluded that market share was a strong predictor of

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Marketing Perfomlance Measures 715

cash flow and profitability. This, combined with the spectacular success at thetime of Japanese finns that emphasized market share as a perfonnance measure,drove much examination of market share as the best measure of marketingperfonnance. Unfortunately, in retrospect the market share-profitabilityrelationship has proven both controversial and complicated Qacobson, 1988;Szymanski, Bharadwaj, and Varadarajan, 1993).

Aside from market share, other non-financial measures advocated as outputsincluded services and level of new product development/innovation. Bucklin(1978) is particularly adamant in his claim that the quality of services providedmust be included in any marketing productivity measure. Rather than consideronly the benefit to a customer of using a product, Bucklin attempts to accountfor the services that add to simple foml utility, discussing logistical services (e.g.delivery),infonnational services (e.g.product infonnation), and product functionalservices (e.g.warranties, packaging).

Adaptability or innovativeness of a finn's marketing has received continuousattention as a perfonnance measure (Bhargava, Dubelaar, and Ramaswami,1994; Walker and Ruekert, 1987). Typically cast in tenns of the finn's newproduct or marketing innovations, the idea behind measuring adaptability as anoutput of marketing is that in the face of a changing environment, finns that areunable to adapt will fail (Walker and Ruekert, 1987).

In the last 10 years, three new non-financial output measures have attractedextensive research attention: customer satisfaction, customer loyalty, and brandequity. I will briefly review each measure in tum.

Customer Satisfaction.Perhaps no recent measure of business perfonnance has attracted as much

attention as customer satisfaction. With a large and continuing academicresearch stream (see Halstead, Hartman and Schmidt, 1994 and Vi, 1990 forreviews) and substantial adoption by industry (the 1997 Marketing NewsCustomer Satisfaction Research Directory listed over 200 research fimls withsatisfaction practices), customer satisfaction measures have become importantbenchmarks in many industries. .

The traditional disconfimlation paradigm of customer satisfaction proposesthat customers have prepurchase expectations about the products they buy, andare more satisfied depending on how well the consumption experience exceeds(disconfin11S)those expectations. Having a satisfied customer base is consideredan important marketing asset because it should lead to increased loyalty,with itsconsequent revenue implications and lower marketing costs.

While straightforward in theory, customer satisfaction measurement inpractice has proven more complex. First, at least in North America, mostcustomers are satisfied. Peterson and Wilson (1992) review a large number ofstudies where the distribution of customer satisfaction responses is highlyskewed towards the positive. This finding presents two problems. Managerially, ahigh satisfaction rating may have little' consequence if customers are equallysatisfied with competing products; if everyone gets an 85% score, then no finn

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716 Bmce H. Clark

has a competitive advantage. Methodologically, Peterson and Wilson (1992)observe that the highly skewed distribution reduces the likelihood that asignificant correlation between satisfaction and other perfonnance variables willbe observed; low variance in the satisfaction measure makes it unlikely that anyclear relationship with other variables will be revealed.

Empirical research evidence regarding the disconfinnation paradigm has alsobeen quite mixed, leading to a proliferation of satisfaction frameworks (e.g.Anderson and Sullivan, 1993; Teas, 1993; Voss, Parasuraman and Grewal, 1998).The expectations constmct has proven particularly problematic, in that differentstudies appear to define it differently (Teas and Palan, 1997), leading one towonder exactly what managers should be measuring. Recent research alsosuggests that there may be multiple satisfaction processes (Spreng, MacKenzieand Olshavsky, 1996) and that one should consider measuring satisfaction on anattribute-by-attribute basis (Donaher and Mattson, 1994; Halstead, Hartman andSchmidt, 1994).

Further, satisfaction measurement programs appear particularly difficult toimplement. Piercy and Morgan (1995) note substantial internal barriers to themeasurement process. Measures also appear more subject to manipulation thanobjective items such as unit sales. Once customer contact personnel (e.g.salespeople) or organizations (e.g. retailers) know they will be graded onsatisfaction ratings, there is a tremendous incentive to manipulate the findings(Hauser, Simester, and Wernerfelt, 1994).

Customer Loyalty.Partly in response to problems with customer satisfaction as a measure,

customer loyalty measures have attracted increasing attention as a measure ofgood marketing. Behavioural measures of brand purchase and repurchase haveexisted for years in the marketing literature (e.g. Uncles, Ehrenberg andHammond, 1995), but there has been a recent emphasis on expanding beyondpurely behavioural conceptions of loyalty (Dick and Basu, 1994). Advocates ofloyalty note that financial perfonnance ultimately reflects whether customersrepurchase from a firnl over time, regardless of satisfaction. One of the mostprominent spokespersons for this position, Frederick Reichheld (I994), suggeststhat good marketing attracts the right customers: ones whose loyalty the finn isable to earn and keep. A loyal customer base, it is argued, should increaserevenue per customer as satisfied customers buy more volume, a broader rangeof products, and/or pay a premium for the company's products. It also shouldlower marketing costs; current customers are cheaper to retain, and word-of-mouth from current customers should make new customers easier to acquire. Acommon financially-based measure of the worth of a loyal customer base is tocalculate the "lifetime value" of the customers in this base (Wyner, 1996).

Brand Equity.Many researchers and managers believe that a powerful brand (one with high

"equity") is among the greatest marketing assets a finn can have (see Barwise,

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Marketing PerfOnllanCe Measures 717

1993; Keller, 1998 for reviews). Strong brands, it is argued, (1) allow finns tocharge price premiums over unbranded or poorly branded products; (2) can beused to extend the company's business into other product categories; and (3)reduce perceived risk to customers (and, perhaps, investors).

There have been two approaches to measuring brand equity. The behaviouralapproach looks at customer response to the brand, either in tenns of perceptionsor purchase. One definition of behaviourally-based brand equity is the differentialeffect of brand knowledge on customer response to marketing of the brand(Keller, 1993). Customers in these studies typically respond more favourably tostrong brands than to unbranded or poorly branded products. The financialapproach to brand equity attempts to divine the financial value of the brand tofinns and their investors. A widely cited approach in this area was developed bySimon and Sullivan (1993), who define brand equity as the incremental cashflows that accme to branded products over and above the cash flows that wouldresult from the sale of unbranded products.

There is little question that brands can make a powerful difference in howcustomers respond to brands and brand extensions (Barwise, 1993; Keller, 1998).There is growing evidence that brand equity has an influence on investors as well(Aaker and Jacobson, 1994; Simon and Sullivan, 1993), which has led to changesin financial reporting ntles in the UK (see Ambler and Barwise, 1998 for adiscussion of brand valuation and brand equity). Barwise (1993) notes, however,that we actually know relatively little about the impact of a brand on the brandedproduct's long-tenn profitability. Further, the relationship between thebehavioural and financial approaches to brand equity are at present not well-integrated (see Ambler and Barwise, 1998 for a discussion of definitions). Finally,while brand equity appears a powerful measure of perfonnance, it also is onethat is hard to use as a short-tenn perfonnance measure for managers. It cantake years and huge marketing expenses to create a powerful brand; conversely,this asset can take substantial time to dissipate even in the face of reducedmarketing support

Moving from Output to Input MeasuresRecent emphasis on measures such as customer satisfaction, customer loyalty

and brand equity is part of a general move away from ultimate financial outputmeasures such as profit and sales and toward measures earlier in the input-to-output sequence. In particular, one can look at initial marketing activities (inputs)that lead to intennediate outcomes such as the three measures above that intum lead to financial outputs. The intenllediate outcomes can be thought of asthe marketing assets (Piercy, 1986; Srivastava, Shervani and Fahey, 1998) thatare leveraged to produce superior financial perfonnance.

One of the earliest attempts to assess the underlying marketing inputs thatlead to superior perfOnllanCe was the marketing audit concept (see Brownlie,1993; Rothe, Harvey, and Jackson, 1997 for reviews). The goal of a marketingaudit is to systematically evaluate the appropriateness of the actjvities and assetsa finn uses in its marketing, given the finll'S situation. While initially conceived in

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the 1950s, the audit was strongly popularized by Kotler and his colleagues(Kotler, Gregor, and Rodgers, 1977). They advocate an evaluation of theenvironment, to understand the situation the finn is in, and then examination ofstrategy, organization, systems, and productivity of marketing. Further work canthen be focused on specific marketing functions. While an area of much researchand successful case studies, it is unclear how widespread audits are in practice.They also typically do not result in exact perfon11ance measures so much asdiagnoses for organizational improvement (Brownlie, 1996).

Bonoma (1985, 1986) also weighs in on the question of what constitutes goodmarketing practices. He focuses on the finn's marketing skills and marketingstructures (e.g. systems and procedural support), and argues that good marketingis the product of the interaction between the two.

The most recent systematic evaluation of the quality of marketing inputs hasresulted from the market orientation concept This perspective - also variouslydescribed as marketing-oriented and market-driven (see Jaworski and Kohli,1996; Wrenn, 1997 for reviews) - measures activities that develop and useintelligence about the market While definitions across studies vary (e.g. Day andNedungadi, 1994; Kohli and Jaworski, 1990; Narver and Slater, 1990), commoncomponents of being market oriented include systematic gathering, analysis,dissemination and use of market infonnation within the organization. Day andNedungadi (1994) in particular note the importance of maintaining a balancedperspective between customers and competitors.

Empirical evidence suggests that being good at generation. dissemination andapplication of market infom1ation within the organization can be a significantadvantage (e.g. Day and Nedungadi, 1994; Jaworski and Kohli, 1993; Narver andSlater, 1990), but overall findings on the relationship between market orientationand perfonnance have been mixed (Han, Kim and Srivastava, 1998). This has ledto a search for moderators or new explanatory factors in the relationship (e.g.Han et aI., 1998; Slater and Narver, 1994). Aside from affecting businessperfom1ance, Wrenn (1997) reviews studies suggesting marketing orientationalso positively affects both customer and employee perceptions of the finn.

As with brand equity, the variety of operationalizations of market orientationmake it difficult to use as a perfonnance measure in practice. As many of themeasures of orientation list specific organizational activities (e.g. 'We haveinterdepartmental meetings at least once a quarter to discuss market trends anddevelopments," Kohli, Jaworski, and Kumar, 1993), one may wonder if a focus onmeasuring market orientation as a perfonnance measure might lead to ritualactivities that allow fin11Sto "tick the box" without realizing the true benefits. Thisrelates to the issue of whether market orientation represents a behaviour or aculture (Deshpande and Farley, 1998a; Narver and Slater, 1998).

Moving to Multidimensional MeasuresEarly in the history of measuring marketing perfom1ance, it was common to

use one or a handful of financial or volume measures to track the output ofmarketing. This changed in the, 1970s, beginning with the multidimensional

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Marketing Perforn1ance Measures 719

marketing audit (e.g. Kotler, Gregor and Rodgers, 1977). In the 1980s, Bonomaand Clark (1988) and Walker and Ruekert (1987) independently suggestedschemes of marketing perfonnance measurement tl1at assessed marketingefficiency and effectiveness. Bonoma and Clark (1988) described tl1e fonner as aproductivity measure, comparing outputs to inputs, and tl1e latter as acomparison of outputs to goals, drawing on Dmcker's (1974) distinction betweenefficiency as "doing tl1ings right" and effectiveness as "doing the right tl1ing."Walker and Ruekert (1987) added a measure of adaptability to changes in tl1eenvironment, while Bonoma and Clark (1988) included a measure of tl1e hostilityof tl1e external environment.

Paradigms from tl1e management literature have influenced tl1e move tomultidimensional measures as well. Kumar, Stem and Achrol (1992) draw onfour perspectives from tl1e organizational effectiveness literature to researchreseller perfonnance. Kotler's dimensions of marketing effectiveness (Kotler,1977) have been incorporated into rigorous empirical studies (e.g. Dunn,Norburn and Birley, 1994). Multivariate data analysis techniques such as factoranalysis and Data Envelopment Analysis have been adopted to identify tl1eunderlying dimensions of perfonnance (e.g. Bhargava, Dubelaar and Ramaswami,1994; Spnggs, 1994). While multiple measures are clearly psychometricallydesirable to obtain tl1e most complete picture possible of marketingperfom1ance, tl1eyraise difficult issues for managers, a point to which I will returnbelow.

Evaluating the Trends

Having seen evidence regarding tl1e historical trends in marketing perfonnancemeasures, one can ask if tl1ese trends are good for scholars, managers or botl1.The answer appears to be a qualified yes for both audiences.

That we have moved as a field to examine non-financial measures as well asfinancial is clearly an improvement. The asset-based marketing perspective inparticular (e.g. Piercy, 1986) demonstrates tl1e inadequacy of financial outputs astl1e sole measure of marketing perforn1ance. Indeed, tl1e reason non-financialmeasures were adopted in tl1e first place was tl1e instinct of managers andacademics tl1at some important elements of marketing perfonnance (e.g. brandstrengtl1) were left uncaptured by traditional financial measures. Areas such ascustomer satisfaction and brand equity have been unusual in tl1at scholarlyresearch and practitioner interest have coincided far more powerfully tl1an isusual in our discipline. This is all to the good.

Deeper understanding of tl1e quality of marketing inputs in the fonn ofmarketing processes has had less clear impact. Inherently difficult to studybecause of tl1e complexity of processes and tl1e large number of external andinternal constituencies involved, defining "good marketing activities" has moreoften been tl1e subject of conceptual or qualitative treatments (e.g. Bonoma,1985; Bonoma and Crittenden, 1988) tl1an rigorous statistical research. Conceptssuch as tl1e marketing audit and market orientation, while powerful in tl1eory,

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720 Bruce H. Clark

appear difficult to transfer to the managerial realm. Both ideas have empiricalstudies backing up a link to overall business perfonnance, but, as noted above,this link is by no means a simple linear relationship, depending rather on avariety of potential moderating factors. Further, it is difficult to tell howwidespread either practice is because some finns may adopt elements of eitherapproach without ever using the words "marketing audit" or "market orientation."In sum, this trend has led to richer, deeper understanding of marketing process,but compared to objective financial measures its complexity makes it relativelyintractable for managers and more statistically-inclined researchers.

The trend toward multidimensional measures has arguably been wonderfulfor researchers and horrible for practitioners. Psychometrically and theoretically,researchers know that a multidimensional model of marketing perfonnance islikely to be more "true" in that it will capture more facets of perfonnance thanany single dimension can. Unfortunately, successively more complicated schemesdramatically increase the burden on managers attempting to measureperfomlance in the world. Given bounded rationality, any individual manager canonly juggle so many concepts in his or her mind at once. Yet organizations arefinding themselves overwhelmed with measures. Meyer (1998) notes that it iscommon for corporations to have fifty to sixty "top-level" perfomlance measures(p. xvi). In the marketing context, .Ambler and Kokkinaki (1998) conclude that"marketing is already assessed against plenty of measures," (p. 35) but that theweighting of measures is incorrect Figuring out which of many measures are"really important" may drive the conscientious manager to despair. While onemight be able to reduce these measures to a more manageable set by means ofmultivariate statistical techniques, these techniques seem unlikely to be part ofeveryday management More generally, it is not clear that management isinterested in elegant multidimensional schemes. Ambler and Kokkinaki (1998)find that financial measures dominate UK executives' assessment of marketingperfonnance; Clark (1999) finds sales the most frequent measure used amongUS executives. Even in our own field, researchers who use perfonnance as adependent variable most frequently rely on sales and market share (Ambler andKokkinaki, 1997). One of the original appeals of the balanced scorecardapproach to total business perfonnance measurement was that it organizedmeasures under a small set of dimensions of business perfonnance with whichany manager can work (Kaplan and Norton, 1992). Marketing scholars mustsimilarly present management with a handful of measures that are simpleenough to be usable but comprehensive enough to give an accurate perfonnanceassessment

Understanding Interrelationships among Measures

Much work in recent years on non-financial measures has been to understandtheir relationship to financial measures (e.g. Han et a!., 1998; Anderson, Fomelland Rust, 1997). The presumption behind many of these non-financial measuresis that they are leading indicators of long-nm shareholder value (e.g.Srivastava et

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Marketing Perfonnance Measures 721

aL, 1998). A powerful brand, for example, should not only generate profits on thecurrent accounting statements, but will help generate future profits.

A question one may ask in this context is whether we need all the non-financial measures proposed. For example, if a fim1 has good customersatisfaction measures, does it also need loyalty measures? The key to developinga comprehensive but usable set of measures must be to understand theinterrelationships among the various marketing perfonnance measures proposed.To the extent different measures are all correlated with profit or sales, that theyare uncorrelated with one another seems unlikely. If, on the other hand,measures are highly correlated, they then can be collapsed into multipleindicators of a single constmct (Churchill, 1979). Perhaps most likely is thesituation where various measures are independent but correlated, with causalrelationships among them.

At a relatively simple level, consider the interrelationships among fourconcepts that have drawn research attention in the last 10 years: marketorientation, customer satisfaction, customer loyalty and brand equity. Each hasbeen proposed as an important indicator of marketing perfonnance that shouldin tum affect overall business perfonnance. Following is a brief summary of whatwe as a field know about the interrelationships among these four, with an eyetoward future research that might indicate which measures are most valuable toparticular finns.

Market Orientation and Customer SatisfactionJaworski and Kohli (1996) observe that market orientation should be

positively related to customer satisfaction. Gathering and responding to goodmarket intelligence should lead to products that do a better job of meetingcustomer needs. Unfortunately, they note, there is little empirical study tosupport this proposition. If market orientation does improve overall businessperfonnance, customer satisfaction would be a logical mediating variable throughwhich such a relationship would occur. Indeed, such a mediating relationshipmight explain why the direct effect of market orientation on businessperfonnance has been difficult to document consistentJy. In the long nm, onemight see negative feedback from customer satisfaction to market orientationthrough perfonnance: a company with a satisfied customer base might havesuccess, which in tum might lead to complacency and a dulling of the fim1'smarket orientation (see Miller, 1994 on the perils of success).

Market Orientation and Customer LoyaltyFundamentally, the same logic as under the market orientation-satisfaction

link should apply here. The question is whether the link would be direct orindirect through satisfaction. As loyalty in the absence of satisfaction may occuronly in the absence of competitive alternatives (see below), the indirect linkseems more likely.

Market Orientation and Brand EquityNo one, to my knowledge, has proposed a causal link between market

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orientation and brand equity, but speculation suggests that some positiverelationship might exist A market oriented finn, with good knowledge ofcustomers, presumably can design and support a stronger brand than aninternally-focused, ignorant firn1. While this effect may be indirect throughsatisfaction, one can argue that a direct link might exist before purchase as thewell-designed brand is attractive and affects customer behaviour even prior toany satisfaction experience. Once again, a disconnection may occur betweenbrand equity and market orientation if high brand equity leads to complacencyand a lower market-orientation.

Brand Equity and Customer SatisfactionLooking at brand equity from a psychological perspective, a brand name

evokes a particular set of knowledge about the brand from memory (Keller,1993). This knowledge structure (e.g. image, associations, attitudes) will differamong brands and between branded and non-branded products, withconsequences for customer behaviour.

It seems straightforward that customer satisfaction in one period should affectbrand equity in the next A satisfying experience with a brand should increase thefavourability of the associations a customer has to the brand. Interestingly, SeInes(1993) finds this relationship in only one of the four industries he examines.Another intriguing possibility is that brand equity in one period may affectcustomer satisfaction in the next period, both through its impact on expectationsand on perceived experience with the brand. Regarding expectations, brandknowledge should affect the expectations aspect of customer satisfaction in twodimensions: certainty and level. First, a strong brand will probably produce well-defined expectations, because the knowledge structure about the brand will beelaborate. By comparison, a product with a weak brand or no brand will evokelittle knowledge and thus more uncertain expectations. Measures of satisfactionwith strong brands should be more reliable than with weak brands, because theexpectations construct will be more clearly defined. Second, a brand, howeverstrong, will produce some level of expectations about the product Keller (1993)defines positive brand equity as that evoking more favourable responses than acorresponding unbranded product A strong brand might influence expectationsin a higher direction, making satisfaction more difficult to achieve.

Regarding experience, favourable brand equity may have an influence onperceived experience with using the brand; one might rate more highly anexperience with a well-liked brand than one would with a correspondingunbranded product, simply because the accumulated (positive) experience withthe brand outweighs any single experience. A positive expectation created by astrong brand may also influence the perceived experience through anassimilation effect, such that perceived quality adjusts slightly in the direction ofexpectations (cf.Anderson and Sullivan, 1993).

Brand Equity and Customer LoyaltyFavourable brand equity should affect customer loyalty. Customers are

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Marketing Perfonnance Measures 723

presumably more loyal to well-regarded brands than to poorly-regarded ones;whether this link is direct or mediated by customer satisfaction is open toquestion (see below). Seines (1993) found a direct positive link across fourindustries (see also Lassar, Mittal and Shanna, 1995). Behaviourally, Fader andSchmittlein (1993), found that brands with high market shares exhibited muchgreater brand loyalty than did brands with low market shares.

It seems less likely that simple loyalty in the fonn of consistent repurchase hasan effect on the favourability of brand equity. Rather, loyalty should reinforcewhatever level of brand equity already exists. Every purchase incident shouldreinforce (and possibly elaborate) the current knowledge structure the customerholds regarding the brand, making it more accessible in memory. The onlyinfluence on the level might occur through an exposure effect, such thatfamiliarity increases liking (Bomstein, 1989).

Customer Satisfaction and Customer LoyaltyUnlike some of the other links, this link has received extensive research

attention, the conclusion of which is that customer satisfaction has a positivedirect impact on customer loyalty (Fornell, 1992; Seines, 1993; Anderson andSullivan, 1993; Jones and Sasser, 1995; Fornell, Johnson, Anderson, Cha andBryant, 1996). There is evidence, however, that the fonn and strength of this linkvaries across industries. Some analysts suggest that the strength of thisrelationship may vary by the degree of competitiveness within the industry, suchthat the relationship is stronger in more competitive industries (Fornell, 1992;Jones and Sasser, 1995). Seines (1993) suggests the ambiguity of the productmay moderate this relationship. In products where evidence about the quality ofa product experience is ambiguous, brand may matter more and satisfaction lessin detemlining loyalty because the satisfaction j~ldgments will be poor. Thisrelationship has also been posited to be nonlinear, both overall and varying byindustry (Oliva, Oliver and MacMillan, 1992; Anderson and Sullivan, 1993). Lesswell-documented is the idea that loyalty may affect satisfaction through familiaritywith the product Halstead et al. (1994) observe that some level of familiarity isnecessary before a customer can fonn expectations about a product

Looking for a few Good Leading Indicators

When measuring marketing perfonnance began, financial output measuresdominated the field. As marketing perfonnance measures evolved, we added ahost of non-financial and input measures to the measurement mix. Financialoutputs will probably always be used as indicators of marketing perfonnance, butthey are snapshots of the present and say little about the marketing health of thecompany in the future. Unfortunately, the proliferation of potential leadingindicators is managerially problematic; for example, the American MarketingAssociation's, 1999 Customer Satisfaction and Quality Measurement Conferenceis entitled "Making Sense of Multiple Measurements."

Looking for a few good leading indicators suggests two research agendas. On

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724 Bruce H. Clark

a micro level, one should attempt to reduce the number of items used tomeasure particular constnlCts, while still retaining enough for reliability.Psychometrically, there are a variety of standard data reduction techniquesavailable for this endeavor, such as factor analysis. In a structural equationmodeling context, Baumgartner and Homburg (1996, p. 144) suggest that threeitems per latent constnlct is a minimum standard for reliable measurement.Regarding the number of constructs managers should attempt to track, simplepsychological limits on the number of items people can juggle in memorysuggests seven is a plausible maximum (Miller, 1956; Lynch and Srull, 1982) -below I will suggest four specific measures.

Beyond this, the academic community sometimes encourages proliferation ofmeasurement schemes, as each scholar suggests his or her own items for aparticular constnlct. For some constructs, we have enough of a history that weshould be moving in the opposite direction: using a standard set of measuresrather than inventing new measures for each study. Research developingparsimonious syntheses of larger sets of measures would be particularly useful,both for managers and for academics who might be measuring multipleconstructs in a single study. Deshpande and Farley (1998b), for example,examine three different sets of measures of the market orientation construct anddevelop a short synthesized inventory that incorporates the core infonnationresearchers and managers need to capture. More of this kind of research isneeded.

On a more macro level, the challenge for marketing scholars is to understandthe nature of causality among multiple constructs, both for advancement oftheory and to advise managers regarding which measures will be most useful fortheir businesses. Regarding the four constructs discussed in the previous section,Figure 2 summarizes the hypothesized relationships. These, and other constnlcts,should be examined jointly to detennine their relationship and mutual influence(see SeInes, 1993, for example).

The challenges in this endeavor are fourfold. First one must establish thedirection of causal relationships. Second, one must identify the fonn of therelationship Oinear, nonlinear, etc,), Third, one must establish the strength of therelationships in practical tenns. Finally, one must understand the temporalrelationships among these measures to truly use them as predictors of overallbusiness health.

In tenns of research approaches, to tndy demonstrate these causal links,researchers will need to model systems of equations using longitudinal data.Stnlctural equations modeling is a technique that may be useful in this context(Diamantopoulos, 1994). One likely source of data would be customer databasesin transaction-intensive industries such as finance, telecommunications, or airtravel. Companies could be assessed on market orientation scales, and customerscould be interviewed or surveyed regarding customer satisfaction and brandequity. Customer loyalty would be available through the database records. Usingthis kind of data, scholars can examine the direction, shape, and dynamics of therelationships.

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Marketing Perfonnance Measures

Figure 2. Hypothesized Interrelationships among Key Measures

725

MarketOrientation

BrandEquity

---- ••••~~ causal relationships..- - - - - - - - feedback relationships

Regarding shape, in several cases nonlinear or contingent relationships havebeen identified, with consequences for both academic modelling and managerialmeasurement Economics certainly suggests the general principle that any effecteventually suffers diminishing returns (i.e. a concave function), but research incustomer satisfaction suggests that in certain competitive situations one may seeincreasing returns to satisfaction (e.g.Jones and Sasser, 1995). Another possiblenonlinear fom1 would be an s-shaped relationship.

Regarding dynamics, to identify leading indicators one is interested in thespeed with which an effect occurs in the presence of a causal agent, and thespeed with which an effect diminishes when the causal agent is removed. Forexample, how quickly does customer satisfaction affect customer loyalty? If

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726 Bruce H. Clark

customer satisfaction subsequently declines, how long will it be before customerloyalty diminishes as well? Speed here may depend on interpurchase times.Someone who has just purchased a car is not likely to be in the market again fora few years, while a customer's relationship with a financial institution mayinvolve multiple transactions in the course of a year. In the fonner case, the effectof satisfaction on loyalty will not be seen for years, while in the latter it mayappear within months.

Cross-industry studies will be important both to identify contingent factorsand to better infonn managers in particular industries which measures may bemost useful. Industry competitiveness appears a particularly likely moderatingvariable in relationships among measures (e.g. Fornell, 1992; Slater and Narver,1994). Because of the importance of temporal relationships, research shouldlook for feedback loops among the measures, as shown in Figure 2; it is likelythat A may cause B in one period, but B may influence A in the next.

Finally, aside from the econometric approach, experimental studies may alsobe useful in examining some relationships. The relationship between satisfactionand brand equity could probably be approached in this fashion. One might alsoincorporate repurchase intention as a measure of loyalty in experimentalapproaches, but market orientation will be harder to approach in this fashion.

What Do I Do While I'm Waiting?

The previous sections suggest a substantial research agenda that will eventuallybear fruit for managers. The question naturally arises, what should managers dowhile they are waiting for this research?

Allowing that we still have much to learn, it seems clear that managers shouldcontinue to track financial measures such as sales and profits. Publicly-tradedfinns are required to report these measures, and internally, one is more likely toreceive budgets from financially-oriented managers if one can show previousfinancial success.

Beyond this, I believe satisfaction and loyalty measurement are the areas inwhich most managers should concentrate in the near tenn. Satisfaction assessescustomer perceptions of the finn's offerings, while loyalty tracks actual customerpurchasing behaviour. Between these two measures, finns should get at least arough indication of competitive strengths and weaknesses and future financialreturn on marketing efforts.

Satisfaction should be assessed relative to customers' satisfaction withcompeting products. Rather than overall satisfaction, finns should measuresatisfaction with each of the different attributes/benefits customers value. Aweighted sum of these items, based on the importance customers place onattributes, will produce a more reliable composite than simple overall items, andshould also identify competitive strengths and weaknesses in the finn's offerings.One fiml I have worked with grades customer satisfaction (A, B, C, etc.)depending on how its customer satisfaction scores compare to the scores ofcompetitors. Piercy (1997) has a number of helpful suggestions on how to

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Marketing Performance Measures 727

measure and use customer satisfaction within the organization.Regarding loyalty, surveys of repurchase intention can be beneficial, but,

where possible, finns should assess loyalty through a database of transactions.For companies with many customers, a good summary measure of loyalty is thepercentage of customers lost in a time period. For companies with feweraccounts, salespeople often have good infonnation on loyalty. This cansometimes be quantified in terms of number of rebids lost Other transaction-based elements of a loyalty constmct might include frequency, recency, andamount of purchase, and breadth of purchase in a finn's line.

For both of these measures it is critical to compare the summary measures totwo referents. First, what are the trends over time in customer satisfaction andloyalty?As Dickson (1997, p. 12) observes, the change or "delta" in measures isoften far more infonnative in tenns of managing marketing than looking atmeasures on a stand-alone basis. Second, each of these measures should bebroken out by market segment Averages taken across segments can masksignificant differences in the threats and opportunities facing managers. For agiven market segment, then, one would hope to see a chart or table with fournumbers measured over time: sales, profit, relative customer satisfaction, andcustomer loyalty. These, in tum, can identify areas for further research ormarketing efforts.

Conclusion

This paper has attempted to layout what we know about the history andinterrelationships among key marketing measures. The three historical trendsidentified - toward non-financial output measures, marketing input measures,and multiple measures - have improved our understanding of marketingperfomlance. The challenge left for further research is to identify the few goodleading indicators that managers can track for the future.

Acknowledgments

The history portion of this paper benefited from comments on a paper presentedat the, 1998 Conference on Business Perfonnance Measurement at CambridgeUniversity.This paper has also benefited from the comments of two anonymousreviewers.

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