AN EXPERT SYSTEMS APPROACH FOR TEACHING MARKETING CASE ANALYSIS Developments in Business Simulation & Experiential Exercises, Volume 14, 1987 31 AN EXPERT SYSTEMS APPROACH FOR TEACHING MARKETING CASE ANALYSIS Hugh M. Cannon, Wayne State University Fred W. Morgan, Wayne State University ABSTRACT This paper presents a procedure for using an expert-system development program to enhance marketing students’ case analysis skills. The procedure presents students with case studies and requires them to develop a set of decision rules to select an appropriate marketing strategy for each case. Students’ decision rules are used as feedback by instructors, enabling them to adjust teaching style to students’ needs. INTRODUCTION The past decade has witnessed explosive growth in the development of expert systems technology. This term “expert systems” refers to a branch of artificial intelligence in which sets of heuristic problem-solving procedures are developed to simulate human decision-making. They are referred to as “expert” systems because they have been traditionally developed by interviewing recognized experts in a given field to determine how they make decisions. Their decision rules are then codified to form the expert system, as illustrated in the famous MYCIN medical-diagnostic system experiments during the 1970s [2] Expert systems technology does not require the use of experts in the conventional sense. Rather, it focuses on programming computers to process information and make actual decisions, much as an actual human decision-maker might do. A system might be developed by modeling expert decision-making, by drawing on the knowledge of many different experts, or by charting the decisions called for by a body of established theory, independent of expert judgment [8]. In marketing and economics, modeling efforts have often sought to explain how typical consumers [13] or industrial decision-makers [5], maybe not experts, make decision. Developmentally-Oriented Systems In practice, the approach taken to expert systems varies with the primary objectives for which the system is being constituted. First, systems vary according to their developmental orientation, whether they are research or applications oriented. The former are constructed primarily for the insights they provide into the ways people make decisions or choices. This is characteristic of the MYCIN experiments on spinal meningitis. The MYCIN experimenters were mainly interested in discovering techniques by which a computer might model the diagnostic process. By contrast, other systems are applications-oriented. They are conceived to solve problems, rather than to show how problems are solved, for example the development of ONCOCIN for clinical cancer diagnosis [14]. Functionally-Oriented Systems Objectives also vary according the functional orientation of the system. whether it is being developed to help people to solve actual problems or whether it is to be used to train people in problem-solving skills. MYCIN was organized to address actual problem-solving activities, while GUIDON was a MYCIN-derived system developed for training purposes [3]. Overview of Expert Systems Types Figure 1 captures the various distinctions just noted, suggesting four general classes of expert-systems objectives. Type-l objectives call for the creation of expert systems in an effort to understand how actual experts might make decisions. MYCIN is the prototypic example, but much of the work done in marketing decision-making would also fall into this category. The classic works of Nicosia [11], Engel, Kollat, and Blackwell [5], and Howard and Sheth [9], and more recently Bettman [1], illustrate efforts made to model and to understand consumer decision processes for marketing strategy purposes. Many of these models are expressed in terms of actual computer models of consumer processes [6; 7; 12;]. In the management decision-making area, the classic pricing behavior studies carried out at Carnegie Mellon in the early 1960s exemplify type-l objectives [4]. Type-2 objectives call for the construction of expert systems that are designed to facilitate actual managerial decision- making. ONCOGIN is the prototypic example, but the marketing literature contains other examples of decision models that might fall into this classification [10]. Type-3 objectives imply the development of systems that teach students how experts make decisions. These might include everything from expert-based tutorial programs to actual expert systems, the use of which provide students with guided experience in the decision-making process. Systems of this nature are not described in the marketing literature, but such applications are not difficult to imagine. Harmon and King [8] summarize the training applications of expert systems in general. Developments in Business Simulation & Experiential Exercises, Volume 14, 1987 32 Type-4 objectives differ from type-3 in that the students are involved in developing (as opposed to simply using) expert systems as a learning process. One approach is very similar to what happens in many types of case classes: students are asked to analyze cases and to make actual decisions, taking the role of the expert decision-maker. The instructor via questioning and commenting then helps them to analyze the nature and quality of their decision processes. The remainder of this paper addresses type-4 objectives. It uses an expert system development software package to help students model, and thus gain insight regarding, their own decision-making. USING EXPERT SYSTEMS IN CASE ANALYSIS Implicit in the case study approach is the existence of heuristic decision processes that will enable someone to solve actual cases in the so-called real world. These processes draw upon decision rules that address usual types of marketing problems. For instance, a case might call for the selection of a “volume” strategy: How is the company going to increase sales volume? The correct answer might be to increase the number of product users in the category, to increase the usage rate among current users, or to increase market share without changing the number of people in the market or their usage rate. While solving this kind of case, students begin to conceptualize these heuristic processes. They start to classify case types and rules for considering them. For instance, if products are in the early stages of the product life cycle, the decision might be to initiate a growth strategy, bringing new customers into the market. Products that are late in the life cycle might call for increased usage or increased market share. Classroom Use The method to be discussed here incorporates an expert- system development software package to facilitate this process. Three steps should be carried out: 1. Students are presented with a series of carefully structured cases to analyze. Each case contains information that the instructors deem important to the selection of different marketing strategies, or in some situations, similar strategies with different rationales. 2. The program asks students to select a strategy appropriate for the first case. It then requires them to choose a key fact that differentiates the first case from the second, and so on. Students add cases one at a time, each time selecting a strategy and the key differentiating facts that set the case apart from all others. 3. Based on the differentiating facts, the program compiles an expert system- -a program that will ask key questions about a case and suggest solutions based on answers the user provides. The program provides an analysis of the decision rules implicit in each student’s analysis. The instructor reviews these and presents feedback to the student. This can be used to structure future lectures and/or cases, or it can be used as a basis for class discussion. By making students identify key facts and strategies, the method forces them to be explicit about the rules they are designing to solve the cases. The outcomes can be dramatic. First, students can recreate the model intended by the instructor. When this happens, students react with a sense of “Ahah!” and the whole process takes on added meaning. Second, students might discover a new set of rules that the instructor had not imagined. This can lead to a new level of understanding by both the instructor and the students as they discuss the nature and implications of the extended set of rules. For instance, the cases might be structured to illustrate the use of growth strategies for market leaders early in the life cycle and market share strategies for followers. Students might perceive the follower as having potential for overtaking the leadership position, as IBM did in the personal computer market when it entered behind Apple and Tandy. This would call for an elaboration of the original model to account for the distinction between “vulnerable” and “strong” market leadership. Third, students might come up with a nonsense system- -one that does not hold together in any generalized sense. When students do not recognize this already, it is usually quite easy to identify flaws in the system. This, in turn, can lead to a discussion of the principles underlying the rules applying to the cases. Administrative Considerations The expert systems approach recommended here can be used in conjunction with lecture materials or in a class consisting entirely of case analysis. In the former situation, cases must be carefully chosen and be somewhat narrow in scope; otherwise, students will develop classification schemes including topics to which they have not yet been exposed in lectures. With well-constructed cases, however, the approach Developments in Business Simulation & Experiential Exercises, Volume 14, 1987 33 accommodates an excellent transition from lecture to case analysis. In a class focusing entirely on case analysis, students will require more guidance through the initial cases because they will not have the advantage of hearing about the instructor’s problem-solving framework. The same is true of conventional case analysis, however. The expert systems approach merely forces students to articulate the rules they are using to make decisions. In general, the instructor should demonstrate how the expert systems program works by presenting his/her analysis of a few simple cases which are similar in some ways but different in others. By observing such a demonstration, students will see how the expert systems approach "saves” prior decision rules and forces them to add consistent amendments to these rules. Expert systems are probably more effective when students solve cases in groups. In order to generate consensus regarding a set of decision rules, students must work through the cases and resolve their differences about how to categorize them. The resolution process will enable students to hear alternative viewpoints, perhaps ones they had not considered, and to understand the cases in greater depth. Hence, three or four students working in concert should be able to conceive a more complex and useful set of rules than a single student. THE EXPERT SYSTEMS APPROACH VERSUS CONVENTIONAL CASE ANALYSIS In many ways the use of expert systems to analyze cases is like conventional case analysis. Expert systems do, however, present the instructor with certain advantages. First, students receive instant feedback about their decisions because the expert system will signal them regarding any inconsistencies in their decision-making criteria. Through helpful hints or direct intervention the instructor can help students rethink their analyses so that their decision frameworks become workable. The typical time lag of several days for the instructor to read and grade written case assignments is thus eliminated. The. shortened turnaround for feedback may facilitate either more assignments or more diverse assignments, thereby enriching students’ experiences in the course. By choosing a variety of cases, the instructor can expose students to several seemingly unrelated cases which turn out to be similar in less than obvious ways. Thus, students will develop decision frameworks which have integrated many cases. With guidance, they should have a greater understanding of the interrelatedness of business decisions than occurs via classroom discussions and traditional written or oral case presentations. CONCLUSION The teaching approach presented in this paper utilizes an expert system development software package to help students become aware of the decision rules they use to solve cases. It provides a useful tool for helping students manage the transition between lecture material and case analysis. When used in conjunction with conventional cases, it adds intellectual structure to the case analysis process- - one that many students miss in their anxiety to produce a “solution.” It provides a useful framework for objectively evaluating cases. REFERENCES [1] Bettman, James R., An Information Processing Theory of Consumer Choice, (Reading, MA: Addison Wesley, 1979). [2] Buchanan, Bruce G. and Edward H. Shortliffe, Rule- Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project, (Reading, MA: Addison-Wesley, 1984). [3] Clancey, William J., “Use of MYCIN’s Rules for Tutoring,” International Journal of Man-Machine Studies, Vol. 11, 1984, pp. 25-49, as adapted in Bruce G. Buchanan and Edward H. Shortliffe (editors), Rule- Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project, (Reading, MA: Addison-Wesley, 1984, pp. 464-489). [4] Cyert, Richard M. and James G. March, A Behavioral Theory of the Firm, (Englewood Cliffs, NJ: Prentice- Hall, 1963). [5] Engel, James F., David T. Kollat, and Roger D. Blackwell, Consumer Behavior, (New York: Holt, Rinehart, and Winston, 1968). [6] Farley, John U. and L. Winston Ring, “‘Empirical’ Specification of a Buyer Behavior Model,” Journal of Marketing Research, Vol 11, No. 1, 1974, pp. 89-96. [7] Gensch, Dennis H., Advertising Planning: Mathematical Models in Advertising Media Planning, (Amsterdam: Elsevier, 1973). [8] Harmon, Paul and David King, Expert Systems; Artificial Intelligence in Business, (New York: John Wiley, 1985) [9] Howard, John A. and Jagdish N. Sheth, The Theory of Buyer Behavior, (New York: John Wiley, 1969). [10] Kotler, Philip, Marketing Decision Making: A Model building Approach, (New York: Holt, Rinehart, and Winston, 1971). [11] Nicosia, Francesco M., Consumer Decision Processes: Marketing and Advertising Implications, (Englewood Cliffs, NJ: Prentice-Hall, 1966). [12] Nicosia, Francesco M., “Advertising Management, Consumer Behavior, and Simulation,” Journal of Advertising Research, Vol. 8, No. 1, 1968, pp. 29-57. [13] Sexton, Donald E., Jr., “Microsimulation of Consumer Behavior,” in Jagdish N. Sheth (editor), Models of Buyer Behavior: Conceptual. Quantitative and Empirical, (New York: Harper & Row, 1974, pp. 88- 107). [14] Shortliffe, Edward H., A. Carlisle Scott, Miriam B. Bischoff, A. Bruce Campbell, William van Melle, and Charlotte D. Jacobs, “An Expert System for Oncology Protocol Management,” Seventh International Joint Conference on Artificial Intelligence Proceedings, 1981, as adapted in Bruce G. Buchanan and Edward H. Shortliffe, editors, Rulebased Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project, (Reading, MA: Addison- Wesley, 1984, pp. 653-665). Table of Contents Volume 14, 1987 Simulation Gaming as An Experimental Context for the Study of Multicriteria Decision Making How to Multiply Your Management Game Grading the Personnel In-Basket Exercise Simulation: The Players™ Perspective The Acute Susceptibility of Nominal Grouping to Negativity The Relative Value of the National ABSEL Meetings: An Analysis of Perceptions by Faculty and Deans A Survey of Behavioral Labs Used by American Business Schools A Structured Framework for Applying Conceptual Models to Problem Analysis: Hardening Up An Expert System Approach for Teaching Marketing Case Analysis From Theory to Practice: A Model for Teaching Beginning Advertising The Use of a Simple Forecasting Technique During an Interactive Computerized Business Game An Investigation of the Relationship Between Formal Planning and Simulation Team Performance and Satisfaction The Exponential Logarithm as an Algorithm for Business Simulations Learning macroeconomic Theory and Policy Analysis Via Microcomputer Simulation Laptop: A Principles of Marketing Simulation Teaching About Sampling in a Marketing Research Class Integrating Decision Support Systems and Business Games Demand Generation in a Service Industry Simulation: An Algorithmic Paradox ABSEL - At a Crossroads? Research on Predicting Performance in the Simulation An Expert System for Financial Planners "Developing Various Student Learning Abilities Via Writing, the Stock market Game, and Modified Marketplace Game in Beginning Macroeconomics" Professional Activity Reports: Getting back to Basics Œ A Preliminary Investigation "A Comparison of Performance, Attitudes, and behaviors of MBA and BBA Students in a Simulation Environment: A Preliminary Investigation" Decision Styles and Student Simulation Performance: A Replication Personal Power Strategies: An Experiential Exercise LTL I: A Trucking Simulation Game Total Enterprise Business Games: An Evaluation Can the Skill of Management be Taught? Developing Critical Thinking and Effective Communication Meaningful Notebooks and Enhanced Learning Using a Totally Menu-Driven Data base and Decision Support (DSS) Program Using a Computerized Menu-Driven Data Entry and fiWhat-Iffl Program as a Pedagogical Enhancement for Management Simulation Using a Joint Project Involving MBA Marketing Management and Undergraduate Marketing Research Students to Teach Marketing Research Testing the PAGE Technique: Results and Further Developments A Comparison of Student Perceptions with Accepted Expectations for Business Simulations "Conflict Management in Action: Verbal Strategies, Nonverbal behaviors and Conflict Styles" Dramatic Monologues as Surrogates for Experiential Learning Influencing One™s Superior: An Assessment and Exercise Personality Traits and Organizational Cultures: Two One-Hour Experiential Learning Exercises Computype: A Strategic Marketing Game Open System Simulations and Simulation Based Research "Teaching Forecasting to the Masses: Little Decision Support Training, But a lot of Answers" Goal Setting and Performance Evaluation with Different Starting Positions Œ The Modeling Dilemma The Gamesmanship of Pricing: Building Pricing Strategy Skills Through Spreadsheet Modeling Teaching MPR Experientially Through the Use of Lotus 1-2-3 Business Simulation Through Activities of a Manufacturing Company Simulated Stock Broker Teaching Employee Counseling Skills to Management Students: A Simulation Airline: A Strategic Management Simulation Developing Simulations and Experiential Exercises on the Personal Computer: Some Critical Issues A Powerful Tool Œ For What? An Integrative Systems Approach to Simulating Free Enterprise in a Small Business Course in a Business School Profits: The False Prophet The Use of Expert Systems to Develop Strategic Scenarios: An Experiment Using a Simulated Market Environment An Interdisciplinary Workshop: Spreadsheet Modeling and Case Analysis Techniques for Beginning Executive MBA Students An Innovative Approach to an O. D. Class Œ The Creative Interaction The Application of Spreadsheet Software Technology to Complete Taxpayer Elections Experiential Learning as the Cornerstone of an Industry Management Course "Identifying, Exercising and Enhancing Right Brain Skills in the Business Classroom" Utilizing Envisionary Technology in the Business Policy Course: An Experiential Pedagogy Business Simulation in the Policy Course: A Survey of American Assembly of Collegiate Schools of Business The Moving Van: Vehicle to Success of Disaster for the Two-Gender Work Force Oil and Gas Well Investment Analysis Using the Lotus 1-2-3 Decision Support System Team Cohesion Effects on Business Game Performance