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A DYNAMIC MODEL OF BUSINESSTRADE SHOW EFFECTIVENESSSecond only to expenditures on print advertising, trade shows are the largestcomponentof the business marketing communications mix, accounting for nearly 18% of theadvertising and

promotion budget for industrial firms in the United States and about 25% in Europe(BusinessMarketing 1990a; Schafer 1987). In the United States alone, trade shows are a $24

billionindustry with their overail economic impact exceeding $50 billion (BusinessMarketing 1991).The growing cost of personal contacts in the selling process suggest why businessmarketers seekalternative methods for communicating with current and prospective customers.Considering aspecific example, CARR (1992) estimates that it takes an average of 3.7 sales call toclose a saleat $292 per call, versus $185 to reach a prospect at a trade show followed by 0.8 salescalls toclose thereafter. These figures yield cost-to-close numbers of 3.7 x $292 or $1080 forsales callsalone versus $185 plus 0.8 x $292 or $419 using trade shows (Trade Show Bureau1992a). Over50% of the attendees at trade shows have plans to buy one or more products exhibited,while over75% influence the buying process for those products. Industry projections indicate that

by 1997,the number of net square feet of exhibit space, the number of exhibiting firms and thetotalattendance should all grow by over 50% compared to 1992 levels (Trade ShowBureau 1992b).Despite the large expenditures and the wide use of trade shows, the limited researchon

trade show effectiveness has focused on the show as an isolated event. In other words,typicalresearch studies have evaluated each show individually, ignoring possible synergies,carryovereffects and the effects of audience overlap between shows. However, fums exhibit at anaverageof about 6 shows a year with 68% of firms participating in at least 4 shows annually(Trade ShowBureau 1991). The presence of exhibiting tis at multiple shows raises the question ofwhether

and how the effects of previous show-activity caf~y over into the effectiveness of thecurrent

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show. Specifically, we question whether a longer term, programmatic view of asequence of tradeshows can lead to different trade show budgeting decisions--in terms of both the levelofexpenditure and the right mix of expenditures on such show variables as booth spaceand preshow

promotion--than the prevalent, single show view.Our focus on the dynamic, programmatic effects of trade shows is analogous toseveral ofthe dynamic effects of advertising. Advertising contributes to the stock of goodwill fora productor brand, which summarizes the effects of current and past advertising expenditures(Nerlove andArrow 1962). A program of advertisements may have different effective levels ofcarryover for anumber of different reasons. For example, the ads may be placed in different media,reachingdifferent people, the ads may have different inter-exposure times pequency), SO thatforgetting ordecay takes place, or the ads may have different levels of intensity and/or copy-effectiveness,leading to different levels of response. Carryover effects from previous trade showsmay exist for .several similar reasons: (i) audiences at trade shows within a given industry canoverlapsignificantly, (ii) multiple exposures by the same purchase influencers through boothcontact atmultiple shows may create learning effects similar to the repetition effect ofadvertising, and (iii)

by exhibiting at several shows, a firm learns how to do the job better, fine tuning keytrade showvariables in ways that lead to better performance in subsequent shows.In this paper, we propose an approach to assess the impact of key trade show decisionvariables on performance within a dynamic framework. Previous research hassuggested that akey element of trade show effectiveness is a firms ability to attract the target audienceto its booth(Gopalakrishna and Lilien 1992). We develop a model that relates investments in

previous tradeshows to effects at a current show. The model considers the impact of key decisionvariablessuch as attention-getting techniques, booth space and pre-show promotion moderated

by type ofshow and size of the tirm on performance. We then examine the models normativeimplications

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and discuss the differences in the total trade show budget and the optimal split between variouselements when viewed in a dynamic rather than in a static framework. Next, we

provide anempirical validation of the model and show how it can be used for a variety ofstrategic andtactical planning purposes. We conclude with an evaluation of our model and somesuggestionsfor future research.