3 - Mulhern Customer Profitability Analysis

CUSTOMER PROFITABILITY ANALYSIS:

MEASUREMENT, CONCENTRATION, AND

RESEARCH DIRECTIONS

F r a n c i s J . M u l h e r n

f

A B S T R A C TAs marketing activities become more precisely targeted toconsumers through direct and interactive forms of communication,customer profitability takes on a central role in the development ofmarketing strategies. This paper provides a conceptual andmethodological foundation for measuring customer profitability bygeneralizing approaches to measuring customer lifetime value indirect marketing for broader target marketing applications. Particularemphasis is placed on the precise specification of the inputs into aprofitability analysis and the measures of the degree ofconcentration of profits among customers. An empirical analysisinvolving the profitability of customers in a business-to-businessmarketing context is described, along with research propositions forfuture work on the determinants of customer profitability.

© 1999 John Wiley & Sons, Inc. and

Direct Marketing Educational Foundation, Inc.

CCC 1094-9968/99/010025-16

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JOURNAL OF INTERACTIVE MARKETING

VOLUME 13 / NUMBER 1 / WINTER 1999

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FRANCIS J. MULHERN is AssistantProfessor in the Department ofIntegrated MarketingCommunications at the MedillSchool of Journalism, NorthwesternUniversity, Evanston, Illinois.

INTRODUCTION

Marketing practice is in the midst or a dramatictransformation, from aggregate-level mass mar-keting programs to micro-level programs thatfeature more customized offerings marketedthrough direct and interactive forms of commu-nication. An important feature of these special-ized marketing programs is the ability to ad-dress marketing communications to individualcustomers or small customer segments. In se-lecting which customers to communicate to,and specifying the content of the communica-tions, managers can benefit greatly by knowingthe profitability of individual customers. Differ-ent communication programs can be developedbased on the actual, or projected, profitabilityof customers.

Analysis of customer profitability has been acentral tenet of direct marketing for many years(Shepard, 1990). As interactive forms of com-munication become more commonplace, directmarketing concepts, measures, and methodsneed to be broadened to encompass more tra-ditional marketing practices. Accordingly, thereis a need for greater theoretical and method-ological grounding in these areas to deal withthe varied settings, some quite different fromdirect marketing, in which highly targeted com-munications are now being used.

Customer profitability is the net dollar con-tribution made by individual customers to anorganization. Customer profitability is referredto in the literature with several different terms,including lifetime value (Keane and Wang,1995), customer lifetime value (Berger andNasr, 1998), customer valuation (Wyner, 1996),customer lifetime valuation (Dwyer, 1989), cus-tomer relationship value (Wayland and Cole,1997) and customer equity (Blattberg andDeighton, 1996). Here, consistent with Stor-backa (1998), we use the term customer profitabil-ity since, as we describe below, profitability anal-ysis in many non–direct marketing applicationsdoes not always involve a sequence of purchasesover an extended period of time, and the termvalue has multiple connotations in the market-ing literature. For example, perceived value isused to refer to the ratio of perceived quality

relative to price (Zeithaml, 1988). Customer val-ues are psychological constructs that pertain toconsumer moral judgments. Most studies in themarketing literature that have addressed profit-ability issues have measured profits at the levelof the firm, business unit, product, or brand(e.g., Anderson, Fornell, and Lehmann, 1994).The measurement of profits at the customerlevel has drawn little attention, not because itlacks importance, but because of difficulties inobtaining accurate information on individualcustomer purchase behavior. Measuring cus-tomer profit requires data on individual cus-tomer purchases and variable marketing costsover a period of time. Customer profitabilityanalysis now is possible because of the availabil-ity of large-scale customer databases containinga history of purchase transactions (Blattbergand Deighton, 1991)

As noted by Wyner (1996), customer profit-ability reconfigures traditional marketing prac-tice by treating the customer as an asset analo-gous to other economic units. In this context,marketing decisions are similar to investmentdecisions in that expenditures are evaluated interms of expected returns. Viewed this way, cus-tomer profitability becomes a central tenet ofcustomer relationship marketing (Morgan andHunt, 1994). Knowledge of customer profitabil-ity can improve decision-making for many as-pects of marketing including product and ser-vice development, pricing, and all forms ofmarketing communications including promo-tion and personal selling. Ultimately, customerprofitability provides a metric for the allocationof marketing resources to consumers and mar-ket segments. Marketing efforts are best di-rected at the most profitable consumers. Mar-keting theorists (e.g., Day and Wensley, 1983;Schultz and Schultz, 1998) often note that formost organizations, profits are concentratedamong a small set of customers. However, withthe exception of Schmittlein, Cooper, and Mor-rison (1993), little consideration has been givento how customer profitability is defined andmeasured. In this paper, we delineate the mea-surement issues pertaining to customer profit-ability. Such a conceptualization is importantbecause profitability measures, and the subse-

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quent assessment of the distribution and deter-minants of profitability, will become central tothe data-driven world of interactive marketing.

In the traditional marketing literature, fewstudies have provided sophisticated analysis ofcustomer profitability. Berger and Nasr (1998)provide an excellent treatment of the structuralmodeling aspects for constructing profitabilitymodels. This treatment goes well beyond themore heuristic structural models provided inthe direct marketing literature (Dwyer, 1989;Keane and Wang, 1995). Storbacka (1998) de-scribes customer profitability as a central aspectin relationship marketing, and provides somemeasures for evaluating the distribution of prof-itability across customers. Papers by Schmit-tlein, Morrison, and Colombo (1987) andSchmittlein and Peterson (1987) provide sto-chastic modeling procedures for determiningtwo of the micro-level inputs to a profitabilityanalysis—whether a customer is still “active,”and what future purchase behavior can be ex-pected. While these studies provide importantelements for customer profitability, there is nosystematic treatment in the literature of themeasurement issues involved in customer prof-itability. The purpose of this paper is to formallyset forth the issues that are involved in (1)measuring customer profitability and (2) evalu-ating the concentration of profitability amongcustomers. We go on to discuss how customerprofitability measures can be used for strategicmarketing practice and what research is neededto develop customer profitability as a usefulresearch area. To help demonstrate the impli-cations of the measurement issues raised here,we provide an empirical analysis in a business-to-business marketing context.

MEASURING CUSTOMER PROFITABILITYWhile measuring customer profitability mightappear to be a straightforward process, it isactually quite complex. The exact specificationof a profitability analysis has important implica-tions for marketing decisions based on profit-ability measures. Accordingly, it is crucial toconsider the many specification issues that per-tain to a profitability analysis. The appropriate

specification of a profitability analysis should bemade in the context of the strategic situation,and will vary for different applications. For ex-ample, a profitability analysis done for the pur-pose of allocating a sales force might use cor-porate customer locations as the customer unit,whereas an analysis for direct marketing mightuse individuals as the customer unit. Below weidentify the key measurement components andthe issues involved in specifying a profitabilitymeasure.

Profitability Measurement Components

Specification of Customers. Definition of Cus-tomer Unit. The customer unit is the entity forwhich profitability is computed. Firms often dis-tinguish different types of customers (e.g., con-sumer versus corporate customers for an air-line, store versus catalog shoppers for aretailer). Depending on the application, a prof-itability analysis could be performed for all cus-tomers, or some specified customer unit. Spec-ification of the customer unit delineates thescope of a profitability analysis.

Aggregation of Customer Units. Ideally, a profit-ability analysis is performed for the individualcustomer units; however, in some cases it ispreferable to compute profitability at a higherlevel of aggregation. Higher levels of aggrega-tion are practical when individual customerpurchase data are not available or individual-level marketing is not feasible. For example,consumer packaged goods manufacturersmight not find it economically practical to mar-ket to individual customers because of the lowdollar amount of individual purchases. A higherlevel of aggregation could represent a marketsegment consisting of customers that shouldreceive the same communication.

In consumer marketing, individual custom-ers are often studied at the household level. Inbusiness marketing, the customer unit issue ismore complex. Customer units can be definedin many different ways: an entire corporationwith several holdings, strategic business units(SBUs), divisions of SBUs, departments withindivisions, or specific corporate locations. What-

C U S T O M E R P R O F I T A B I L I T Y A N A L Y S I S


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ever the chosen unit of analysis, considerationmust be given to the comparability of the cus-tomers. Profitability results can be distortedwhen there are major size differences in cus-tomer units.

Existing or Prospective Customers. A profitabilitymodel may be constructed for both existingcustomers and prospective customers. Whilemeasures of profit for both customers and pros-pects are important, it is much easier to buildmodels for existing customers because detailedinformation on their purchases is often avail-able in marketing databases. Information onprospects can be extremely difficult to obtain.The potential profit of prospects can be mea-sured by matching them to existing customerswith data mining and clustering techniques thatidentify similarities among observations.

Determining Which Customers Are “Active.” Cus-tomers in a database vary dramatically in theirpurchase activity. Most customer databases con-tains names of customers who are no longeractive. Should a customer who made extensivepurchases a year ago, but has made no pur-chases in the current year be included? Thedetermination of what qualifies a customer asactive is critical because the profit of each cus-tomer is assessed relative to the profit of othercustomers. The question of delineating the sta-tus of customer has been addressed in detail bySchmittlein et al. (1987) and Schmittlein andPeterson (1994). Those papers compute theprobability that a customer is active given thenumber and timing of purchase transactions.Such an analysis is an important precursor toprofitability measurement. Schmittlein et al.(1993) show that the evaluation of the concen-tration of purchases among customers can bedramatically affected by whether nonusers areincluded in an analysis.

Specification of Products/Services Level ofProducts/Services. Separate profitability analysiscan be performed for individual product linesor brands. For example, a computer manufac-turer could do separate analyses for main-frames, workstations, and desktop systems. Con-

versely, profitability analysis can be performedat an aggregate level such that every element ofthe relationship between an organization and acustomer is included in a single profitabilitymeasure. From a relationship marketing per-spective, all of the brands or products pur-chased should be included. In practice, how-ever, lesser levels of aggregation may beappropriate for certain marketing decisions.

Organizational Level.Depending on data availability, profitability

analysis can be performed at a variety of orga-nizational levels. For example, a computer soft-ware company could compute the profitabilityof customers at the level of its own sales territo-ries, local sales offices, regional sales offices, orthe national level.

Customer Profitability Measure Core Profit El-ement. A profitability model centers on a coreprofit component, typically dollar contribution.Other measures could be used, depending onthe purpose of the analysis. For example, cus-tomers who are opinion leaders or market ma-vens, avid proponents of a product or brand toothers (Feick and Price, 1987), may be valued ata level above what is represented by their ownprofit contribution.

Present or Future Profit.Customers can be evaluated based on present

purchase behavior or of the anticipated futurestream of purchases. One aspect of customerlifetime value models that is not often discussedis the need to forecast individual customer pur-chase behavior over the specified lifetime. Thiscan be performed with well-established forecast-ing techniques (e.g., exponential smoothing orARIMA models), or by using an analog ap-proach where customer purchases are pre-dicted by evaluating the purchase behavior ofsimilar customers in the past. However, modelsincorporating predicted future purchases aresubject to a great deal of forecasting error.

The appropriateness of future-oriented life-time profitability analysis varies by industry.Lifetime analysis is natural for industries such asfinancial services and some business-to-business



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supplier relationships in which customers natu-rally are bound to an organization for a longtime period because of high switching costs.However, in many industries, customer lifetimesare difficult to specify because the length of a“relationship” maybe very short. More impor-tantly, the difficulty in projecting purchase be-havior at the individual level makes lifetimemodels difficult to estimate. Hence, we advocateprofitability analysis of past purchase behaviorinitially, with subsequent analysis of projectedfuture purchases for more sophisticated appli-cations. When future purchases are included ina profitability model, dollars must be adjustedto present profit using a standard net presentprofit procedure, as discussed in research oncustomer lifetime value (Berger and Nasr, 1998;Keane and Wang, 1995).

Brand or Category Profit.Models can be constructed to measure the

realized profit from a customer’s purchasesfrom one company (brand), or the categorylevel profit represented by the sum of a custom-er’s purchases from all companies selling in acategory. Typically, one would first measurebrand profit, and then attempt to estimate cat-egory-level profit. The most complete picture isobtained by measuring both brand and cate-gory-level profit. This allows for the computa-tion of share of requirements—the portion of acustomer’s profit that one company possesses.Marketing resources can then be allocated ac-cording to the available potential profit that canbe obtained by attracting a larger portion ofcustomer category expenditures.

Length of Time Period.For both the analysis of projected future or

actual past purchases, a decision must be maderegarding the length of time that a profitabilityanalysis should encompass. Longer time peri-ods incorporate more purchase cycles andtherefore are less subject to behavioral anomo-lies. However, longer time periods involve thediminished relevance of more historical data(in the case of analysis of past data) or theincreasing inaccuracy of projected future pur-chases as time into the future increases (for

lifetime models). Schmittlein, Cooper, andMorrison (1993) show that the length of thetime affects the measured concentration of pur-chases among customers. Typically the length-of-time-period decision should be made basedon the time-related aspects of customer life-time, interpurchase times, and an organiza-tion’s planning cycle.

Cost Allocations Assignment of VariableCosts. A fully developed profitability modelfeatures the assignment of variable costs to cus-tomers. Variable costing shifts a revenue analy-sis to a contribution margin analysis. When vari-able costs cannot be allocated, less completeformulations can be used that assign costs tomarket segments, or collapse variable costs intofixed costs. More sophisticated customer data-bases contain data on the costs for marketingcommunications and other customer-specificvariable costs. In fact, the mere matching ofvariable marketing costs to revenue streams ofcustomers is valuable information for market-ing decisions. In most cases, fixed costs are notallocated to customers (Berger and Nasr, 1998;Dwyer, 1989). Foster, Gupta, and Sjoblom(1996) provide a more detailed description ofthe cost accounting issues that should be con-sidered when computing customer profitability.

Assignment of Acquisition Costs.In some industries there are specific and de-

finable acquisition costs, such as prospectingsales calls, product specifications, and price dis-counts. More typically, acquisition costs cannotbe directly assigned to individual customers. Forexample, consumer packaged goods manufac-turers spend a great deal of money in mediaadvertising, much of which is intended to ac-quire new customers. However, there is no wayto match media expenditures with the custom-ers that are attracted. Thus the only possibleallocation would be to apply an average cost toall customers—a practice that simple lowerseach customer’s computed profitability by aconstant, leaving the relative profitability ofeach customer unchanged. In such instances,acquisition costs are best left out of a profitabil-ity analysis.



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A final aspect we note is the possible inclu-sion of customer responsiveness to marketingefforts. Typically, profitability models are“closed” in that the value of a customer is as-sumed to exist independently of marketing ac-tions. However, the value of a customer is partlya function of the marketing efforts directed atthat customer. Deighton, Peppers, and Rogers(1994) note the profitability of a customer de-pends in part on a firm’s ability to leverage itsaccess to the customer. A firm can increase acustomer’s actual or potential profit by improv-ing product or service performance, changingprices or conducting effective communications.Accordingly, a more advanced form of profit-ability analysis would include response coeffi-cients that account for the effects of marketingefforts on customer profit. This is a recursivemodel, and is analogous to diffusion of innova-tion models that represent the diffusion rateand saturation levels as a function of marketingeffort (Mahajan, Muller, and Bass, 1990).

Structural Profitability Model. Once the mea-surement specifications discussed above havebeen established, a structural measurementmodel can be constructed. Since Berger andNasr (1998) provide an excellent overview ofalternative model specifications, we refrainfrom a detailed discussion of modeling issueshere. We describe two basic profitability mod-els. The first applies to situations in which fu-ture purchases are forecasted and profitability isestimated by discounting future cash flows andvariable costs to a present value. This is thestandard customer lifetime value approach.More detailed formulations for various pur-chase scenarios are described by Berger andNasr (1998). A general formulation for cus-tomer lifetime value is:

CPi 5 Ot51

TOj51

Ji

~ pijt 2 cijt! 2 Ok51

Ki

mcikt

~1 1 r!t (1)

where

CPi 5 the profit of customer i to a firm,

pijt 5 the price of purchase j made by customeri in period t,

cijt 5 the unit cost of purchase j made by cus-tomer i in period t,

mcikt5 variable marketing cost, k, for customer iin period t,

r 5 the discount rate for money establishedto reflect the riskiness of cash flow.

Customer lifetime models are appropriatewhen customers have ongoing relationshipswith organizations and future purchase and coststreams can be accurately forecast at the indi-vidual level. In marketing situations where life-time analysis is not relevant or when accurateprojections of purchase cannot made, be a his-torical profitability analysis can be performed.The following model applies for historical prof-itability models:

CPi 5 FOt51

T SOj51

Ji

~pijt 2 cijt! 2 Ok51

Ki

mciktDG~1 1 I!t ~2!

where the elements are defined as in equation 1except that I represents an adjustment factor toeliminate the time value of money (e.g., annualinflation rate).

Equations 1 and 2 incorporate customerprofit over a series of discrete time periods. Foreach time period, the dollar contribution mar-gin (price minus costs of good sold) for allpurchases is computed, and the variable mar-keting costs are identified. The margins andvariable costs for all time periods are adjusted toremove time value of money effects, and to-taled. Note that this formulation does not implythat variable marketing costs in time period tnecessarily apply to purchases in time period t.Since this is a simple additive model, it makesno difference if net contribution margin (priceminus cost of good sold minus variable market-ing costs) is computed separately for each timeperiod, or computed singly after margin andvariable costs are discounted to present timeseparately.



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EMPIRICAL DEMONSTRATIONWe provide a demonstration of customer profit-ability in a business-to-business marketing situa-tion involving pharmaceutical sales. Pharmaceuti-cal manufacturers have sales forces that call onphysicians to influence prescription writing be-havior. The measurement problem involves deter-mining the profitability of individual physicians sothat marketing communications and personal sell-ing efforts can be allocated accordingly. We describea profitability analysis performed on three sales ter-ritories of a pharmaceutical manufacturer over asix-quarter period from July 1995 to December1996. A total of 834 individual physicians are locatedin the three territories. Data on prescription writingbehavior were obtained from a large data vendorthat sells syndicated data on prescriptions written byphysicians to pharmaceutical manufacturers. Dataon the variable costs for marketing efforts were ob-tained from internal company records on sales calls,marketing communications, and free samples dis-tributed as promotions.

Customer profit is computed by aggregatingtotal prescription dollar volume and subtractingvariable marketing costs, as shown in equation 2.Acquisition costs were not specified and are not

included in the analysis. Since we only deal withhistorical purchases, we have no need for a dis-count factor for money. To incorporate inflation-ary effects, dollars were adjusted using the averageprice increase for all the drugs sold by the phar-maceutical manufacturer being studied. The spec-ifications for the profitability measure are pro-vided in Table 1. Descriptive statistics forcustomer profitability are shown in Table 2. Themean profitability per physician was just over$7,000 for the two-year period. Profitability perphysician varies quite dramatically as it rangesfrom below negative $12,000 to over $62,000. Thewide variability in profitability per customer canbe attributed to physician specialties, differences

T A B L E 1Specification of Customer Profitability Measure for Empirical Analysis

Measurement Issue Chosen Specification

Specification of CustomersCustomer Unit PhysiciansAggregation level IndividualsExisting or Prospective Customers ExistingActive Condition All physicians writing one or more prescriptions

Specification of ProductsAggregation of products All products sold by firmOrganizational level Three sales territories

Customer Profitability MeasureCore profit element Dollar contributionPresent of Future Profit PresentBrand or Category Profit Brand (company)Length of time period 1.5 years

Cost AllocationsAssignment of variable costs Sales calls, product samples, direct mailAcquisition costs None

T A B L E 2Descriptive Statistics on Customer Profitability forPhysicians

Number of Obs. 834Maximum $ 62,407.20Minimum $212,814.12Mean $ 7,026.04Median $ 3,536.68



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in size of patient loads, differences in physicianpreferences for pharmaceutical brands, and vari-ability in physician behavior with respect to effi-cacy and appropriateness of pharmaceuticals. Wediscuss the analytic interpretation of the customerprofit measure for this empirical setting in thenext section.

THE CONCENTRATION OF CUSTOMERPROFITABILITYOne of the primary uses of customer profitabil-ity analysis is the evaluation of the how profit-ability varies across customers. Assessing the dis-tribution of profitability is extremely importantbecause it reveals the extent to which an orga-nization depends on a small set of customers forits profits. Information on the distribution orconcentration of profits can also be used fortargeting marketing decisions. While much at-tention is placed on the concentration of profitsamong customers (the so-called 80/20 rule),

very little research in the marketing literaturehas addressed the measurement and meaningof such profit concentration. One exception is apaper by Schmittlein et al. (1993) that providesa statistical basis for evaluating purchase con-centration with the negative binomial model.Their empirical analysis deals strictly with pur-chase volumes, not profitability.

At the simplest level, an assessment of thedistribution of customer profit can be made byobserving a ranking of computed profits fromhighest to lowest. Consider a two-dimensionalplot in which the vertical axis represents cus-tomer profit and the horizontal axis representscustomers—in decreasing order from highest tolowest profit. The straight line in Figure 1 de-picts the special case of all customers havingexactly the same profit. While this is not a typ-ical distribution of customer profit, it may applyin some situations such as a single purchaseevent where a flat price is charged to all custom-ers and marketing costs do not vary across indi-vidual customers (e.g., admission to a parkinggarage on a single day).

A more likely ordering of customer profit isrepresented by the curve Figure 1. In such asituation a relatively small number of customershave a very high level of profit, while the bal-ance of customers have a low, and in somecases, negative, profit. This type of distributionappears to be prevalent and business and con-sumer marketing contexts based on anecdotalevidence. In Figure 2, we show the ordering of

F I G U R E 1Hypothetical Customer Profit Orderings

F I G U R E 2Customer Profit Ordering for Physicians: Highest to Lowest



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customer profit for the physicians in our empir-ical demonstration. Note that there are a fewextremely high-profit customers (about 100 cus-tomers generate over $20,000 in profit each)and a large number of very low-profit custom-ers. This figure clearly shows that a large por-tion of profits comes from a relatively small setof customers.

Another tool for assessing the distribution ofcustomer profit is a frequency distribution asshown in Figure 3. Importantly, one should becareful not to confuse the customer profit plotsin Figure 2 with a frequency distribution inFigure 3. In Figure 2, the vertical axis is repre-sents profit while the horizontal axis representscustomers, in descending order of profit fromleft to right. In the frequency distributionshown in Figure 3, the vertical axis represents afrequency and the horizontal axis representscustomer profit dollar ranges.

Viewing the distribution of customer profitsbecomes cumbersome when a large number ofcustomers exist. Therefore, it is useful to con-vert the raw data into percentiles, and plot thecumulative percentile of customer profit againstthe cumulative percentile of the number of cus-tomers. (A common practice in direct market-ing is to classify customers into 10 deciles. Whiledeciles have the advantage of being easily com-municated to managers, percentiles allow for amuch more precise level of analysis.) A graphi-cal representation of percentiles is known as aLorenz curve in economics, where it is used torepresent the concentration of incomes (Sen,1997). The convention in economics is to rep-resent the cumulative percentiles on the hori-zontal axis such that the first percentile corre-sponds to the lowest-income consumers whilethe highest percentile corresponds to the high-est-income consumers. We use this convention

F I G U R E 4Lorenz Curve for Customer Profit

F I G U R E 3Frequency Distribution of Customer Profit for Physicians



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in Figure 4, which shows a hypothetical Lorenzcurve. The horizontal axis represents cumula-tive percentiles of customers ordered from theleast to the most profitable. The straight 45-degree line represents points where the cumu-lative percentile of customers is equivalent tothe cumulative percentile of customer (e.g.,40% of the customers provide 40% of the totalprofit). This condition is analogous to the flatcustomer profit curve in Figure 1. When cus-tomer profit is distributed unevenly across thepercentiles, the Lorenz curve bows downward.The extent of this bow represents the degree ofunevenness in customer profit. The most ex-treme form of profit concentration would be ifone customer represented 100% of all profitswhile all other customers provided zero profit.This situation can be represented by a Lorenzcurve that goes along the horizontal axis untilthe 100th percentile, at which point it goesvertically up to the 45-degree line.

An important observation is that a sharplydescending curve for the ordering of customerprofit, as in Figure 2, corresponds to a skewedfrequency distribution (see Figure 3) and ahighly bowed Lorenz cure (see Figure 4).

A shortcoming of using the Lorenz curve forprofitability analysis is that it cannot portraypercentiles of customers who represent a finan-cial loss to a firm. Customer profitability will be

negative when variable marketing costs exceedunit contribution. Conceptually, when some ofthe percentiles generate losses, the bow woulddrop below the horizontal axis. However, this isnot numerically attractive because there is nosensible interpretations for negative values ofthe vertical axis. A suitable remedy to this prob-lem is to flip the Lorenz curve above the 45-degree line as advocated by Schmittlein et al.(1992) and Storbacka (1997). By flipping thecurve above the 45-degree line, we avoid thepresence of the lower bound present in a tradi-tional Lorenz curve. The lower bound elimi-nates the possibility that a portion of the cus-tomer base constitutes more than 100% of theprofit. Also, the inverted Lorenz curve is moreamenable for marketing purposes, since mar-keters are often concerned with the highestprofit market segment. Also, with the invertedLorenz curve, the placement of customersalong the horizontal axis is consistent with thehighest-to-lowest ordering used in Figures 1 and2. Flipping the curve above the axis is achievedby reversing the percentiles on the horizontalaxis such that the first percentile represents themost profitable one percent of customers. Fig-ure 5 shows the inverted Lorenz curve for thephysicians in our empirical analysis. Note thatthe curve is quite bowed and surpasses the100% line. The apex of the curve represents the

F I G U R E 5Inverted Lorenz Curve for Physicians



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portion of customers who are profitable. Eachpercentile to the right of the apex (represent-ing about 15% of the customers) reduces theoverall profitability of the customer base. Profitsare quite concentrated, as 20% of the customersaccount for 65.5% of the profits and half thecustomers account for 95.5% of the profits.

The disparity of customer profit evident inFigure 5 can also be depicted mathematically.Many measures of disparity can be applied tocustomer profitability. These include the range,the standard deviation, the mean absolute devi-ation and the coefficient of variation. Here wedescribe two more popular measures of incomedisparity that can equivalently be applied to aLorenz curve for customer profit. For a stan-dard Lorenz curve (Figure 4), the Schultz coef-ficient is a measure of the longest vertical dis-tance between the 45-degree line and theLorenz curve (Cowell, 1977). The Schultz coef-ficient is represented geometrically by the linein Figure 4. The Schultz coefficient has a min-imum of zero and a maximum of 100, and ismeasured in the units of the vertical axis (cu-mulative percent of customer profit). For theinverted Lorenz curve in Figure 5, the Schultzcoefficient can exceed 1.0, as there is no up-ward bound in profit concentration (since someconsumers could theoretically contribute infi-nite losses). Higher Schultz coefficients corre-spond to greater concentration of customerprofit.

A second measure of profit concentration isthe Gini coefficient, which is the geometric areabetween the 45-degree line and the Lorenzcurve divided by the total area under the 45-degree line (Sen, 1997). Mathematically, theGini coefficient is one half of the relative meandifference — the mean of the absolute values ofthe differences of all pairs of profit levels. Forthe inverted Lorenz curve in Figure 5, we candefine a modified Gini coefficient as the areabetween the curve and the 45-degree line di-vided by the area under the 45-degree line(Storbacka, 1998). Compared with more sim-plistic formulations (range, standard deviation,etc.), the modified Gini coefficient is attractivebecause it is based on the differences in profitof all pairs of customers as opposed to the over-

all mean or extreme values that determineother disparity measure. Sen (1997) and Cowell(1977) provide comparisons of measures of dis-parity used in economics. The Gini coefficientfor a Lorenz curve is defined as:

Modified GINI 5A

A 1 B(3)

where A is the area above the 45-degree line inFigure 5 and B is the area below the 45-degreeline. For a standard Lorenz curve, the Gini co-efficient ranges from zero (even distribution ofprofit) to one (all profits from one customer).For the inverted Lorenz curve, the modifiedGini coefficient, like the Schultz coefficient, hasa minimum of zero but no upward bound. Inour empirical example, the modified Gini coef-ficient is 0.666. The Schultz coefficient is 50.02.Each of these numbers represents a very highlevel of disparity in customer profit. Their inter-pretation of these measures becomes moremeaningful in the context of comparing mea-sure of disparity of profits across firms, product,sales territories or time.

One other measure of disparity (concentra-tion) of customer purchases is proposed bySchmittlein et al. (1993). They show that if pur-chasing rates are distributed gamma across thepopulation of customers, one parameter of thegamma distribution, r, can act as an inversemeasure of purchase concentration. Mathemat-ically, 1/r is the squared coefficient of variationin purchase rates across customers. While this isto a useful measure of concentration, it is not aseasily computed or communicated as the moregeometric-based measures described above.

STRATEGIC USES OF CUSTOMERPROFITABILITY MEASURESCustomer profitability measures can be used formany marketing decisions at both a strategicand a tactical level. Here we discuss uses ofcustomer profitability for market segmentationand resource allocation decisions.



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Market SegmentationMost discussions in the marketing literature andtextbooks describe behavioral segmentation interms of usage volume such as heavy users, me-dium users, and light users (Kotler, 1997) orbrand-buying behavior such as brand loyals,other brand loyals, and brand switchers (Ros-siter and Percy, 1997). Customer profitabilitycan serve as another important basis for behav-ioral segmentation because of the central im-portance of profits.

Profitability measures can help organizationstarget marketing efforts to the most lucrativemarket segments. At the simplest level, two seg-ments may be formed—customers worth keep-ing (retention segment), and those that are notworth the effort. If customers are targeted in aone-to-one fashion purely in terms of measuredprofit, then the optimal target market, in a mi-croeconomic sense, consists of those customerswhose marginal return exceeds the marginalcosts of marketing to them (Baumol, 1977).This is equivalent to selecting all customerswhose expected profit score exceeds zero. Thatis, ignoring the time value of money for themoment, all customers would be targeted whomeet the condition:

Oj51

J

~ pijt 2 cijt! 2 Ok51

K

mcikt . 0 (4)

At a more advanced level, several segmentsmaybe formed based on customer profit. Forexample, one may identify a best customers seg-ment consisting of the highest-profit customersfor whom extensive retention programs couldbe developed. Another valuable segment is lessprofitable customers who are similar to the bestcustomers in purchasing pattern of generalcharacteristics. Programs can be developed tomigrate these customers to higher levels of prof-itability.

Segmentation analysis performed with stan-dard basis, exclusive of an economic measure ofprofit, often yield segments that are not eco-nomically viable to the firm. Thus customerprofit adds information to existing segmenta-

tion approaches that ascertains the economicattractiveness of a segment, and guides deci-sion-making concerning the amount of market-ing effort that can be profitably expended on asegment. This approach represents a multivari-ate basis for segmentation, which is more prac-ticable with large-scale databases that with sur-vey-based which if often too spares to use formultiple basis segmentation.

Resource AllocationMarketing mix decisions involve the allocationof marketing budgets across customers or mar-ket segments. When a valid profitability mea-sure is available, resource allocations can bemade in a manner that maximizes the return onthe marketing investment. This is accomplishedby matching customer profitability with mea-sure of customer responsiveness to marketingefforts. Mantrala, Sinha, and Zoltners (1992)discuss the use of such customer response infor-mation, in combination with customer profit(in their case potential profit). They show thatresources should not simply be allocated to cus-tomers or market segments in direct proportionto profit. Rather, resources should be allocatedaccording to both profit and responsiveness.For example, one would not make high expen-ditures in personal sales calls to a high-profitcustomer if it were known that sales calls to thatcustomer were completely unrelated to pur-chase behavior. Table 3 shows how profitabilityand responsiveness can be combined to formtarget segments that can facilitate resource allo-cation. Knowledge of both profitability and re-sponsiveness of customers can help a firm lever-age its marketing efforts in a more profit-maximizing manner that analytic approachesbased solely on market-level responsiveness thatprevail in the literature (Hanssens, Parsons, andSchultz, 1990).

We note one additional measure that per-tains to resource allocation with respect to thevalue of a customer of an organization. Custom-ers sometimes vary in terms of the degree towhich they can be replaced. Customers who areeasily replaced, even if they have very highprofit levels, are not as attractive for marketingefforts as high-profit customers who are difficult



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to replace. To incorporate replacement costinto a profitability analysis, the measure knownas Tobin’s q can be applied. Tobin’s q is theratio of the market profit of an investment tothe replacement cost (Hayashi, 1982). Convert-ing absolute profitability measures to Tobin’s qcan direct resource allocations to customersbased both on their profitability and replace-ability.

DISCUSSION AND FUTURE RESEARCHThis paper establishes a foundation for measur-ing customer profitability and assessing dispar-ity of profitability across customers. As de-scribed above, customer profitability is animportant component for marketing practicesthat involve communicating with individual cus-tomers, particularly with interactive media. Agreat deal of additional research is required todevelop a knowledge base in three importantareas: (1) what factors act as determinants ofcustomer profitability (as in a scoring model indirect marketing [Shepard, 1990]), (2) whatfactors determine the degree of disparity ofprofits across customers, and (3) how customerprofitability measures should be incorporatedinto marketing planning.

While profitability is obviously an outcome ofprices, unit costs, unit volume, purchase fre-

quency, and variable costs, there are other, non-economic factors that are likely to relate toprofitability. For example, Reichheld (1996)claims that the profitability of a customer in-creases over the tenure of a customer relation-ship because variable costs decline and pur-chases may become more concentrated with asingle seller. Another factor that may relate tocustomer profitability is customer satisfaction.Anderson et al. (1994) find a relationship be-tween customer satisfaction, measured at theindividual customer level, and firm profitability,measured at the firm level. Implicit in this find-ing is that satisfaction at the individual cus-tomer level is correlated with individual cus-tomer profitability. This relationship existsbecause more satisfied customers are likely tobe more profitable because they may be lessprice sensitive, and less prone to purchase fromcompetitors. This relationship is also likely toexist at the individual customer level. In thecontext of a customer orientation, customerprofitability is likely to be greatest for customerswhose needs are best satisfied by the productoffering. In fact, early proponents of the mar-keting concept emphasized that a profit focus asopposed to a sales volume focus as one of theelements of the marketing concept (Bell andEmory, 1970). However, only now, with theavailability of customer databases, can customer

T A B L E 3Combining Customer Profitability and Responsiveness for Market Segmentation



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profitability be easily measured and included inconsumer-oriented marketing strategies.

Customer profitability is also likely to be de-termined by several other behavioral factors in-cluding price sensitivity, brand loyalty, and pur-chase timing, as well as perceptual factors suchas perceived value, brand attitude, and attitudetoward advertisements. Empirical research inneeded to determine if, and under what condi-tions, factors such as these relate to customerprofitability.

Research is also needed to explore the distri-bution of customer profitability across customers.The distribution of profitability is likely to varydramatically for different brands, products andindustries. For example, Schmittlein et al. (1993)find that profit concentration levels differ across avariety of consumer packaged goods. Identifica-tion of what determining factors relate to thedegree of disparity in profitability can help man-agers understand how marketing efforts relate toprofitability at the customer level. The degree ofdisparity is likely to be influenced by factors suchas the breadth of assortment and prices offeredand the heterogeneity of customers needs andbuying behaviors. Further research in needed toidentify how such factors influence the disparityof profitability across customers.

A great deal of research in marketing hasexplored how sales levels and market share re-spond to marketing efforts. What is needed is asimilar stream of research for how customerprofitability relates to marketing efforts. This

would elevate customer profitability models to arecursive level and would allow organizations todevelop a systematic approach to managing re-lationships with customers in a profit maximiz-ing fashion. Table 4 provides a list of researchpropositions drawn from this section that canguide empirical research on customer profit-ability.

One methodological area in need of researchconcerns the prediction of individual customerpurchasing behavior so that lifetime models canbe calibrated. While the direct marketing liter-ature has explored many aspects of lifetimeprofitability models, there is little research thatdevelops methods for accurately forecasting in-dividual customer purchase behavior. Suchforecasting is subject to a great deal of errorbecause of the stochastic nature of individualpurchase decisions. Research building on thework by Schmittlein and Peterson (1994)should help address this problem.

CONCLUSIONCustomer profitability analysis has importantimplications for marketing decision-making.Knowledge of the profitability of individual cus-tomers can help organizations segment mar-kets, allocate marketing resources and specifymarketing mix elements in a way that returnshigh levels of profits. In the previous section wenoted two straightforward uses of customerprofitability measures—market segmentation

T A B L E 4Suggested Research Propositions

Customer profitability is positively related to customer satisfaction.Customer profitability is positively related to brand loyalty.Customer profitability is positively related to the length of a customer relationship.Customer profitability is positively related to the match between a product’s benefits with the customer’s needs.Customer profitability is positively related to the quantity and quality of marketing communications to the customer.Customer profitability is inversely related to price sensitivity.Customer profit is directly related to the degree of favorableness of customers’ attitudes toward a company or brand.Customer profit is directly related to the portion of a customer’s business that a company owns (share of

requirements).The concentration of customer profitability is positively related to the breadth of brands and product lines offered.The concentration of customer profitability is positively related to the variability in prices offered.



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and marketing resource allocation. Further re-search is needed to build theories and modelsof marketing practice that incorporate cus-tomer profitability.

The inclusion of customer profitability in mod-els for segmentation and other marketing prac-tices can focus attention more on the manage-ment of customers. This perspective represents asubstantial change in the way organizations thinkabout their markets. Much of marketing today isdefined by, and acted on, in terms of the market-ing mix variables. In practice, the marketing man-ager’s function to manage the variables repre-sented by the 4 Ps. The use of customer databasesto measure individual customer profitability helpsshift the emphasis from managing marketing mixvariables to managing customers. This shift facili-tates the implementation of the marketing con-cept which, despite its central presence in market-ing theory, is not actively practiced by manyorganizations. As an operational tool, customerprofitability analysis fits well into the customer/relationship perspective of marketing practice as aframework for marketing management (Gron-roos, 1991; Morgan and Hunt, 1994). In this man-ner, customer profitability analysis plays a centralrole in data-driven relationship marketing andindividual customer-based marketing communi-cations.

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