the impact of need frequency on service marketing strategy

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The Impact of Need Frequency on ServiceMarketing StrategyEileen Bridges , Katherine B. Ensor & Kalyan RamanPublished online: 08 Sep 2010.

To cite this article: Eileen Bridges , Katherine B. Ensor & Kalyan Raman (2003) The Impact ofNeed Frequency on Service Marketing Strategy, The Service Industries Journal, 23:3, 40-62, DOI:10.1080/714005114

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The Impact of Need Frequency on Service Marketing Strategy

EILEEN BRIDGES, KATHERINE B. ENSOR and KALYAN RAMAN

Strategic marketing decisions for services are complicated byfactors that distinguish services from goods. Because servicesare intangible, variable, and the production and consumptionexperiences are inseparable, trial of the identical service to bepurchased cannot be offered. Services cannot be inventoried, sopotential customers are lost if they are unaware of a particularservice or it is unavailable during their time of need. Partiallydue to the difficulty of matching service supply to demand,service marketers typically assume that it is best to work towardretaining current clients, rather than focusing too much onattracting new customers. If there is an ongoing or frequentperiodic need for the service, it may indeed be less expensive tomaintain an existing customer relationship than to build a newone; however, for services that customers require infrequently,the marketer must find ways to build awareness and attract newcustomers. Just as marketing strategies for manufactured goodsdepend on whether the items are frequently purchased ordurable, more effective service marketing decisions may beobtained by considering whether customer needs are frequent ornot. We discuss these differences in general terms, then providean application, developing an optimal promotion strategydecision model for an infrequently purchased service. Weconclude that customers in the market for infrequently purchasedservices have particularly high needs for product awareness andpurchase risk reduction, influencing strategic marketingdecisions.

Eileen Bridges is in the Department of Marketing, Kent State University, Kent, USA. KatherineB. Ensor is in the Department of Statistics, Rice University, Houston, USA. Kalyan Raman is inthe School of Management, University of Michigan at Flint, USA.

The Service Industries Journal, Vol.23, No.3 (May 2003), pp.40–62PUBLISHED BY FRANK CASS, LONDON

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INTRODUCTION

Based on typical customer needs, service products may be classified as eitherfrequently or infrequently purchased. Thus, services such as fast food andfilling stations that are needed on a daily or weekly basis may be thought ofas frequently purchased, while less frequent or unexpected needs characteriseservices that are infrequently purchased. Examples of the latter categoryinclude emergency health care or unexpected car repair. Some servicemarketers consider the possibility of a third category that includes services forwhich the purchaser makes a one-time decision for continuous service, suchas an eating or a health club. For the present study, we focus on services forwhich customers make a decision each time they make a purchase, leaving thecontinuous-delivery service category for future research.

Highlighting the differences between marketing strategy decisions forfrequently and infrequently purchased products, the goods marketingliterature provides substantial streams of research comparing packagedgoods to consumer durables. Packaged goods marketers have an extensiveliterature describing models that test the impact of marketing mix variableson customer buying behaviour (for a review, see Blattberg and Neslin[1990]). Because their goal is typically to build market share throughincreased repeat purchasing (as opposed to switching) behaviour, they maytry to enhance awareness and trial of a new product among their targetmarkets, who will then return for future purchases. As packaged goods arefrequently purchased and not a major investment, they are not a riskypurchase for the consumer; thus, buyers need not rely on word-of-mouth tohelp them decide which brand to buy. Instead, they might buy on impulse ifthey see an attractive display, or take advantage of a coupon offer to try anew product. Researchers typically assume a heterogeneous target market,i.e. one in which different households respond differently to variousproducts and promotional activities, and use study results to optimise themarketing mix for each target market.

Marketers who wish to optimise the marketing mix for a durable goodoften use a form of diffusion model (see Mahajan, Muller, and Bass [1990]for a comprehensive review), which typically assumes a homogeneoustarget market and forecasts aggregate-level purchases. Such models mayincorporate one or more marketing mix elements – they have beendeveloped for price [Kalish, 1983; Robinson and Lakhani, 1975],advertising [Horsky and Simon, 1983; Teng and Thompson, 1983], productcharacteristics [Srivastava et al., 1985], and combinations of elements[Kalish, 1985; Parker and Gatignon, 1994]. Thus, the marketing mix may beselected with the goal of maximising profits resulting from cumulativeproduct adoptions. Because the focus in durable goods marketplaces is on

41IMPACT OF NEED FREQUENCY ON MARKETING STRATEGY

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first purchases, trial is not easy to obtain and customers rely much moreheavily on word-of-mouth than they do when buying frequently purchasedpackaged goods. Further, because the customer is often unable toexperience ownership before buying a durable good, and because a durablegood typically requires a substantial investment, the purchase of a durablegood is more important and involves greater risk than does purchase of apackaged good.

Based on the differences that have been observed between frequentlyand infrequently purchased goods, a similar differentiation might be usefulin defining the marketing mix for services. Decision models for frequentlypurchased services would be expected to focus on developing repeatcustomers and optimising marketing decisions for various target segments,while decision models for infrequently required services would be morelikely to centre on reducing risk and encouraging initial adoption. A numberof studies in service marketing have addressed frequently purchasedservices, but infrequently purchased services have received insufficientattention. The purpose of the present study is to further the development ofservice research for infrequently purchased products, and to provide someinsight comparing them to frequently purchased services. We begin byreviewing the relevant literature. Next, we develop a model for promotionalspending decisions in an infrequently purchased service, using data from ahome inspection company. In the concluding section, we discuss how ourresults differ from those for a frequently purchased service, and identify theneed for further research in this area.

PREVIOUS RESEARCH IN SERVICE MARKETING

The literature in service marketing mentions the impact of purchasefrequency on marketing strategy. For instance, Lovelock [1983]recommends classifications based on, first, whether customers have a‘member’ relationship with the service provider; second, whether servicedelivery is discrete or continuous; third, match between supply and demand;and fourth, location/method of service delivery. Each of theseclassifications relates in some way to the frequency of customer need –further, Lovelock notes [1983: 10] that for goods, durability hasimplications for both communication and distribution strategies. Inaddition, Bolton and Lemon [1999] find that service customer usage ratesinfluence satisfaction, through evaluation of economic fairness (price andvalue) of the exchange. Thus, there is evidence in the literature that needfrequency is relevant to marketing decision making.

Services for which there is a frequent need are more commonly seen in theliterature than those that are infrequently required, possibly because they are

42 THE SERVICE INDUSTRIES JOURNAL

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more a part of our everyday lives. For example, the SERVQUAL scale, whichoffered an important early contribution in the area of measuring consumerperceptions of service quality, was developed and tested in five frequentlypurchased service categories, including appliance repair/maintenance, retailbanking, long-distance telephone, securities brokerage, and credit cards[Parasuraman, Zeithaml and Berry, 1988]. Other researchers developingservice marketing strategies have performed empirical work usinghaircutting, dental work, repair service, telephone service, airlines, hotelcheck-in, credit cards, hotels, ATMs, and fast food [Bitner and Hubbert, 1994;Bolton and Drew, 1994; Davidow and Uttal, 1990]. The large number ofexamples available suggest that appropriate strategies for marketingfrequently purchased services are already being developed and tested.

To obtain a more complete comparison of the literature’s relative focus onfrequently and infrequently purchased services, we obtained abstracts ofarticles published during a ten-year period from 1988 through 1997. Theseabstracts covered all articles appearing in the Service Industries Journal, theJournal of Services Marketing, and the International Journal of ServiceIndustry Management (beginning with the first volume in 1990). In addition,all service-oriented articles appearing in the Journal of Marketing, MarketingScience, the Journal of Marketing Research, and the Journal of ConsumerResearch were included. This search resulted in a total of 812 article abstracts.Based on these abstracts, we counted the number of articles per year, and foreach article, identified whether or not it was illustrated by an example from aspecific service product category. The total number of articles that includedexamples was 418, and the proportion of articles that included a specificexample was fairly stable over time, averaging just over 50 per cent.

To compare frequently and infrequently purchased services, we dividedthe articles offering specific examples into two groups, according to thenature of the example service. The group of frequently purchased servicesincluded hotels, restaurants, retail, banking and financial services, while thegroup of infrequently purchased services included education, tourism, realestate and professional consulting. Overall, only 71 of the 418 articlesdescribing specific examples, or less than 20 per cent, examinedinfrequently purchased services. These 71 articles offer some insight intostrategies for marketing such services.

The findings of the articles describing studies of infrequently purchasedservices may be categorised into four groups: (1) predictions ofperformance, quality, or satisfaction, (2) recommendations for marketingstrategy and/or the marketing mix, (3) observations related to marketingresearch, and (4) advice for managers. The advice given in the last categorymay be further subdivided into: (4a) projected impact of the businessenvironment, including socio-cultural, economic and political factors, (4b)


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recommendations regarding people, including human resources andcustomer interaction, (4c) recommendations for improving the serviceprocess, and (4d) findings regarding specific industries.

A total of 21 articles representing a variety of industries offer keyfindings related to performance, quality, or satisfaction. Performance maybe measured directly or indirectly, and is found to depend on such factors asaccessibility, size/scope, uniqueness, and reputation. Quality comes throughtangibles, communication, reliability, productivity, and effectiveness; also,personal encounters or relationships may influence measures of quality. Theservice characteristics found to drive performance and quality make sensefor infrequently purchased services, because they address customer needsfor information and risk reduction through such factors as accessibility,communication, effectiveness, and word-of-mouth.

Eight articles based on research in the tourism and consulting industriesmake suggestions regarding strategy and the marketing mix. Market shareis often seen as the goal of strategy decisions, which is consistent with aneed to increase awareness among potential customers. In addition, meetingconsumer information needs is seen as important, supporting the idea thatmost potential customers of an infrequently purchased service have notpurchased previously. Sales performance depends on competition as well asresources including quality, price, availability, and range of services – thesecharacteristics are helpful in reducing customer risk. Thus, the strategyresearch that has been done focuses on the critical needs in infrequentlypurchased services for potential customer awareness and risk reduction.

The tourism and consulting industries provide the most work related tomarketing research; nine articles were counted here. Researchers areadvised to set objectives before designing their research and to use properdata collection and analysis procedures. This is obviously important advice,but not unique to infrequently purchased service products.

Among the managerial advice offered, findings regarding the businessenvironment come from four articles that suggest prosperity, culture,urban landscape, infrastructure and other services influence businesssuccess. Eight articles reflect the importance of people in services, makingrecommendations regarding training, strategic planning, utilisingnetworks, interviewing techniques for recruiting, and continuing customerrelationships. Service process improvement suggestions come from fivearticles, including dealing with risk, use of value chains in operations, andoffering written customer service policies. The remaining 16 articles,based on a wide variety of industries, offer advice specific to the industrystudied. Thus, the literature suggests (not surprisingly) that infrequentlypurchased services require careful management of people, process, andphysical evidence.


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Comparing Need Frequency and Relationship Classification

A common thread in the service marketing literature relates to the idea thatsome services are more amenable to relationship marketing than others.Relationship marketing, a term first used in connection with services byBerry [1983: 25], is ‘attracting, maintaining, and ... enhancing customerrelationships’. We note that classifying services based on whether they arefrequently or infrequently purchased is not the same as classifying thembased on whether or not they are amenable to relationship marketing, norare the strategies that arise from such classifications the same. Themarketing activities necessary to establish and build customer relationshipsmay include characterising repeat user groups, and directing the marketingmix toward these groups. For example, a restaurant offers a free lunchdrawing to current customers who drop their business cards into a fishbowl,a bank provides additional services to customers who maintain largerbalances, and airlines award a free ticket to passengers building up enough‘frequent flyer’ miles. However, not all services, and not all customers, aresuited to an ongoing marketing relationship. For example, Lovelock [1983:14] notes that requiring membership to obtain a marketer’s offers may‘result in freezing out a large volume of desirable casual business’.

Morgan and Hunt [1994] point out that there is a difference between thediscrete transaction, which has a distinct beginning, short duration, andsharp ending, and the relational exchange, which is longer in duration andreflects an ongoing process. Where does the transition from a transaction toa relationship occur? Dwyer, Schurr, and Oh [1987, p.12] suggest thisoccurs ‘when the buyer pays by check or the seller schedules delivery fornext week. That is, when dependence is prolonged, performance is lessobvious, uncertainty leads to deeper communication, the rudiments ofcooperative planning and anticipation of conflict arise, and expectations oftrustworthiness may be cued by personal characteristics’.

Berry [1995: 237] identifies services with ‘relationship appeal’ as thosewhich are personally important, variable in quality, complex or high-involvement. These attributes have a key element in common – they eachlead to more effortful information processing on the part of the customer.Other researchers [Lovelock, 1983; Palmer and Cole, 1995; Zeithaml andBitner, 1996] recognise specific characteristics of services consistent withrelationship marketing. These characteristics include allowingcustomisation, utilising provider judgement, directing the service toward aperson (as opposed to a possession), requiring the customer to be physicallyand/or mentally present, involving a multistage production process,requiring monitoring, involving service contracts, operating in turbulent orhighly competitive markets, or facilitating customer feedback. Thus,


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transactions are more consistent with purchases that require lessinformation processing, while more effortful, important decisions tend to bemade in the context of a relationship.

Classifying services based on relationship appeal offers some guidanceas to optimal marketing strategy, and further insight may be obtained byconsidering the customer’s frequency of need for the service. Services thatare amenable to relationship marketing may still differ in need frequency,and thus, strategies should be adjusted accordingly. Two example serviceshaving relationship appeal and yet demonstrating substantially differentpatterns of customer purchase are skydiving and white water rafting. Celsi,Rose, and Leigh [1993] describe skydiving as a high involvement service;purchase motives include self-efficacy, new self-identity, group camaraderie(community), and heightened experience. Thus, skydiving is both complexand personally important; further, the nature of the service encouragesinterpersonal interaction and communication. Arnould and Price [1993]explore white-water rafting, which is similar to skydiving in several keyways. Purchase motives include a desire to leave the familiar behind and dosomething completely different, and interpersonal influence plays a role inthe buying decision. For both skydiving and rafting, the new customer isunsure what to expect in the activity, and both skydivers and rafters seem tobe escaping, in a sense, from jobs and other encumbrances of society. Insummary, both skydiving and white-water rafting have relationship appeal.However, the frequency of usage differs considerably between them –skydiving services are frequently purchased (typically once a week) whilewhite-water rafting is infrequently purchased (possibly only once in alifetime). This difference in need frequency leads to differences in optimalmarketing strategy.

We can also observe differences in optimal marketing strategy forservices that are typically purchased as transactions. For example, neither afilling station nor car-towing service is very amenable to relationshipmarketing strategies. However, the difference in frequency of customer needfor these two services suggests differences in their marketing strategies,particularly with regard to customer awareness and risk reduction.Awareness of a filling station is often obtained while driving by, but cartowing must be sought out when it is needed. Risk reduction is less of anissue when buying gasoline than when in need of towing service – typicallythe presence of a brand name provides sufficient customer confidence for agasoline purchase. (Note that car towing is often marketed in a package withan automobile club membership. This is not the same as marketing cartowing as a relationship service. The automobile club involves a membershiprelationship, and serves a professional advisory role in selection of a cartowing service. We will return to this topic in the next section.)


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The Need for Awareness and Risk Reduction

One key point that appears repeatedly in the articles described above is thatpotential customers of infrequently required services have great need forinformation and risk reduction. In this section, we discuss these two keyissues at greater length, addressing in particular: (1) customer awarenessmay be obtained through alternate paths when more familiar informationsources are unavailable, and (2) customer risk reduction is more importantwhen available services are less familiar. These relate to the examplementioned earlier, regarding automobile clubs as sources of information forcustomers who need to purchase towing service. Such customers may notbe either aware of or familiar with available services.

How do potential customers become aware of available services to meetan infrequent need? The manufactured goods literature indicates that theawareness process may be very different for durable products than it is forfrequently purchased products. Few articles have appeared in the literature,thus far, to help us discover how potential customers become aware ofinfrequently purchased service offerings, or how potential customers decidewhich service to purchase when the need arises. (Key exceptions includeFreiden and Goldsmith [1988, 1989]) Here, we briefly review the types ofservices that tend to be infrequently required, discuss how potentialcustomers obtain information to assist in making choices, and compareinfrequently purchased services with durable products.

The need for an infrequently purchased service may be defined by a life-changing event; thus, examples include funeral homes, divorce attorneys,alternative birthing centre and cruise lines. Because of the magnitude of theevent, the customer views the service choice as an important one. Thedecision may also be stressful because key product attributes may be muchmore difficult for a potential customer to identify and evaluate, because theservice is purchased infrequently. As noted earlier, the choice may be seenas risky, both because the situation is unfamiliar and because the servicecannot be experienced prior to making a purchase decision.

How might marketers of infrequently purchased services increaseawareness and likelihood of purchase among the potential market? How canthese marketers even identify who is in their target market, a constantlychanging group of new potential customers experiencing an infrequentneed? Although most such service providers advertise, at least in the yellowpages if not on the World Wide Web, it is unlikely that the target market willfind them through advertisements at their time of need. Rather, they aremore likely to utilise some form of referral [Freiden and Goldsmith, 1989].

Diffusion models, which forecast cumulative adoption of durablemanufactured goods, focus on first adoptions by new purchasers. Thus,


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marketing mix activities such as advertising, promotion, and pricing areoptimised to reach such customers. Further, word-of-mouth extending fromadopters to the remaining potential market is a key driver of new adoptionsin a diffusion model. Marketing mix activities and word-of-mouth probablyalso influence purchase of infrequently required services. However, becauseof the intangibility of services, and the interpersonal interaction that definesmany service production processes, marketing mix activities may not beable to adequately reduce the risk faced by potential buyers of infrequentlyrequired services. Further, because of the inexperienced target market andthe confidentiality associated with many services, word-of-mouthinformation may not be as readily available as it is for durable manufacturedgoods. How, then, might the potential customer gather enough informationto make a decision regarding an infrequently purchased service?

One key source of information regarding infrequently purchasedservices is an intermediary. This go-between is likely to be a serviceprovider in a closely related field. For example, just as you ask aprofessional advisor which home furnishings to buy or which medicine totake, you might consult an expert before selecting an infrequently purchasedservice. Lovelock [1983: 19] defines ‘specialist intermediaries’ thatrepresent a number of different service organisations. He states ‘consumerssometimes perceive such intermediaries as more objective and moreknowledgeable about alternatives than the various service suppliers theyrepresent. The risk to the service firm of working through specialistintermediaries is, of course, that they may recommend use of a competitor’sproduct!’ Bearing Lovelock’s caveat in mind, a service provider who wishesto make use of an intermediary must ensure that a good relationship with thego-between is built and maintained. A number of suppliers of infrequentlypurchased services are already making use of intermediaries, although thisstrategy may not be clearly defined as such. For example, wedding co-ordinators and funeral directors work with clergy representatives who areaware when a potential client enters the target market.

For services that are infrequently purchased, reduction of customer riskis crucial. This is partially because, when purchasing such a service, thecustomer often has no prior experience with the category. Appropriatetangibles are search attributes that give the potential customer reason tobelieve the provider is capable of meeting his or her service needs.Experience attributes associated with the service process can best bedirectly observed after a decision has been made to purchase. However, theservice provider may also reduce the potential customer’s risk by providing,for example, an initial meeting free-of-charge. For instance, a divorceattorney might offer a 15-minute consultation prior to commitment.Customers assess whether experience attributes meet their expectations


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after the service is provided. Credence attributes are the most difficult forcustomers to evaluate, because even after the service is complete, thecustomer cannot directly observe presence or absence of expectedperformance. For these attributes, it may be possible to reduce the potentialcustomer’s perceived risk by providing a warranty, such as a money-backguarantee, in case the customer is completely dissatisfied.

In summary, it is crucial for providers of infrequently purchased servicesto assist potential customers in developing awareness and in reducingpurchase risk. To locate potential customers who are in a time of need,service providers may wish to utilise specialist intermediaries who will offerthem referrals. Potential customer risk may be reduced through tangibles,testimonials, consultations, and/or guarantees. In the next section, wedevelop a model for an optimal promotion policy for an infrequentlypurchased service. This policy will reflect the need for early developmentof awareness and risk reduction for an innovative service category,reflecting the experience of an example firm.

PROMOTION STRATEGY FOR AN INFREQUENTLY PURCHASED

SERVICE

Because we develop a promotion policy for an infrequently requiredservice, customers must be drawn from a new potential market each period– any given person is in the potential market only during a time of need. Weconsider the example of a home inspection firm marketing its services tobuyers of high-end homes. Note that the customer is in the potential marketonly until the need passes (i.e. until the home purchase is complete).

To establish our recommended promotional expenditure policy, wedevelop a model for changes in potential customer awareness and theresulting purchase decisions, taking into account the impact of promotionalactivities. We also indirectly consider the influence of word-of-mouth – toinitialise awareness in each period, we assume each buyer has told onepotential buyer. In addition to deterministic diffusion-based models byHorsky and Simon [1983] and Teng and Thompson [1983], other models forthe dependence of durable good sales on promotional activities include thestochastic models of Monahan [1984] and Weerahandi and Dalal [1992].Monahan develops a pure-birth model for optimal advertising, whileWeerahandi and Dalal incorporate a birth-and-death process allowingpotential customers to exit the target market.

Our work differs from previous research in that we develop a closed-loop, or ‘feedback’, policy for optimal promotional expenditures,expressing them in terms of the previous period’s sales. This allows amanager to react to changing marketplace conditions by modifying


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promotional plans on the basis of recent information. We begin byspecifying a stochastic model for awareness and the resulting sales,incorporating uncertain market response. We then use stochastic optimalcontrol to solve for the optimal promotional expenditure policy. Forcomparison, we also derive a baseline or ‘naïve’ sales model in whichpromotional expenditures are not considered. Further, we demonstrate ourmodel empirically using a relatively new technique for estimating stochasticmodel parameters. Our demonstration uses data from the home inspectioncompany, and we test both our complete model (considering the impact ofpromotional policy) and the naïve model. We compare model fits to actualdata, calculate optimal promotional expenditures, and use the results inproviding managerial recommendations.

In the marketplace we consider, one firm acts as a monopolisticcompetitor; this firm may be either a market leader with no closecompetition or a ‘niche’ player that is clearly a leader in its chosen segmentof the market. Thus, the firm’s promotional activities are directed primarilytoward this specific target market, making retaliation by competitors of littleconcern. A second assumption, mentioned earlier, is that there exists a newpotential market in each period. In other words, the marketplace we study isone in which each potential customer is in the market for only a limitedtime, and consequently, makes a decision to either purchase or not in thecurrent period. Repeat sales and/or return customers occur at a negligiblerate and consequently are not an issue; this is typical of an infrequentlypurchased service.

Consider the flow diagram shown in Figure 1. Each period, a potentialmarket of size N might purchase the service – at the beginning of a period(time t) the portion of these who are aware is initialised to the number ofbuyers in the previous period, xt–1. As the period progresses, unawarepotential customers may become aware, then aware potential customerseither fail to purchase, or purchase by the end of the period. At thebeginning of the next period (time t+1) there are again N potentialcustomers, and xt are assumed to be initially aware. Thus, the processcontinues indefinitely.


UnawareN-xt-1

Awarext-1

Buyers

Non-Buyers

FIGURE 1FLOW DIAGRAM WITH SITUATION AT TIME t

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Based on the above-described framework, we develop a stochastic birth-and-death model (see, for example, Çinlar [1975]), in which a birthrepresents a unit increase in the number of aware potential customers, whilea death represents the failure of an aware potential customer to purchase(i.e. to leave the aware potential market without purchasing). DefiningP(x,t) to be the probability of x aware potential customers at time t, wepropose the following three axioms for increase and decrease in the numberof aware potential customers:

A1. The probability of two or more changes (increases or decreases) inthe number of aware potential customers during the time interval [t,t+∆t]approaches zero as t approaches zero.

A2. The probability of a unit increase in the number of aware potentialcustomers during the time interval [t,t+∆t] is proportional to the remainingpotential market and to the level of promotional expenditures at time t.

where N is the total potential market in each period, N–x is the number ofunaware potential customers, and a(t) is the advertising and promotionexpenditure in the period beginning at time t.

A3. The probability that an aware potential customer decides not topurchase during the time interval [t,t+∆t] is proportional to the numberof aware potential customers at time t.

Using these axioms, we can define the probability that there are x awarepotential customers at time t+∆t in terms of the three scenarios by which thevalue x may occur. First, there may be x+1 aware potential customers at timet and a death may occur during the interval ∆t. Second, there may be x awarepotential customers and neither a birth nor a death may occur during theinterval ∆t. Finally, there may be x–1 aware potential customers at time tand a birth may occur during the interval ∆t. Summing across these threepossible scenarios, we obtain

)2(][)1( t.xM=tx,|t+t,xP ∆∆−

)1()]Ä([)Ä1( ttx,aN=Qx,tt|,t+x+P −


.)](,1[),1(

)3(])](,[1][][1)[,(

]1[),1(),(

ttaxNQtxP

ttaxNQtxMtxP

txMtxPttxP

∆+−−+∆−−∆−+

∆++=∆+

P(x + 1, t + ∆t | x,t) = Q[N – x,a(t)]∆t (1)

P(x – 1, t + ∆t| x,t) = M[x]∆t . (2)

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We need to specify functional forms for promotional activityeffectiveness (Q) and for the decision of aware potential customers to notpurchase before their time of need is over (M), to completely define adifferential difference equation representing changes in the number of awarepotential customers. Increases in awareness are driven by available potentialmarket and by promotional expenditures. To reflect decreasing returns-to-scale, we use the square root of promotional expenditures. We also include aconstant term in our model to capture increases in awareness not due topromotional expenditures. While we cannot model these influences morespecifically, they may include effects of the environment (e.g. changes in theeconomy or legal system), changes in distribution or availability, or effectsof uncontrolled information sources (e.g. news broadcasts, public serviceannouncements, or word-of-mouth). Therefore, we define

where qn is a constant growth term and qa is a coefficient of promotionalexpenditure effectiveness. We model failure to purchase as a linear functionof the number of aware potential customers. Therefore

where m is a coefficient of failure to purchase. Note that m is notconstrained from above; if a very small percentage of aware potentialcustomers do purchase, m may be quite large.

Substituting equations (4) and (5) into (3), and taking the limit as ∆tapproaches zero, we obtain the following differential difference equationdescribing the evolution over time of the probability that x potentialcustomers are aware of the service:

To develop our baseline (naïve) model, we substitute for Axiom 2 analternative axiom in which the impact of promotional expenditures onpotential customer awareness (and resulting sales) is not considered:

),1()1)()((

)6(),()])()(

),1()1(),(

txPxNtaqq

txPxNtaqqmx

txPxmdt

txdP

an

an

−+−++

−++−

++=

)5(][ mxxM =

)4()]()([)](,[ xNtaqqtaxNQ an −+=−


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Alternate A2. The probability of a unit increase in the number of awarepotential customers during the time interval [t,t+∆t] is proportional tothe remaining potential market at time t.

Using Alternate A2, we define for our naïve model

This model does not reflect the impact of increases in promotionalexpenditures on awareness.

Substituting equations (7) and (8) into (3), and taking the limit as ∆tapproaches zero, we obtain the following differential difference equation forour naïve model:

Based on the stochastic differential equation representation of equation(6), we derive the optimal promotional expenditure policy for our completemodel, using standard techniques of stochastic optimal control. Thisapproach has proven useful in developing optimal decisions for dynamicmarketing problems with uncertainty. Raman and Chatterjee [1995] usedthis technique to derive optimal pricing policies for dynamic markets.Raman [1990] analysed the ratio-rule method of determining the advertisingbudget in a similar framework. Our results suggest an optimal promotionalexpenditure policy, in a closed-loop or feedback form, which specifies theoptimal promotional expenditure for the current period in terms of theprevious period’s sales. This optimal policy is

).,1()1(

)9(),()]([

),1()1(),(

txPxNq

txPxNqmx

txPxmdt

txdP

n

n

−+−+−+−

++=

(8)x).-(Nq=x]-Q[N n

)7(.][)|,1( txNQx,tttXP ∆−=∆++


]2

2)1(

)()(

[)()( 2

22

2

a

ann

Nq

Nqm

prmqrmq

xNxa+

++++++−−=

(10)

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Thus, a manager can use a parameterised version of equation (10) tomonitor the market and adjust promotional plans in response to currentconditions. Our results are also useful to the manager who wishes to predicthow changes in the various parameter values would be expected to impactoptimal policy for a service characterised by our model. To show this, wemake use of first-order conditions, which provide the followingunambiguous results: if the size of the potential market, N, were to increase,optimal promotional expenditures would increase as well, a result consistentwith intuition. Similarly, if the profit contribution per unit sale, p, were toincrease, optimal spending would increase. This makes sense because, if thesame sales were maintained, additional revenues would be available tocover the cost of additional promotion. Also, increased promotion may benecessary to retain the current level of sales if the price were increased toobtain the greater profit contribution.

We find increases in the coefficient of promotional expenditureeffectiveness, qa, to be associated with increases in the optimal level ofpromotion; it makes sense that an increase in the productivity of advertisingexpenditures leads to an increase in the optimal level of advertising. Incontrast, increases in qn, which would suggest increases in awareness due tosources other than advertising and promotional expenditures, lead todecreases in the optimal level of advertising spending. Further, increases inm (the coefficient which estimates the tendency of aware potentialcustomers to fail to purchase) lead to decreases in optimal promotionalspending. Therefore, if advertising expenditures result in fewer adoptions,their lower productivity is reflected in recommendations to use lessadvertising.

Finally, we observe that our optimal promotional policy is robust withrespect to uncertainty. Provided that the decision maker is risk neutral, theerror variance does not impact the optimal expenditure. From a practicalstandpoint, this means that the decision maker may obtain optimal profits byfollowing our recommended policy in environments of high as well as lowuncertainty.

In the next section, we compare sales estimates obtained for our examplefirm under assumptions of our complete model, which incorporates theimpact of promotional expenditures, against those obtained under our naïvemodel assumptions. We also determine optimal promotional expenditures,using our feedback model, for this example firm.

EMPIRICAL RESULTS FOR THE HOME INSPECTION FIRM

We use data from a home inspection firm to empirically test theapplicability of both our complete model (incorporating promotional


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expenditures) and our naïve model. The firm operates as a monopolisticcompetitor: it is positioned as the high-end alternative, at a price more than50 per cent above its nearest competitor. A target market of approximately5,000 homebuyers, who are potential new customers, is available annually.Each of these potential customers is typically ‘in the market’ for less than ayear. If the potential customer is aware of the availability of the homeinspection service at the time of purchasing a home, a decision is made toeither purchase an inspection, or not, during this short time frame. We have12 years of annual sales data (in units) and promotional expenditures (in USdollars), so we use the first year as an initial condition and estimate ourmodel parameters for subsequent years. To correct for annual differences inthe value of the US dollar, we adjust the promotional expenditures by theconsumer price index (CPI).

We obtain parameter values for both our complete and naïve modelsusing a simulation-based technique (SIMEST) introduced by Thompson,Atkinson and Brown [1987] in the field of cancer modelling. Using theirtechnique, stochastic model parameters are estimated directly from theaxioms defining the process, so that it is not necessary to obtain a closedform solution to a stochastic differential equation. This technique has beendemonstrated in a managerial application, modelling high-tech marketplaceentries and exits [Bridges, Ensor and Thompson, 1992].

In the present implementation of the SIMEST method, we make use ofaxioms A2 and A3 to obtain probability functions for times of increases anddecreases in the number of aware potential customers. The time until thenext increase or decrease is then simulated based on these probabilityfunctions. Making use of the means of 5,000 such simulations at differentparameter values, the best parameter values are determined by minimisingthe sum of squared error between first differences in actual salesobservations and the comparative differences between model-based one-step-ahead forecasts and the previous actual observations.

For our complete model, which incorporates the influence ofpromotional expenditures on potential customer awareness, we obtain theparameter values (and standard errors) qn=0.008831 (0.00426), qa=0.05947(0.01863) and m=5.259 (0.2261). The relatively large value obtained for m,indicating ‘failure to purchase’, suggests that a substantial percentage ofaware potential customers do not purchase. Using the above values toestimate one-step-ahead sales, given actual promotional expenditures,results in the estimated sales path, which is shown in comparison to actualsales in Figure 2. (This estimated path was obtained by simulating 1,000realisations of the path at the estimated parameter values, then taking themean of these 1,000 estimates.) Comparing the sum of squared error to thetotal sum of squares (the sum of squared first differences in our sales data),


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we find that our complete model is able to explain 44.0 per cent of thevariation in the data.

Parameter estimates obtained for our naïve model are qn=0.26735(0.02611) and m=2.10097 (0.3012). As in the complete model, our ‘failureto purchase’ parameter value suggests that a substantial percentage of awarepotential customers do not purchase. Our estimate for qn is larger than thatin our first model, because the constant now takes up a portion of thevariance explained by promotional expenditures in the complete model.Using the naïve model parameter values to estimate one-step-ahead salesresults in the sales path, which is shown in comparison to both actual salesand our complete model results in Figure 2. (This path was obtained as themean of 1,000 simulated realisations at the estimated parameter values.)Comparing the sum of squared error to the total sum of squares, we find thatour naïve model is able to explain 28.3 per cent of the variation in the data.Thus, by considering promotional expenditures in our estimation of sales,we are able to explain an additional 15.7 per cent of the variance in the data.Further, we observe a 55.4 per cent improvement in fit of the completemodel as compared to the naïve model. While there is still considerableunexplained variation in the sales data, including promotional expendituresclearly contributes to the fit of the model.


0 5 10 15Year

0

100

200

300

400

500

600

700

Uni

t Sal

es

NaiveCompleteActual

FIGURE 2COMPLETE AND NA ÏVE MODEL ESTIMATES COMPARED TO ACTUAL SALES

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How do actual promotional expenditures compare with thoserecommended by our optimal policy? To answer this question, we obtainoptimal promotional expenditures based on equation (10) and compare theoptimal and actual advertising expenditures graphically in Figure 3. As seenin the Figure, our model would have recommended heavier advertisingexpenditures during the startup phase of the business, followed byreductions in advertising expenditures at later times. This would have led togreater sales volume in the early years as well as savings on advertisingspending in the later years.

DISCUSSION

As in manufactured goods markets, frequency of customer need mayinfluence optimal service marketing strategy decisions – thus, it is importantto examine how strategic recommendations depend on the frequency ofneed. We note that, for durable manufactured goods, the strategic focustends to be on building customer awareness and reducing risk, and weanticipate that similar motivations exist for infrequently purchased services.Therefore, we build a promotional strategy model for an infrequentlypurchased service, using as an example a home inspection firm. Our model


0 5 10 15Year

0

5000

10000

15000

Adv

ertis

ing

Exp

endi

ture

s in

Con

stan

t Dol

lars

OptimalActual

FIGURE 3ACTUAL AND OPTIMAL ANNUAL PROMOTIONAL EXPENDITURES

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attempts to portray the effect of promotional activities on sales in aninfrequently required service category and to use the results in deriving anoptimal promotional policy. This policy is obtained in a feedback form,which means that managerial recommendations are updated each periodbased on current performance. In designing the model, we view changes insales from one period to the next as increasing with awareness, anddecreasing when aware potential customers fail to purchase before theirtime of need is past. We recognise that influences we have not accounted formay also impact sales, and consequently, our model is necessarily anapproximation. We partially compensate for this limitation by incorporatinguncertainty in potential customer response into our model, and interestingly,we find that the optimal promotional policy does not depend on marketplacevariability.

In addition to deriving a policy for optimal promotional expenditures,we obtain estimates of sales, both for our complete model (which considersthe impact of promotional expenditures) and for a naïve model (whichneglects promotional expenditures). We demonstrate substantialimprovements in model fit when promotional expenditures are considered.Finally, we derive normative results that suggest modifications in promotionplans under certain conditions. Specifically, optimal promotionalexpenditures decline as sales increase; thus, a firm that is in a growth marketshould initially select a high level of promotion and, as sales grow, allowexpenditures to decline. Further, promotional expenditures should increaseif their effectiveness increases, if the potential market expands, or if theprofit contribution of a sale increases. We note that the resultingrecommended pattern of promotional expenditures makes sense for aninfrequently purchased product, in that it starts high and declines slowlyover time, as product awareness increases and word-of-mouth works todecrease customer risk. This is consistent with the recommended pattern ofpromotional expenditures for a durable good [Horsky and Simon, 1983].Thus, it is not unreasonable to conclude that promotional objectives(building customer awareness and reducing risk) are consistent across bothinfrequently purchased services and durable goods.

CONCLUSIONS

Our model makes recommendations regarding promotional expenditures foran infrequently required service, depending on the observed impact ofpromotion on sales over time. Specifically, our results suggest that, becausethe primary goals of promotional spending for an infrequently purchasedservice are building customer awareness and reducing risk, promotionalexpenditures should be high initially and decline over time as sales increase.


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But, for the marketer actually managing these promotional activities, howcan potential customers for an infrequently required service be identifiedand reached during their time of need? Although most such serviceproviders advertise, at least in the yellow pages if not on the Web, it isunlikely that members of the target market will find a service providerthrough advertisements during their time of need [Freiden and Goldsmith,1989]. Rather, customers are more likely to utilise some form of referral.Often, the assumption is made that referral is through word-of-mouthextending from buyers to the remaining potential market. However, owingto the intangibility of services, and the interpersonal interaction that definesmany service production processes, promotional activities (directed at thetarget market) and word-of-mouth may not be able to adequately reduce therisk faced by purchasers of infrequently required services. Further, becauseof the dynamic nature of the potential market and the confidentialityassociated with many professional services, word-of-mouth informationmay not be as readily available as it is for durable manufactured goods.

Many firms supplying infrequently purchased services direct a largeportion of their promotional activities toward professionals in relatedindustries who might offer a recommendation to a potential customer at acritical moment. For instance, the home inspection firm that participated inthis study was actively involved with real estate agents that would providereferrals to potential homebuyers. We note that experts might also beconsulted for frequently purchased services such as air travel or stocktrading, but the nature of the expertise needed is different; it is more relatedto the fact that in such dynamic categories there are rapid changes inavailable alternatives and prices. Thus, an intermediary, a specialist in aclosely related service industry, might be very helpful in identifying currentpotential customers for an infrequently required service. Promotionalactivities directed at appropriate intermediaries may be useful in generatingreferrals that will reach the target market during their time of need. Post-purchase reinforcement directed at buyers may also reinforce theintermediary’s recommendation and generate word-of-mouth.

We observe that strategic goals and tactics are quite different forinfrequently and frequently purchased products, regardless of whether theyare goods or services. For instance, promotional strategy models forfrequently purchased packaged goods consistently recommend a ‘pulsed’pattern of promotional spending [Abraham and Lodish, 1993; Silva-Russo,Bucklin and Morrison, 1999]. This type of spending makes sense in amarketplace motivated by the need to build market share, whether throughincreased purchasing by loyal customers or through customer switchingfrom competitive products. Frequently purchased services are alsocharacterised by the need to build market share in this manner. We have


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shown that promotional objectives for infrequently purchased goods andservices are different than those for frequently purchased products, and weobserve that objectives for other elements of the marketing mix may alsodiffer depending on the frequency of customer need.

The present work opens a number of possibilities for future research.Perhaps the most urgent need is the development of models to assist inunderstanding and decision making for other marketing mix variables forinfrequently purchased services; such models may draw on those designedfor durable goods. In addition, we would like to consider services for whicha purchase decision is made infrequently, but for which a series of servicesare purchased – examples include orthodontic work, physical therapy, andweight loss programs. For these types of services, it may be possible toreduce customer risk by offering a warranty allowing customers to exit atcertain points in the series, obtaining a refund of any unused payments. Inany case, it is clearly worthwhile to continue investigating the impact ofservice purchase frequency on best marketing strategy decisions.

ACKNOWLEDGEMENTS

The authors wish to express their appreciation to Bob Reeds for providing data and insight. Theyalso wish to thank Frank Bass, Amir Barnea, Rod Brodie, Susan Ellis, Renee Florsheim, DipakJain, Murali Mantrala, Chris Miller, Eitan Muller, Scott Neslin, Ram Rao, and Chi Kin (Bennett)Yim for helpful suggestions. The second author wishes to acknowledge computer hardwareprovided by NSF grant No.DMS 9005783.

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the impact of need frequency on service marketing strategy

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