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Expert systems in marketing: An application for pricing new products
Expert Systems With Applications, Vol. 7, No. 4, pp. 545-552, 1994 Copyright © 1994 Elsevier Science Ltd Printed in the USA. All fights reserved 0957-4174/94 $6.00 + .00
Pergamon
Expert Systems in Marketing: An Application for Pricing New Products CATHAL CASEY AND CIARAN MURPHY Department of Accounting,Financeand InformationSystems,UniversityCollegeCork,Cork, Ireland
Abstract-- The use of expert systems in support of the marketing function is a relatively new field within expert systems. This paper reports on the issues arising in the development of an expert system for a key area of marketing--that of new product pricing. The paper describes the approach followed in the acquisition, modelling, and encoding of marketing expertise. The system developed is described, and sample consultations are outlined. An important aspect of the system detailed in this study is that it has been validated by marketing personnel in a range of businesses and has been used by a number of companies to support them in their new product pricing.
1. INTRODUCTION
are better understood than problems involving customers and markets (Feigenbaum, McCorduck, & Ni, 1988). The possible gains, risks, and costs, along with the effect on corporate performance, can be more easily measured for systems concerned with internal problems. It is likely therefore that the external focus of marketing problems has inhibited the application of expert systems in the area. This represents a significant oversight on behalf of the business community, given that the information technology applications that are potentially most rewarding are those in strategic areas of business such as marketing (Cashmore & Lyall, 1991). It is not surprising therefore that where expert systems have been applied in marketing (albeit in relatively isolated cases), the applications have been considered successful and useful (Bernstein, 1989; McDonald, 1989; Mockler, 1989; Mockler & Dologite, 1992). This paper reports on a study that was concerned with the development of an expert system in an important area of marketing--that of new product pricing. Issues involved in the acquisition, modelling, and programming of marketing expertise are outlined, followed by descriptions of the system structure and operation. The paper concludes with a report on its evaluation by business managers.
THE DEVELOPMENTof expert systems for use in business management has grown significantly in the past decade (Biswas, Oliff, & Sen, 1988; Blanning, 1987; Liebowitz, 1990; Mockler, 1989; Mockler & Dologite, 1992; Schutzer, 1990; Sen & Biswas, 1985; Walden, 1992; Westland, 1992). Although most of these management applications are concentrated in the production and finance functions of business, a number of researchers have proposed expert system applications for marketing tasks, e.g. areas of advertising, brand management, new product introduction, and product pricing (Blanning, 1987; MacDonald, 1989; Walden, 1992). Despite these proposals little progress has been made in applying expert systems in the area of marketing. A conventional explanation put forward for the lack of marketing expert systems is that marketing tasks differ in nature from tasks where expert systems have traditionally been applied. Marketing problems do not lend themselves to the same precise logic as, for example, scientific problems (McDonald, 1989). Marketing managers deal with uncertainties and vague concepts and make judgements using criteria and rules that are difficult to define. Their knowledge does not fit neatly into underlying models, and therefore expert system solutions have not been considered suitable for marketing problems. Moreover, companies prefer to try out new technologies, such as expert systems, on problems that involve internal operations, because they
2. NEW PRODUCT PRICING Like most marketing tasks, the pricing of new products is an exercise characterised by few, if any, clearly defined or generally accepted principles. The literature on pricing is rife with criticism of the practices adopted by pricing practitioners in companies (Gabor, 1985; Keating, 1991; Nagle, 1983; Winkler, 1983). Modem
Requestsfor reprintsshouldbe sentto CathalCasey,Departmentof Accounting, Financeand InformationSystems,UniversityCollege Cork, Cork, Ireland. 545
546 marketing principles, it seems, have widely influenced all elements of marketing strategy except pricing (Nagle, 1983). Gabor, for example, described the pricing of new products, particularly those that are technologically innovative, as "pricing in a vacuum," because the company can have only scant knowledge and experience of the product and market (Gabor, 1985). Dean remarked that in practice product pricing was "a top management puzzle too often solved by cost-theology and hunch" (Dean, 1960). The most frequent criticism of pricing practices is that the task of setting price is too often based on product cost information, at the expense of more relevant information relating to the market, competitors' prices, and company objectives. Too often, simple pricing mechanisms such as cost-plus formulae are used to determine product price (Winkler, 1983), with inadequate research and consideration going into the pricing process. Atkin and Skinner (1975) for example, estimated that up to 80% of companies used cost-related pricing techniques when pricing products. This overreliance on cost-based information is attributable to the fact that such information is easily obtained within the company. In contrast, information relating to market demand and competing products is exogenous to the company and is more difficult to collect, interpret, and build into the decision process. However, cost information should never replace market information in the pricing of products, because cost information gives no insight into market demand. Baxter and Oxenfeldt (196 l) advise that even crude estimates of market demand serve better than cost information in pricing products. Many researchers are of the opinion that although product pricing decisions, and marketing decisions in general, require creative thinking (in that there are no simple rules or mechanisms to apply when making these decisions), there is a body of expertise in the area which is currently being ignored, or at best being misapplied, by pricing practitioners in most companies (Keating, 1991; Nagle, 1983; Oxenfeldt, 1960; Winkler, 1983). Assuming that this body of expertise could be collected and represented in a knowledge base, a welldesigned expert system would be capable of disseminating it to those involved in pricing new products. At present this expertise is not readily available to these people, because it is contained in a wide variety of sources such as books, journals, and in many cases in the minds of experienced pricing practitioners. This role for an expert system in marketing is along the lines proposed by McDonald (1989) and Walden (1992): ...in general, marketing decisions are taken without sufficient analysis and understanding of the relevant issues. The reason for this is a lack of knowledge and understanding of how and why the multifarious factors of marketing interact and serve to form the parameters of any business activity. In this real
C. Casey and C. Murphy life situation we see emerging the perfect role for an expert system in marketing planning. (McDonald, 1989) The approach taken in this study was, first, to investigate if an expert system was a feasible option in addressing the task of pricing new products. This preliminary investigation was undertaken in the realisation that expert systems are not suitable solutions for all problems and that many expert system applications have resulted in failure. In fact, the c o m m o n misconception of expert systems as a panacea for all problems has contributed to the failure of many applications. This preliminary investigation was designed to avoid rash development. If, on the basis of this investigation, an expert system was considered appropriate for new product pricing, the remainder of the study was to be devoted to the development and evaluation of such a system. If it was found that an expert system was not feasible for the task, the proposal would be rejected prior to any development effort taking place. 3. I N V E S T I G A T I N G T H E FEASIBILITY OF AN E X P E R T S Y S T E M FOR N E W P R O D U C T PRICING The most commonly prescribed approaches for analysing if expert systems are suitable for addressing problems feature checklists of desirable attributes or characteristics (Murdock 1990). These checklists vary in length and in procedural rigour (Hayes-Roth, 1984; Prerau, 1985; Slagle & Wick, 1988; Walters, 1987), but all operate on the basic principle of examining the characteristics of problem solving in a particular area to check for the presence of attributes that indicate the suitability of an expert system application. Characteristics such as the following are often identified: • The problem should be a high value, nontrivial one. • There should be recognized experts who solve the problem, and these people should be capable of communicating their knowledge. • Heuristic knowledge (sometimes called I F - T H E N rules of thumb or general cause and effect relationships), gained through experience of the task, should form the basis for problem solving in the area. • Conventional programming/algorithmic solutions to the problem should be unsatisfactory. • It might be necessary to solve the problem using incomplete and uncertain information. • The problem should be sufficiently narrow and selfcontained that the number of important concepts (such as factors to account for and rules to apply) should be bounded to several hundreds. Based on an analysis of the area of new product pricing using such prescribed criteria, the researchers were satisfied that the task was one for which an expert system could be developed. The task was certainly nontrivial, in that the consequences of incorrectly
Expert Systems in Marketing pricing a new product were significant (Gabor, 1985; Winkler, 1983); there were identifiable sources of expertise to which the researchers could relate (in fact, several research studies had been devoted to the task); the task required heuristic knowledge (general rules of thumb) for its solution, and often required prices to be set on the basis of incomplete and uncertain information (Gabor, 1985). Conventional algorithmic approaches to new product pricing (e.g., cost-based pricing formulae) were not satisfactory (Oxenfeldt, 1960), and from the beginning the problem appeared to be sufficiently narrow and self-contained that the number of important concepts would never exceed several hundred. Furthermore, the task was characterised by the misapplication (or nonapplication) of a body of expertise and this feature strongly indicated that an expert system solution might be viable (Daly, 1990). Assuming that the relevant expertise could be captured, it was likely that this system would prove to be a useful support tool in an area where decision makers were overreliant on inappropriate and simplistic techniques for important organisational decisions. As a preliminary step, discussions were undertaken with three parties--a pricing practitioner, an academic from the discipline of marketing, and an expert system developer--to elicit their views on the proposed expert system application. Positive responses were received from the academic and from the expert system developer. The pricing practitioner did not fully understand the concept of an expert system, but did indicate that a computerised software tool incorporating new product pricing expertise would be useful. On the basis of these preliminary findings, it was decided to proceed with the development of an expert system for new product pricing and to subsequently evaluate its usefulness.
4. DEVELOPMENT OF THE EXPERT SYSTEM 4.1. Knowledge Acquisition Unlike conventional system developers, developers of expert systems do not simply analyze the problem, get the user to sign off, and then proceed with development. Rather, they work with experts throughout the entire development process, constantly acquiring new knowledge and integrating it into an evolving system. For this reason the development process is more akin to a prototyping or iterative approach to system development, with high levels of interaction between the expert and knowledge engineer. As an initial step in the development of the system, practitioners involved in pricing were interviewed in an attempt to establish how new product pricing decisions were made. It became clear, however, that these
547 experts were unable to articulate their decision making process in sufficient detail. First, the practitioners did not describe their knowledge in a well-structured form that suited the idea of an underlying decision model. Secondly, their knowledge consisted largely of rules of thumb, which were private and difficult to recall and articulate in an interview. Finally, practitioners did not consider their methods of pricing to constitute expertise and were inhibited by this perceived inadequacy when questioned about their decision making. They felt that their approaches to the problem were not methodical enough, could be improved, and were therefore slow to reveal their latent expertise. From these interviews, it became clear that there was a communication problem between the researchers (as knowledge engineers in this context) and the pricing practitioners (as experts). It appeared that the questions asked of experts were normally too vague or general to elicit useful responses. However, when prompted about some specific aspect of pricing, experts were able to respond with revealing expertise. To ensure that the researchers could probe experts for this quality expertise, they needed at least a basic understanding of pricing. This understanding was attained by studying the literature on pricing and through the natural learning that occurred as a result of the interaction with pricing practitioners. In the context of expert system development, Newquist referred to gaining this as the "knowledge engineer immersing himself in the expert's domain" (Newquist, 1989). This finding is consistent with Bramer's views on the future of knowledge engineering: ...we may see the current "generalist" knowledge engineer superseded by specialists who are themselves trained in a field such as insurance, medicine, or civil engineering and thus are hopefully better able than the generalist to elicit knowledge from subject experts in that field. (Bramer, 1988) Having a level of understanding of new product pricing enabled the researchers to glean more meaningful expertise from the experts. Hence expertise, usually in the form of rules of thumb, flowed more freely. Sometimes it was simply a case of having the expert confirm or clarify a piece of expertise that the researchers had gleaned from a book or journal. As some rules were elicited, other related rules came to the mind of the expert. The result of this process was that hundreds of independent rules of thumb were identified.
4.2. Knowledge Modelling The expertise at this stage was not in a form that could be directly encoded in an expert system. The rules were incomplete and there were no linkages between related rules. As of yet, no attempt had been made to organise the expertise into a decision-making model, and the
548 pieces of expertise, although meaningful, were disjointed. Knowledge modelling involves organising the expertise elicited during knowledge acquisition into what is termed a valid domain model of the decision making process (Barrett & Beerel, 1988). This model is then used as a basis for coding or programming the knowledge base. The significance of the task of knowledge modelling varies from system to system. In some straightforward rule-based expert systems, the factors and rules involved in the decision-making process simply fall into place and the task of building the domain model is relatively simple. With more significant applications however, the expertise elicited from the expert will be difficult to organise for the purpose of encoding into the knowledge base of the system. In the case of this expert system (simply called PRICE), the organisation of the expertise into an acceptable and meaningful knowledge base model proved a significant task, due to the lack of structure and the imprecise nature of marketing expertise. First, all of the concepts identified as contributing to the new product pricing decision were organised into factors (e.g., financial objectives for the project, anticipated level of price competition in the market, maturity of the market, level of sophistication of customers about the product, and others) and rules (i.e., how these factors are processed). Each factor assumes a symbolic value based on the product concerned, and the rule processes the factor according to this value. For example, if the factor anticipated level ofprice competition is assigned the value low, then it is processed on the basis that competition in the market will not be based on price and that higher price levels will be attainable. The factors were then organised into groups of related factors, for example, factors relating to company objectives, the target market, competitors, product cost, and others. In this way, a model for the decision process was constructed. This model needed several alterations before the researchers, based on feedback from the experts, were satisfied that it was an acceptable representation of how the new product pricing decision was made. Seven groups of related factors were finally agreed upon. These groups are shown in Table 1. It is important to remember that each of the factors contained within these groups is processed on the basis of the symbolic value assigned to the factor by the person using the system.
4.3. Knowledge Encoding At this stage a paper-based model of the decision-making process had been constructed. This paper model served as the specifications for developing the knowledge base of the system. For the purpose of actually coding this knowledge base, an expert system shell called Experience was used. Experience, developed by Expert Edge Computer Systems, Ltd., in Ireland, is a
C. Casey and C. Murphy TABLE 1 Factors Influencing Pricing Decision
Genera/company characteristics and objectives -Company's financial goals for the investment in the venture -Present company or brand image (i.e., customer orientation) -Prominence/strength of company (or brand) image Target market characteristics -Price sensitivity of customers in the target market -Maturity of the market Characteristics of the new product -Level of product differentiation -Identification of product characteristics of most value to customers -Performance/quality of product compared to nearest rivals -Level of customer perception of price as quality indicator Competition in the market -Significance of barriers to entry to market -Expected level of price competition in the market -Expected level of competition in the market, along factors other than price Potential for customers to be desensitized to price -Number of dimensions to product quality -Visibility of product quality -Level of sophistication of customers about the product -Is the product a "complementary product"? -Level of brand loyalty in the market -Importance to customers of peripheral product features -Frequency of purchase -Level of investment per purchase Cost structure -Product cost at time of launch -Extent to which the company will benefit from an experience curve effect -Extent to which the company will benefit from economies of scale Miscellaneous factors -Retailer margins -Long run production capacity -Funds available for promotion -Current monetary price levels in the market
PC-based expert system shell that represents expertise in the form of rules. Because new product pricing expertise was predominantly heuristic or procedural in nature, it was more easily expressed as I F - T H E N rules of thumb than descriptive or factual expertise, which tends to be more easily expressed in tables, networks, or frames (Barret & Beerel, 1988; Mahmood & Sullivan, 1992; Walden, 1992). Hence, rule-based representation was considered most suitable. In addition, the Experience shell allowed expert systems to be easily built and was particularly strong in the area of user interface development. Ease of use and the quality of the user interface were considered important because it was likely that the end users of the system would be business people from noncomputing backgrounds. The initial coding of the system was the most straightforward of the development tasks. However, this
Expert Systems in Marketing initial coding was refined and modified based on the views of experts. The initial prototype system consisted of about 400 rules. Based on feedback from experts, many of the rules were combined, some were dropped, and a few were added. The resulting system was pruned to under 300 rules. From a methodological standpoint, the development approach was similar to the standard prototyping approach, with the user unsure of the type of system required, constant feedback, and an evolving system. However, to provide the initial versions of the system, the developers had to become familiar with some of the expert's area in order to be able to elicit meaningful knowledge to build into the system. Based on the transfer of knowledge, information, and opinions, the final version of the system was constructed.
549 forced to price at lower levels. Symbolic values are thus assigned to each of these factors. Questions in the consultation module demand the user to respond by selecting appropriate values from menus. Based on the user's responses, values are assigned to variables representing the factors. These are then communicated to Module 2 for processing. Figure 1 shows some sample screens from the consultation module. Help facilities are available throughout the system to enable the user to operate and to understand the system and its processing. This help is context-sensitive and easily accessed. It is likely that the level of usage of these help facilities will depend on the level of sophistication of the user about the new product pricing decision. In this way, the expertise within the system is organised to cater to many levels of users, from those learning about pricing to those experienced in the area.
5. S Y S T E M S T R U C T U R E The expertise in the system is based on the premise that, although new product pricing procedures may vary from product to product, there is a core group of generic factors that should be considered in every new product pricing decision. The nature of these factors will vary from decision to decision (i.e., the symbolic value assigned), but the factors themselves do not change nor does the manner in which these factors are processed. For example, the financial objectives for a new product investment will vary from product to product. However, the factor "financial objectives" is an identifiable concept that should be accounted for in pricing every new product. Each factor can assume a value from a range of possible factor values. The way the system processes the factor (i.e., the rules applied) depends on the value it assumes. The expert system is logically divided into three modules: 1. Consultation Module 2. Processing Module 3. Validation Module
5.1. Consultation Module The purpose of the consultation module is to establish the value of each factor as it relates to the current market situation or new product pricing decision. In all, there are almost 30 factors, and each factor can be assigned one of between three and six symbolic values. This is achieved by having the user select from contextprompted menus. For example, in some product markets the level of price competition places severe downward pressure on price; in other markets there is little or no price competition. In some markets customers exhibit high levels of brand loyalty; in others customers shop around for the best offer. Some companies have a strong customer brand image, which they exploit to achieve premium prices for their products; less prominent brands/companies are not able to do so and are
5.2. Processing Module The processing module imports the values assigned during consultation. Some factor values are combined to form aggregate measures, weights are assigned to others based on their influence in the decision, while others trigger the calling up of further rules. Based on the values assigned to variables (and to variable combinations), the processing module applies the relevant rules from the knowledge base to the values provided by the user during consultation and processes the factors using a forward chaining reasoning mechanism. The following are sample factors along with an outline of the type of processing (expressed in English) conducted on each: FACTOR: Price Sensitivity of Customers in the Target Market. (see Figure 1: Consultation Screen 1) PROCESSING: The price sensitivity of the market is measured using the response to Consultation Screen 1, relating to the "importance of price in the purchase decision." This factor is processed on the basis that the more price sensitive the market is, the less is the opportunity to charge high prices (and visa versa). For example, if product price is " o f vital importance" for customers in their purchase decision, the customers are treated as "very price sensitive" and the factor is processed accordingly, i.e., the factor is assigned a value that will have a downward influence on the price strategy adopted. FACTOR: Strength of Company or Brand Image. (see Figure 1: Consultation Screen 2) PROCESSING: This factor is processed on the basis that the more prominent the company or brand image, the more confidence customers place in products that have that brand or company name. This increases the company's bargaining strength, as customers will usually be willing to pay higher prices for its products.
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H o w i m p o r t a n t a consideration would product price be, for
Customers in the target market, in deciding whether or not to purchase your product?
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How prominentis yourcompanyor brandimageamong the customersyou aim to targetwith this new product?
1) EXTREMELY PROMINENT: N A M E S Y N O N Y M O U S WITH THE PRODUCT IMPORTANT
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IT VARIES, BUT PRICE IS IMPORTANT FOR A SIGNIFICANT PORTION
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MODERATELY IMPORTANT
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4} WEAK OR FAIRLY UNKNOWN
OF ONLY MINOR IMPORTANCE 5) PREVIOUSLY NON-EXISTANT
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Sample Consultation Screen !.
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Sample Consultation Screen 2.
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Sample Consultation Screen 3 FIGURE 1. Sample screens from the consultation module.
Expert Systems in Marketing Therefore, strong or prominent brands are usually able to attain higher prices than weaker ones. FACTOR: Expected Level of Price Competition in the Market. (see Figure 1: Consultation Screen 3) P R O C E S S I N G : Price competition places severe downward pressure on both the price level and the likely profitability of the market. Therefore, a high level of expected price competition will force price downward, whereas low levels of expected price competition allow higher prices to be attained. Each factor is processed in its own way, with related factors being combined for processing. This culminates in the recommendation of a price strategy within certain price limits. For example, in a particular market the possible pricing options might range from a bargain price of $100 to a p r e m i u m price of $200. The system, based on its analysis of all the factors, might recomm e n d a high price strategy with an initial price in the range $175 to $185.
5.3. Validation Module Having received a recommendation, the user may wish to run the Validation Module, which provides an explanation of the reasoning behind the decision. Alternatively, the user may wish to rerun the system, change some values, and conduct some "what-if" analysis. If the user opts for validation, the system will provide an explanation for the recommendation, based on the most important (or influential) factors that contributed to the decision. In the validation module, there is also a deeper level of help, which explains how the system reasons about each factor. The user first interacts with the system by providing responses to about 30 questions asked during consultation. It is possible to skip some questions if the required information is not available and the system will reason with incomplete information by assigning a neutral value to the factor(s) involved. However, to reach a reliable decision it is desirable that all questions be answered, even if further thought or research is required. Within seconds of this information's being provided, the system generates its recommendation and offers the option of validation or a rerun. A complete session with the system might take as little as 10 to 20 minutes if the user is confident and quick to respond. It could take days if the user needs to leave the system from time to time, investigate a factor, and return to provide a response. In either case, the system contributes by ensuring that the decision is well researched before a solution is reached.
551 was not a sufficient criterion to determine the usefulness of expert systems in the area. It was necessary to test this usefulness or operational feasibility by having the system evaluated. This evaluation was conducted on the basis of personal interviews with pricing practition e r s - b u s i n e s s people who make new product pricing decisions. Because the system was generic in nature (in that it was not applied to a specific type of c o m p a n y or industry), the subjects were chosen randomly from various industries. Ten pricing practitioners evaluated the system. These subjects were required to test the system individually using information relating to a new product pricing decision with which they had recently been involved. After they had used the system, the subjects were interviewed to elicit their views on its usefulness. The same questions were asked of all subjects and their responses were recorded on structured questionnaires. The average testing time per subject was about 90 minutes, of which subjects spent approximately 70 working with the system and approximately 20 responding to evaluation questions. Based on an analysis of the findings, the researchers concluded that the system was considered a useful application. Pricing practitioners found the system useful in practice for the following reasons: • It contained significant pricing expertise, • It would help managers make more effective pricing decisions, • Although the expertise was generic in nature, it was found to be useful for specific pricing decisions, • The user interface made the system easy to understand and transparent to users, • It generated appropriate recommendations in 80% of test cases. The subjects also suggested that the system would be useful for managers from nonmarketing backgrounds who make new product pricing decisions and also for training novices; that it would be useful for ensuring that only relevant matters were discussed at meetings and for avoiding politically favoured decisions being implemented. Subjects also indicated that a useful characteristic of the system was that it ensured that marketing information, as distinct from internal accounting information, was used to determine price. The results of this evaluation by practitioners confirmed that the system was suitable for operational use and several companies have made use of the system for new product pricing decisions. 7. C O N C L U S I O N S A N D F U R T H U R RESEARCH
6. E V A L U A T I O N O F T H E E X P E R T S Y S T E M
In recent years, numerous expert systems have been developed for business applications. Although m a n y
Based on preliminary investigations (Section 3) and on the development of the system (Section 4), it was established that it was technically feasible to develop an expert system for new product pricing. However, that
Two subjectsfeltthat the pricestrategieswere too high. However, one of them subsequently informed the researchers that he had decided to adopt the recommended price strategy.
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o f these systems have i m p a c t e d on business m a n a g e ment, little progress has been m a d e in the area o f m a r keting, with m o s t expert system a p p l i c a t i o n s c o n c e n trated in o t h e r business functions, m a i n l y p r o d u c t i o n a n d finance. This scarcity o f m a r k e t i n g expert systems is usually attributed to the c o m p l e x nature o f m a r k e t i n g expertise. W h i l e accepting that m a r k e t i n g expertise is i n h e r e n t l y different from expertise in areas o f knowledge where expert systems have t r a d i t i o n a l l y been applied, the researchers c o n t e n d that these systems can m a k e valuable c o n t r i b u t i o n s to decision m a k i n g in m a n y areas o f marketing. T h e system d e v e l o p e d in this study has d e m o n strated that expert systems are useful in marketing. However, the role foreseen for these systems in m a r keting differs from the role o f c o n v e n t i o n a l expert syst e m a p p l i c a t i o n s in areas such as medicine, chemistry, a n d geology. T h e i r role in m a r k e t i n g should go further t h a n s i m p l y t a k i n g a well-defined a n d accepted b o d y o f knowledge, c o m p u t e r i z i n g it in an expert system, a n d using the system to m a k e repeatedly consistent decisions. T h e real c o n t r i b u t i o n foreseen for expert systems in m a r k e t i n g is m o r e in i m p r o v i n g the creativity a n d quality o f decisions, by m a k i n g practitioners m o r e knowledgeable a b o u t decision-making processes. Expert systems can m a k e available to practitioners m a r k e t i n g expertise from areas such as p r o d u c t pricing, p r o d u c t p r o m o t i o n a n d advertising, new p r o d u c t dev e l o p m e n t , a n d others, which otherwise w o u l d n o t be k n o w n by or easily accessible to such people. Hence, practitioners b e c o m e m o r e aware o f w h a t constitutes a g o o d decision, m o r e knowledgeable a b o u t the factors a n d reasoning involved, a n d m o r e confident a n d rigo r o u s in choosing a m o n g alternatives. This role o f m a r k e t i n g expert systems as intelligent aids to practitioners u p h o l d s the view t h a t c o m p u t e r s c a n n o t replace the role o f h u m a n reasoning in strategic business decisions such as marketing ones b u t that they can p r o v i d e valuable s u p p o r t leading to i m p r o v e d decision m a k i n g in such areas. T h e d e v e l o p m e n t methodology used for this study c o u l d enable the successful d e v e l o p m e n t o f expert systems for o t h e r m a r k e t i n g tasks like advertising a n d p r o m o t i o n , m a r k e t segmentation, b r a n d m a n a g e m e n t , a n d new p r o d u c t introduction.
REFERENCES Atkin, B., & Skinner, R. (1975). How British industryprices. London: Industrial Market Research Ltd. Barrett, M. L,, & Beerel, A. C. (1988). Expert systems in business: A practical approach. Sussex, UK: Ellis Horwood Ltd. Baxter, W.T., & Oxenfeldt, A.R. ( 1961, Winter) Costing and pricing: The cost accountant versus the economist. Business Horizons, pp. 17-39. Bernstein, A. (1989). MCI wins marketing game with 'Expert' IS strategy. Computer World, 23(37):18-19.
Biswas, G., Oliff, M., & Sen, A. (1988), An expert decision support system for production control. Decision Support Systems, 4:235247. Blanning, R.W. (1987). A survey of issues in expert systems for management. In B.G. Silverman, ed., Expert systemsfor business (pp. 24-39). Reading, MA: Addison Wesley. Bramer, M.A. (1988). Applying expert systems in business: A critical overview. In M. A. Bramer, ed., Proceedings of the second international symposium on artificial intelligenceand expert systems, Berlin, 3, 17-39. Cashmore, C., & Lyall, R. (1991). Business information systems and strategies. Englewood Cliffs, NJ: Prentice Hall. Daly, D. (1990). Expert systems. ICSA National Conference, March 29/30 (Unpublished). Dublin, Ireland. Dean, J. (1960). Pricing policies for new products. In B. P. Shapiro (Ed.), Harvard business review: Pricingfor profit (pp. 79-91). Feigenbaum, E., McCorduck, P., & Ni, H.P. (1988). The rise of the expert company. London, UK: Macmillan. Gabor, A. (1985). Pricing. Cambridge, UK: Cambridge University Press. Hayes-Roth, F. (1984). Knowledge-based expert systems--the state of the art in the US. In J. Fox, Expert systems--State of the art report, 12(7), 52, 54. Keating, D. (1991). Pricing practices in Irish companies. Irish Accounting Association Conference. (Unpublished). Dublin, Ireland. Liebowitz, J. (ed.) (1990), Expert systems for business and management. Englewood Cliffs, NJ: Prentice Hall. McDonald, M. (1989). Marketing planning and expert systems: An epistemology of practice. Marketing Intelligence and Planning, 89(7): 16-23. Mockler, R.J. (1989). Knowledge based systemsfor management decisions. Englewood Cliffs, NJ: Prentice Hall. Mockler, R.P., & Dologite, D.G. (1992). Expert systems for human resource management: Development and implementation. In Proceedings of the Hawaii International Conference on System Sciences (Vol. 3, pp. 129-132). Murdock, H. (1990). Choosing a problem--When is artificial intelligence appropriate for the retail industry? Expert Systems, 7( 1): 42-49. Nagle, T. ( 1983, July-August). Pricing as creative marketing. Business Horizons, pp. 14-19. Newquist, H.P. (1989), Expert systems: The promise of a smart machine. In J. C. Wetherbe, V.T. Dock, & S. Mandell (Eds.), Readings in information systems:A managementperspective(pp. 358365) St. Paul, MN: West. Oxenfeldt, A.R. (1960). Multi-stage approach to pricing. In B. P. Shapiro (Ed.), Harvard Business Review: Pricingfor Profit. (pp. 12-20). Prerau, D.S. (1985). Selection of an appropriate domain for an expert system. The AI Magazine, 6(2):26-30. Schutzer, D. (1990). Business expert systems: The competitive edge. Expert Systems with Applications, I: 17-21. Sen, A., & Biswas, G. (1985). Decision support systems: An expert system approach. Decision Support Systems, 31(1): 197-204. Slagle,J.R., & Wick, M.R. (1988). A method for evaluating candidate expert system applications. The AI Magazine, 9(4):44-53. Walden, P. (1992). Rule-based expert systems in strategic market management. In Proceedings of the Hawaii International Conference on System Sciences (Vol. III, pp. 111-121). Waiters, J. (1987). Determining the feasibility of knowledge-based applications. In Expert Systems Conferenceand Exposurefor Advanced Manufacturing Technology, Dearborn, MI. Westland, J.C. (1992). Integrating expert systems and compiler technology in an accounting environment in strategic market management. In Proceedings of the Hawaii International Conference on System Sciences (Vol. III, pp. 133-144). Winkler, J. (1983). Pricingfor results. Dublin, Ireland: Mount Salus Press.
Pergamon
Expert Systems in Marketing: An Application for Pricing New Products CATHAL CASEY AND CIARAN MURPHY Department of Accounting,Financeand InformationSystems,UniversityCollegeCork,Cork, Ireland
Abstract-- The use of expert systems in support of the marketing function is a relatively new field within expert systems. This paper reports on the issues arising in the development of an expert system for a key area of marketing--that of new product pricing. The paper describes the approach followed in the acquisition, modelling, and encoding of marketing expertise. The system developed is described, and sample consultations are outlined. An important aspect of the system detailed in this study is that it has been validated by marketing personnel in a range of businesses and has been used by a number of companies to support them in their new product pricing.
1. INTRODUCTION
are better understood than problems involving customers and markets (Feigenbaum, McCorduck, & Ni, 1988). The possible gains, risks, and costs, along with the effect on corporate performance, can be more easily measured for systems concerned with internal problems. It is likely therefore that the external focus of marketing problems has inhibited the application of expert systems in the area. This represents a significant oversight on behalf of the business community, given that the information technology applications that are potentially most rewarding are those in strategic areas of business such as marketing (Cashmore & Lyall, 1991). It is not surprising therefore that where expert systems have been applied in marketing (albeit in relatively isolated cases), the applications have been considered successful and useful (Bernstein, 1989; McDonald, 1989; Mockler, 1989; Mockler & Dologite, 1992). This paper reports on a study that was concerned with the development of an expert system in an important area of marketing--that of new product pricing. Issues involved in the acquisition, modelling, and programming of marketing expertise are outlined, followed by descriptions of the system structure and operation. The paper concludes with a report on its evaluation by business managers.
THE DEVELOPMENTof expert systems for use in business management has grown significantly in the past decade (Biswas, Oliff, & Sen, 1988; Blanning, 1987; Liebowitz, 1990; Mockler, 1989; Mockler & Dologite, 1992; Schutzer, 1990; Sen & Biswas, 1985; Walden, 1992; Westland, 1992). Although most of these management applications are concentrated in the production and finance functions of business, a number of researchers have proposed expert system applications for marketing tasks, e.g. areas of advertising, brand management, new product introduction, and product pricing (Blanning, 1987; MacDonald, 1989; Walden, 1992). Despite these proposals little progress has been made in applying expert systems in the area of marketing. A conventional explanation put forward for the lack of marketing expert systems is that marketing tasks differ in nature from tasks where expert systems have traditionally been applied. Marketing problems do not lend themselves to the same precise logic as, for example, scientific problems (McDonald, 1989). Marketing managers deal with uncertainties and vague concepts and make judgements using criteria and rules that are difficult to define. Their knowledge does not fit neatly into underlying models, and therefore expert system solutions have not been considered suitable for marketing problems. Moreover, companies prefer to try out new technologies, such as expert systems, on problems that involve internal operations, because they
2. NEW PRODUCT PRICING Like most marketing tasks, the pricing of new products is an exercise characterised by few, if any, clearly defined or generally accepted principles. The literature on pricing is rife with criticism of the practices adopted by pricing practitioners in companies (Gabor, 1985; Keating, 1991; Nagle, 1983; Winkler, 1983). Modem
Requestsfor reprintsshouldbe sentto CathalCasey,Departmentof Accounting, Financeand InformationSystems,UniversityCollege Cork, Cork, Ireland. 545
546 marketing principles, it seems, have widely influenced all elements of marketing strategy except pricing (Nagle, 1983). Gabor, for example, described the pricing of new products, particularly those that are technologically innovative, as "pricing in a vacuum," because the company can have only scant knowledge and experience of the product and market (Gabor, 1985). Dean remarked that in practice product pricing was "a top management puzzle too often solved by cost-theology and hunch" (Dean, 1960). The most frequent criticism of pricing practices is that the task of setting price is too often based on product cost information, at the expense of more relevant information relating to the market, competitors' prices, and company objectives. Too often, simple pricing mechanisms such as cost-plus formulae are used to determine product price (Winkler, 1983), with inadequate research and consideration going into the pricing process. Atkin and Skinner (1975) for example, estimated that up to 80% of companies used cost-related pricing techniques when pricing products. This overreliance on cost-based information is attributable to the fact that such information is easily obtained within the company. In contrast, information relating to market demand and competing products is exogenous to the company and is more difficult to collect, interpret, and build into the decision process. However, cost information should never replace market information in the pricing of products, because cost information gives no insight into market demand. Baxter and Oxenfeldt (196 l) advise that even crude estimates of market demand serve better than cost information in pricing products. Many researchers are of the opinion that although product pricing decisions, and marketing decisions in general, require creative thinking (in that there are no simple rules or mechanisms to apply when making these decisions), there is a body of expertise in the area which is currently being ignored, or at best being misapplied, by pricing practitioners in most companies (Keating, 1991; Nagle, 1983; Oxenfeldt, 1960; Winkler, 1983). Assuming that this body of expertise could be collected and represented in a knowledge base, a welldesigned expert system would be capable of disseminating it to those involved in pricing new products. At present this expertise is not readily available to these people, because it is contained in a wide variety of sources such as books, journals, and in many cases in the minds of experienced pricing practitioners. This role for an expert system in marketing is along the lines proposed by McDonald (1989) and Walden (1992): ...in general, marketing decisions are taken without sufficient analysis and understanding of the relevant issues. The reason for this is a lack of knowledge and understanding of how and why the multifarious factors of marketing interact and serve to form the parameters of any business activity. In this real
C. Casey and C. Murphy life situation we see emerging the perfect role for an expert system in marketing planning. (McDonald, 1989) The approach taken in this study was, first, to investigate if an expert system was a feasible option in addressing the task of pricing new products. This preliminary investigation was undertaken in the realisation that expert systems are not suitable solutions for all problems and that many expert system applications have resulted in failure. In fact, the c o m m o n misconception of expert systems as a panacea for all problems has contributed to the failure of many applications. This preliminary investigation was designed to avoid rash development. If, on the basis of this investigation, an expert system was considered appropriate for new product pricing, the remainder of the study was to be devoted to the development and evaluation of such a system. If it was found that an expert system was not feasible for the task, the proposal would be rejected prior to any development effort taking place. 3. I N V E S T I G A T I N G T H E FEASIBILITY OF AN E X P E R T S Y S T E M FOR N E W P R O D U C T PRICING The most commonly prescribed approaches for analysing if expert systems are suitable for addressing problems feature checklists of desirable attributes or characteristics (Murdock 1990). These checklists vary in length and in procedural rigour (Hayes-Roth, 1984; Prerau, 1985; Slagle & Wick, 1988; Walters, 1987), but all operate on the basic principle of examining the characteristics of problem solving in a particular area to check for the presence of attributes that indicate the suitability of an expert system application. Characteristics such as the following are often identified: • The problem should be a high value, nontrivial one. • There should be recognized experts who solve the problem, and these people should be capable of communicating their knowledge. • Heuristic knowledge (sometimes called I F - T H E N rules of thumb or general cause and effect relationships), gained through experience of the task, should form the basis for problem solving in the area. • Conventional programming/algorithmic solutions to the problem should be unsatisfactory. • It might be necessary to solve the problem using incomplete and uncertain information. • The problem should be sufficiently narrow and selfcontained that the number of important concepts (such as factors to account for and rules to apply) should be bounded to several hundreds. Based on an analysis of the area of new product pricing using such prescribed criteria, the researchers were satisfied that the task was one for which an expert system could be developed. The task was certainly nontrivial, in that the consequences of incorrectly
Expert Systems in Marketing pricing a new product were significant (Gabor, 1985; Winkler, 1983); there were identifiable sources of expertise to which the researchers could relate (in fact, several research studies had been devoted to the task); the task required heuristic knowledge (general rules of thumb) for its solution, and often required prices to be set on the basis of incomplete and uncertain information (Gabor, 1985). Conventional algorithmic approaches to new product pricing (e.g., cost-based pricing formulae) were not satisfactory (Oxenfeldt, 1960), and from the beginning the problem appeared to be sufficiently narrow and self-contained that the number of important concepts would never exceed several hundred. Furthermore, the task was characterised by the misapplication (or nonapplication) of a body of expertise and this feature strongly indicated that an expert system solution might be viable (Daly, 1990). Assuming that the relevant expertise could be captured, it was likely that this system would prove to be a useful support tool in an area where decision makers were overreliant on inappropriate and simplistic techniques for important organisational decisions. As a preliminary step, discussions were undertaken with three parties--a pricing practitioner, an academic from the discipline of marketing, and an expert system developer--to elicit their views on the proposed expert system application. Positive responses were received from the academic and from the expert system developer. The pricing practitioner did not fully understand the concept of an expert system, but did indicate that a computerised software tool incorporating new product pricing expertise would be useful. On the basis of these preliminary findings, it was decided to proceed with the development of an expert system for new product pricing and to subsequently evaluate its usefulness.
4. DEVELOPMENT OF THE EXPERT SYSTEM 4.1. Knowledge Acquisition Unlike conventional system developers, developers of expert systems do not simply analyze the problem, get the user to sign off, and then proceed with development. Rather, they work with experts throughout the entire development process, constantly acquiring new knowledge and integrating it into an evolving system. For this reason the development process is more akin to a prototyping or iterative approach to system development, with high levels of interaction between the expert and knowledge engineer. As an initial step in the development of the system, practitioners involved in pricing were interviewed in an attempt to establish how new product pricing decisions were made. It became clear, however, that these
547 experts were unable to articulate their decision making process in sufficient detail. First, the practitioners did not describe their knowledge in a well-structured form that suited the idea of an underlying decision model. Secondly, their knowledge consisted largely of rules of thumb, which were private and difficult to recall and articulate in an interview. Finally, practitioners did not consider their methods of pricing to constitute expertise and were inhibited by this perceived inadequacy when questioned about their decision making. They felt that their approaches to the problem were not methodical enough, could be improved, and were therefore slow to reveal their latent expertise. From these interviews, it became clear that there was a communication problem between the researchers (as knowledge engineers in this context) and the pricing practitioners (as experts). It appeared that the questions asked of experts were normally too vague or general to elicit useful responses. However, when prompted about some specific aspect of pricing, experts were able to respond with revealing expertise. To ensure that the researchers could probe experts for this quality expertise, they needed at least a basic understanding of pricing. This understanding was attained by studying the literature on pricing and through the natural learning that occurred as a result of the interaction with pricing practitioners. In the context of expert system development, Newquist referred to gaining this as the "knowledge engineer immersing himself in the expert's domain" (Newquist, 1989). This finding is consistent with Bramer's views on the future of knowledge engineering: ...we may see the current "generalist" knowledge engineer superseded by specialists who are themselves trained in a field such as insurance, medicine, or civil engineering and thus are hopefully better able than the generalist to elicit knowledge from subject experts in that field. (Bramer, 1988) Having a level of understanding of new product pricing enabled the researchers to glean more meaningful expertise from the experts. Hence expertise, usually in the form of rules of thumb, flowed more freely. Sometimes it was simply a case of having the expert confirm or clarify a piece of expertise that the researchers had gleaned from a book or journal. As some rules were elicited, other related rules came to the mind of the expert. The result of this process was that hundreds of independent rules of thumb were identified.
4.2. Knowledge Modelling The expertise at this stage was not in a form that could be directly encoded in an expert system. The rules were incomplete and there were no linkages between related rules. As of yet, no attempt had been made to organise the expertise into a decision-making model, and the
548 pieces of expertise, although meaningful, were disjointed. Knowledge modelling involves organising the expertise elicited during knowledge acquisition into what is termed a valid domain model of the decision making process (Barrett & Beerel, 1988). This model is then used as a basis for coding or programming the knowledge base. The significance of the task of knowledge modelling varies from system to system. In some straightforward rule-based expert systems, the factors and rules involved in the decision-making process simply fall into place and the task of building the domain model is relatively simple. With more significant applications however, the expertise elicited from the expert will be difficult to organise for the purpose of encoding into the knowledge base of the system. In the case of this expert system (simply called PRICE), the organisation of the expertise into an acceptable and meaningful knowledge base model proved a significant task, due to the lack of structure and the imprecise nature of marketing expertise. First, all of the concepts identified as contributing to the new product pricing decision were organised into factors (e.g., financial objectives for the project, anticipated level of price competition in the market, maturity of the market, level of sophistication of customers about the product, and others) and rules (i.e., how these factors are processed). Each factor assumes a symbolic value based on the product concerned, and the rule processes the factor according to this value. For example, if the factor anticipated level ofprice competition is assigned the value low, then it is processed on the basis that competition in the market will not be based on price and that higher price levels will be attainable. The factors were then organised into groups of related factors, for example, factors relating to company objectives, the target market, competitors, product cost, and others. In this way, a model for the decision process was constructed. This model needed several alterations before the researchers, based on feedback from the experts, were satisfied that it was an acceptable representation of how the new product pricing decision was made. Seven groups of related factors were finally agreed upon. These groups are shown in Table 1. It is important to remember that each of the factors contained within these groups is processed on the basis of the symbolic value assigned to the factor by the person using the system.
4.3. Knowledge Encoding At this stage a paper-based model of the decision-making process had been constructed. This paper model served as the specifications for developing the knowledge base of the system. For the purpose of actually coding this knowledge base, an expert system shell called Experience was used. Experience, developed by Expert Edge Computer Systems, Ltd., in Ireland, is a
C. Casey and C. Murphy TABLE 1 Factors Influencing Pricing Decision
Genera/company characteristics and objectives -Company's financial goals for the investment in the venture -Present company or brand image (i.e., customer orientation) -Prominence/strength of company (or brand) image Target market characteristics -Price sensitivity of customers in the target market -Maturity of the market Characteristics of the new product -Level of product differentiation -Identification of product characteristics of most value to customers -Performance/quality of product compared to nearest rivals -Level of customer perception of price as quality indicator Competition in the market -Significance of barriers to entry to market -Expected level of price competition in the market -Expected level of competition in the market, along factors other than price Potential for customers to be desensitized to price -Number of dimensions to product quality -Visibility of product quality -Level of sophistication of customers about the product -Is the product a "complementary product"? -Level of brand loyalty in the market -Importance to customers of peripheral product features -Frequency of purchase -Level of investment per purchase Cost structure -Product cost at time of launch -Extent to which the company will benefit from an experience curve effect -Extent to which the company will benefit from economies of scale Miscellaneous factors -Retailer margins -Long run production capacity -Funds available for promotion -Current monetary price levels in the market
PC-based expert system shell that represents expertise in the form of rules. Because new product pricing expertise was predominantly heuristic or procedural in nature, it was more easily expressed as I F - T H E N rules of thumb than descriptive or factual expertise, which tends to be more easily expressed in tables, networks, or frames (Barret & Beerel, 1988; Mahmood & Sullivan, 1992; Walden, 1992). Hence, rule-based representation was considered most suitable. In addition, the Experience shell allowed expert systems to be easily built and was particularly strong in the area of user interface development. Ease of use and the quality of the user interface were considered important because it was likely that the end users of the system would be business people from noncomputing backgrounds. The initial coding of the system was the most straightforward of the development tasks. However, this
Expert Systems in Marketing initial coding was refined and modified based on the views of experts. The initial prototype system consisted of about 400 rules. Based on feedback from experts, many of the rules were combined, some were dropped, and a few were added. The resulting system was pruned to under 300 rules. From a methodological standpoint, the development approach was similar to the standard prototyping approach, with the user unsure of the type of system required, constant feedback, and an evolving system. However, to provide the initial versions of the system, the developers had to become familiar with some of the expert's area in order to be able to elicit meaningful knowledge to build into the system. Based on the transfer of knowledge, information, and opinions, the final version of the system was constructed.
549 forced to price at lower levels. Symbolic values are thus assigned to each of these factors. Questions in the consultation module demand the user to respond by selecting appropriate values from menus. Based on the user's responses, values are assigned to variables representing the factors. These are then communicated to Module 2 for processing. Figure 1 shows some sample screens from the consultation module. Help facilities are available throughout the system to enable the user to operate and to understand the system and its processing. This help is context-sensitive and easily accessed. It is likely that the level of usage of these help facilities will depend on the level of sophistication of the user about the new product pricing decision. In this way, the expertise within the system is organised to cater to many levels of users, from those learning about pricing to those experienced in the area.
5. S Y S T E M S T R U C T U R E The expertise in the system is based on the premise that, although new product pricing procedures may vary from product to product, there is a core group of generic factors that should be considered in every new product pricing decision. The nature of these factors will vary from decision to decision (i.e., the symbolic value assigned), but the factors themselves do not change nor does the manner in which these factors are processed. For example, the financial objectives for a new product investment will vary from product to product. However, the factor "financial objectives" is an identifiable concept that should be accounted for in pricing every new product. Each factor can assume a value from a range of possible factor values. The way the system processes the factor (i.e., the rules applied) depends on the value it assumes. The expert system is logically divided into three modules: 1. Consultation Module 2. Processing Module 3. Validation Module
5.1. Consultation Module The purpose of the consultation module is to establish the value of each factor as it relates to the current market situation or new product pricing decision. In all, there are almost 30 factors, and each factor can be assigned one of between three and six symbolic values. This is achieved by having the user select from contextprompted menus. For example, in some product markets the level of price competition places severe downward pressure on price; in other markets there is little or no price competition. In some markets customers exhibit high levels of brand loyalty; in others customers shop around for the best offer. Some companies have a strong customer brand image, which they exploit to achieve premium prices for their products; less prominent brands/companies are not able to do so and are
5.2. Processing Module The processing module imports the values assigned during consultation. Some factor values are combined to form aggregate measures, weights are assigned to others based on their influence in the decision, while others trigger the calling up of further rules. Based on the values assigned to variables (and to variable combinations), the processing module applies the relevant rules from the knowledge base to the values provided by the user during consultation and processes the factors using a forward chaining reasoning mechanism. The following are sample factors along with an outline of the type of processing (expressed in English) conducted on each: FACTOR: Price Sensitivity of Customers in the Target Market. (see Figure 1: Consultation Screen 1) PROCESSING: The price sensitivity of the market is measured using the response to Consultation Screen 1, relating to the "importance of price in the purchase decision." This factor is processed on the basis that the more price sensitive the market is, the less is the opportunity to charge high prices (and visa versa). For example, if product price is " o f vital importance" for customers in their purchase decision, the customers are treated as "very price sensitive" and the factor is processed accordingly, i.e., the factor is assigned a value that will have a downward influence on the price strategy adopted. FACTOR: Strength of Company or Brand Image. (see Figure 1: Consultation Screen 2) PROCESSING: This factor is processed on the basis that the more prominent the company or brand image, the more confidence customers place in products that have that brand or company name. This increases the company's bargaining strength, as customers will usually be willing to pay higher prices for its products.
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H o w i m p o r t a n t a consideration would product price be, for
Customers in the target market, in deciding whether or not to purchase your product?
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IT VARIES, BUT PRICE IS NOT VERY IMPORTANT FOR A SIGNIFICANT PORTION
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OF ONLY MINOR IMPORTANCE 5) PREVIOUSLY NON-EXISTANT
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Sample Consultation Screen 3 FIGURE 1. Sample screens from the consultation module.
Expert Systems in Marketing Therefore, strong or prominent brands are usually able to attain higher prices than weaker ones. FACTOR: Expected Level of Price Competition in the Market. (see Figure 1: Consultation Screen 3) P R O C E S S I N G : Price competition places severe downward pressure on both the price level and the likely profitability of the market. Therefore, a high level of expected price competition will force price downward, whereas low levels of expected price competition allow higher prices to be attained. Each factor is processed in its own way, with related factors being combined for processing. This culminates in the recommendation of a price strategy within certain price limits. For example, in a particular market the possible pricing options might range from a bargain price of $100 to a p r e m i u m price of $200. The system, based on its analysis of all the factors, might recomm e n d a high price strategy with an initial price in the range $175 to $185.
5.3. Validation Module Having received a recommendation, the user may wish to run the Validation Module, which provides an explanation of the reasoning behind the decision. Alternatively, the user may wish to rerun the system, change some values, and conduct some "what-if" analysis. If the user opts for validation, the system will provide an explanation for the recommendation, based on the most important (or influential) factors that contributed to the decision. In the validation module, there is also a deeper level of help, which explains how the system reasons about each factor. The user first interacts with the system by providing responses to about 30 questions asked during consultation. It is possible to skip some questions if the required information is not available and the system will reason with incomplete information by assigning a neutral value to the factor(s) involved. However, to reach a reliable decision it is desirable that all questions be answered, even if further thought or research is required. Within seconds of this information's being provided, the system generates its recommendation and offers the option of validation or a rerun. A complete session with the system might take as little as 10 to 20 minutes if the user is confident and quick to respond. It could take days if the user needs to leave the system from time to time, investigate a factor, and return to provide a response. In either case, the system contributes by ensuring that the decision is well researched before a solution is reached.
551 was not a sufficient criterion to determine the usefulness of expert systems in the area. It was necessary to test this usefulness or operational feasibility by having the system evaluated. This evaluation was conducted on the basis of personal interviews with pricing practition e r s - b u s i n e s s people who make new product pricing decisions. Because the system was generic in nature (in that it was not applied to a specific type of c o m p a n y or industry), the subjects were chosen randomly from various industries. Ten pricing practitioners evaluated the system. These subjects were required to test the system individually using information relating to a new product pricing decision with which they had recently been involved. After they had used the system, the subjects were interviewed to elicit their views on its usefulness. The same questions were asked of all subjects and their responses were recorded on structured questionnaires. The average testing time per subject was about 90 minutes, of which subjects spent approximately 70 working with the system and approximately 20 responding to evaluation questions. Based on an analysis of the findings, the researchers concluded that the system was considered a useful application. Pricing practitioners found the system useful in practice for the following reasons: • It contained significant pricing expertise, • It would help managers make more effective pricing decisions, • Although the expertise was generic in nature, it was found to be useful for specific pricing decisions, • The user interface made the system easy to understand and transparent to users, • It generated appropriate recommendations in 80% of test cases. The subjects also suggested that the system would be useful for managers from nonmarketing backgrounds who make new product pricing decisions and also for training novices; that it would be useful for ensuring that only relevant matters were discussed at meetings and for avoiding politically favoured decisions being implemented. Subjects also indicated that a useful characteristic of the system was that it ensured that marketing information, as distinct from internal accounting information, was used to determine price. The results of this evaluation by practitioners confirmed that the system was suitable for operational use and several companies have made use of the system for new product pricing decisions. 7. C O N C L U S I O N S A N D F U R T H U R RESEARCH
6. E V A L U A T I O N O F T H E E X P E R T S Y S T E M
In recent years, numerous expert systems have been developed for business applications. Although m a n y
Based on preliminary investigations (Section 3) and on the development of the system (Section 4), it was established that it was technically feasible to develop an expert system for new product pricing. However, that
Two subjectsfeltthat the pricestrategieswere too high. However, one of them subsequently informed the researchers that he had decided to adopt the recommended price strategy.
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C. Casey and C. Murphy
o f these systems have i m p a c t e d on business m a n a g e ment, little progress has been m a d e in the area o f m a r keting, with m o s t expert system a p p l i c a t i o n s c o n c e n trated in o t h e r business functions, m a i n l y p r o d u c t i o n a n d finance. This scarcity o f m a r k e t i n g expert systems is usually attributed to the c o m p l e x nature o f m a r k e t i n g expertise. W h i l e accepting that m a r k e t i n g expertise is i n h e r e n t l y different from expertise in areas o f knowledge where expert systems have t r a d i t i o n a l l y been applied, the researchers c o n t e n d that these systems can m a k e valuable c o n t r i b u t i o n s to decision m a k i n g in m a n y areas o f marketing. T h e system d e v e l o p e d in this study has d e m o n strated that expert systems are useful in marketing. However, the role foreseen for these systems in m a r keting differs from the role o f c o n v e n t i o n a l expert syst e m a p p l i c a t i o n s in areas such as medicine, chemistry, a n d geology. T h e i r role in m a r k e t i n g should go further t h a n s i m p l y t a k i n g a well-defined a n d accepted b o d y o f knowledge, c o m p u t e r i z i n g it in an expert system, a n d using the system to m a k e repeatedly consistent decisions. T h e real c o n t r i b u t i o n foreseen for expert systems in m a r k e t i n g is m o r e in i m p r o v i n g the creativity a n d quality o f decisions, by m a k i n g practitioners m o r e knowledgeable a b o u t decision-making processes. Expert systems can m a k e available to practitioners m a r k e t i n g expertise from areas such as p r o d u c t pricing, p r o d u c t p r o m o t i o n a n d advertising, new p r o d u c t dev e l o p m e n t , a n d others, which otherwise w o u l d n o t be k n o w n by or easily accessible to such people. Hence, practitioners b e c o m e m o r e aware o f w h a t constitutes a g o o d decision, m o r e knowledgeable a b o u t the factors a n d reasoning involved, a n d m o r e confident a n d rigo r o u s in choosing a m o n g alternatives. This role o f m a r k e t i n g expert systems as intelligent aids to practitioners u p h o l d s the view t h a t c o m p u t e r s c a n n o t replace the role o f h u m a n reasoning in strategic business decisions such as marketing ones b u t that they can p r o v i d e valuable s u p p o r t leading to i m p r o v e d decision m a k i n g in such areas. T h e d e v e l o p m e n t methodology used for this study c o u l d enable the successful d e v e l o p m e n t o f expert systems for o t h e r m a r k e t i n g tasks like advertising a n d p r o m o t i o n , m a r k e t segmentation, b r a n d m a n a g e m e n t , a n d new p r o d u c t introduction.
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