1. Trade Show Effectiveness

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A Dynamic Model of BusinessTrade Show Effectiveness

Srinath GopalakrishnaGary L. Lilien

Penn State University

ISBM REPORT 3-1994

Institute for the Study of Business MarketsThe Pennsylvania State University

402 Business Administration BuildingUniversity Park, PA 16802-3004

(814) 863-2782 or (814) 863-0413 Fax


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This publication is available in alternative media onrequest.

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A DYNAMIC MODEL OF BUSINESS

TRADE SHOW EFFECTIVENESS

Srinath Gopalakrishna

Assistant Professor of MarketingThe Pennsylvania State University

University Park, PA 16802(814) 863-0687

Gary L. LilienDistinguished Research Professor of Management Science

The Pennsylvania State UniversityUniversity Park, PA 16802

(814) 863-2782

January 1994

The authors wish to thank Exhibit Surveys Inc., and the Institute for the Study of BusinessMarkets, Penn State for supporting this research.


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A DYNAMIC MODEL OF BUSINESS

TRADESHOW EFFECTIVENESS

Second only to expenditures on print advertising, trade shows are the largest component

of the business marketing communications mix, accounting for nearly 18% of the advertising and

promotion budget for industrial firms in the United States and about 25% in Europe (Business

Marketing 1990a;Schafer 1987). In the United States alone, trade shows are a $24 billionindustry with their overaileconomic impact exceeding $50 billion (Business Marketing 1991).The growing cost of personal contacts in the selling process suggest why business marketers seek

alternative methods for communicating with current and prospective customers. Considering a

specific example, CARR(1992) estimates that it takes an average of 3.7 sales call to close a saleat $292 per call, versus $185 to reach a prospect at a trade show followed by 0.8 sales calls to

close thereafter. These figures yield cost-to-close numbers of 3.7 x $292 or $1080 for sales calls

alone versus $185 plus 0.8 x $292 or $419 using trade shows (Trade Show Bureau 1992a). Over

50% of the attendees at trade shows have plans to buy one or more products exhibited, while over

75% 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 firmsand the totalattendance should all grow by over 50% compared to 1992 levels (Trade Show Bureau 1992b).

Despite the large expenditures and the wide use of trade shows, the limited research on

trade show effectiveness has focused on the show as an isolated event. Inother words, typicalresearch studies have evaluated each show individually, ignoring possible synergies, carryover

effects and the effects of audience overlap between shows. However, fumsexhibit at an averageof about 6 shows a year with 68% of firms participating in at least 4 shows annually (Trade Show

Bureau 1991). The presence of exhibiting tisat multiple shows raises the question of whetherand how the effects of previous show-activity caf~yover into the effectiveness of the currentshow. Specifically, we question whether a longer term, programmatic view o asequence of tradeshows can lead to different trade show budgeting decisions--in terms of both the level of


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expenditure and the right mix of expenditures on such show variables as booth space and pre-show promotion--than the prevalent, single show view.

Our focus on the dynamic, programmatic effects of trade shows is analogous to several of

the dynamic effects of advertising. Advertising contributes to the stock of goodwill for a product

or brand, which summarizes the effects of current and past advertising expenditures (NerloveandArrow 1962). A program of advertisements may have different effective levels of carryover for a

number of different reasons. For example, the ads may be placed in different media, reaching

different people, the ads may have different inter-exposure timespequency), SO that forgetting 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 shows may exist for

several similar reasons: (i) audiences at trade shows within a given industry can overlap

significantly, (ii) multiple exposures by the same purchase influencers through booth contact at

multiple shows may create learning effects similar to the repetition effect of advertising, and (iii)

by exhibiting at several shows, a firm learns how to do the job better, fine tuning key trade show

variables 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 decision

variables on performance within a dynamic framework. Previous research has suggested that a

key element of trade show effectiveness is a firms ability to attract the target audience to its booth

(Gopalakrishna and Lilien 1992). We develop a model that relates investments in previous trade

shows to effects at a current show. The model considers the impact of key decision variables

such as attention-getting techniques, booth space and pre-show promotion moderated by type of

show and size of the tirmon performance. We then examine the models normative implicationsand discuss the differences in the total trade show budget and the optimal split between various

elements when viewed in a dynamic rather than in a static framework. Next, we provide an

empirical validation of the model and show how it can be used for a variety of strategic and

tactical planning purposes. We conclude with an evaluation of our model and some suggestions

for future research.

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development of product specifications and supplier search. The cost-effectiveness of trade shows

diminishes as the buying process progresses toward evaluation and selection, but increases in

terms of providing feedback on product/service performance. Similarly, using the sellers

vocabulary, trade shows can be cost-effective in prospecting, opening a relationship, qualifying

prospects and even presenting the sales message (Churchill, Ford and Walker 1993, p. 42.)

At any time, a selling-firms universe of current and prospective customers will be

distributed among the phases in the first column of Figure 1, ranging from being unaware to

seeking purchase reassurance. A single trade show has varying effectiveness in helping this

process flow; multiple shows can have cumulative, synergistic effects. We would expect that (a)

the more the audience overlaps between two shows; (b) the more the product mix at the two

shows by the same exhibitor remains constant and (c) the closer the two shows are in time, the

greater the carryover effect of one show on the other. We will comment on these ideas furtherlater in the paper.

Trade Show Performance

While researchers have acknowledged the importance of trade shows in the business

marketing mix (Cavanaugh 1976; Moriarty and Spekman 1984); they have also acknowledged

that little systematic research about trade shows exists (Rosson and Seringhaus 1990).Descriptive studies have found that firms with complex, less frequently purchased products, withhigh sales levels and high customer concentration were more likely to participate in trade shows

(Lilien 1983),while better performing firms (as rated by the G.rmsthemselves) exhibited moreproducts, had more customers, greater sales volume, had specified show objectives and had used

fewer horizontal shows (Kerinand Cron 1987). Conceptual measures of performance likeaudience activity and audience quality (Mlizzi and Lipps 1984; Cavanaugh 1976) have beensuggested, as have operational measures such as (i) the proportion of target audience attracted to

a firms booth, (ii) the proportion of booth visitors contacted by the booth salesperson and (iii) the

proportion of contacts converted into leads (Gopalakrishna and Lilien 1992).

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Based on the above, we focus on

of a trade show and then relate it both to

carryover effect of past shows.

one particular dependent measure of the effectiveness

contemporaneous decision variables as well as to the

Consider a single show. All attendees at the show are not in the relevant target audience

for a given firm; rather they belong to one of two groups: those potentially interested in the

products exhibited by the firm (~0) and those not interested (1 -~0). For the firm, theattractiveness of participating in a show depends on E(S)*vo,the expected number of potentiallyinterested attendees at the show (where E(S) is the expected show size, measured in terms of the

total number of attendees). Though the target audience has a size given by (E(S)*Q,only afraction of that audience actually visits the f%ms booth. This proportion represents theattraction efficiency ( of the booth. The attraction efficiency indicates how effectively the

booth is able to attract members of its target audience. We define this performance index as:

(1)number of attendees from target audience who visited

the firms boothAttraction efficiency, 7= __________________u____________u________~~~~~~~~-~-~-~~~~~~~~~~~~~~

size of target audience

where target audience refers to the number of attendees at the show with an interest in the

products exhibited by the firm.Note that vin the above framework provides a foundation for other objectives for trade

show participation. For example, objectives like handling complaints from current

customers/dealers and generating quality sales leads from prospects both require the firm to first

attract visitors to the booth and then make effective contact with them (handle complaints, turn

visitors into leads, etc). These other objectives involve attention to issues like adequate boothstag,proper training of booth staff etc, but the first step--attracting the right booth visitors--isa necessary first step. We focus on vas the key dependent variable of interest in the modelstructure next.


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MODEL STRUCTURE

Following the discussion above, we conceptualize the share of a %mstarget audience thatis attracted to the firms booth at a given show as follows:

(2) q= f (at-show variables, pre-show variables, carryover f?omother shows)Previous research (Gopalakrishna and Lilien1992) has suggested that, viewed in a staticframework, booth attraction efficiency can largely be explained by what the firmdoes before theshow (largely pre-show promotion) plus what the firm does af the show (booth location, boothsize, use of attention getting techniques). The idea is that pre-show promotion predisposes

prospects to seek out the booth. At the show, the size, location and use of attention-getting

techniques help separate the firms booth fromthe clutter of the show environment.There is an extensive literature on modeling carryover effects (see Hanssens, Parsons and

Schultz 1990 and Saunders 1987for reviews). The most frequently used dynamic model form isthe Koyck model, in which some constant proportion, say 0, of the past effect of a marketingvariable (usually advertising) carries over into subsequent periods. When 8is near 0, pastmarketing activities have little effect on current effectiveness; for 8near 1, past activities n havedramatic current effects. Indeed, the term l/(l-0)is referred to as the long-run marketingexpenditure multiplier (Lilien,Kotler and Moor-thy 1992).

To incorporate the Koyck form into our trade show effectiveness model, we use the

following specification. We consider successive appearances by a specific firmat w shows(show i followed by show j) and model the carryover as follows:

qj =OtTi+(1 - vi)Cj + ,where

qj=booth attraction efficiency for a firm for show j;Cj =(static) communication effect of show j, specified later8= carryover effect f?omshow i to show j (0 58 1);t =duration (in months) between show i and show j;

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E=error term.

From equation (3),we see the share of a firms target audience at a given show that isattracted to the firms booth is composed of two components. The first represents the goodwill

carryover effect, i.e., a fraction of the share of target audience fromthe previous show is retainedin the current show. Note that the fraction retained is moderated by the inter-show duration (t) of

successive trade show appearances. Since 851, longer time periods between shows result inless carryover. The second component represents the impact of current trade show expenditures.

This impact is a fraction,Ci,of the total requirement adjusted for the carryover effect (1 -etqi).J

Cj should range between 0 and 1 for the model to be logically consistent. In

should allow for diminishing returns to marketing communications spending

and Schultz 1990; Little 1979).

addition, the model

(Hanssens, Parsons

Observe that the specification in (3) implies that the share of target audience qj,is aconcave function of the communication effect Cj.Eastlackand Rao (1986) and Rao and Miller(1975) for advertising and Lodishet al (1988) for salesforce effects provide evidence that isconsistent with S-shaped response while others (see Hanssens, Parsons and Schultz 1990) cite

concave response functions as dominant forms. However, researchers seem to agree that all

appropriate response functions show diminishing returns. In addition, data uncertainty and

methodological questions generally preclude finding the S-shape part of the curve econometrically(Broadbent 1984; Schultz and Block 1986). Even with an S-shaped response function, onlytheconcave part is relevant in the sense that the optimum effort level must lie in this region, leading

to our proposed model-form. Thus, we specifjlCj as follows:Cj=a j z ~3~ -exp -PlSj/Q } {1 -6exp(-PzPjlQj)} ;Sj >0

where

Aj=use of attention getting techniques in show j (0 if used; 1 otherwise)Mj =type of show the firm participated in (0 if vertical; 1 if horizontal)F =firm size (0 iflarge;1 if small)Sj =booth size measured in square feet at show j


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Pj=pre-show promotion expenditure by the firm relative to show jQj= size of firms target audience at show jcul,~2,cy3,PI,/32,and 6are parameters.

Note that in the specification above, we have included only the variables we will be able to

include in our empirical validation. In the discussion section, we review alternative specificationsthat would require other data.

Equation (4) includes two variables--show type and firm size--that we have not discussed.

These are situational variables rather than decision variables, but should be included in the model

to tune the effect

Show type:

market coverage.

of the decision variables.

Trade shows have been traditionally classified as vertical or horizontal based on

The nature of the show audience in terms of product interest, buying plans etc.

can be quite different in each case and has an important bearing on performance (Bertrand 1989;

Swandby 1984). Typically, a vertical show has a narrow focus and attracts specific types of

visitors. For example, most attendees at the Association of Operating Room Nurses (AORN)show are operating room nurses and exhibitors display products that are almost exclusively used

in the operating room. Horizontal shows attract a much wider audience and interest for any one

product category is typically low. For example, The National Design Engineering Show features

exhibitors displaying a wide range of mechanical components, electrical and electronic

components, plastics, elastomers, CAD/CAM systems etc.

F i r m si z e: Large firmsgenerally attend more trade shows than small firms (BusinessMarketing 1986; Trade Show Bureau 1988). The higher level of trade show spending usually

associated with larger fnms(Lilien1983) creates a situation where the small exhibitor oftenfeelsswallowed up at the show (Wall Street Journal Jan 1989). There is some empirical evidence

that in comparable situations, larger firms draw a larger share of the relevant target audience to

the booth than do smaller f7irms(Williams et al 1993).Returning to our model, note that Cj has an upper bound of 1and the model specification

implies a non-linear relationship between Cj and the decision variables (S, Pand A), with the8


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multiplicative form implicitly allowing for interactions. This specification is similar to that used by

Hanssens and Weitz (1980) to model the effectiveness of print advertisements. Since crll1, theeffect of attention getting techniques is scaled relative to that value. Similar effects hold for show

type and firm size. Note that a characteristic of this model is that a firmexhibiting at the showwith no pre-show promotion (P=O) can expect to achieve a communication effect no greater than

(l-6), where the fractionof the target audience attracted to the booth is [1 -s(l-@a~)].Some of the variables in the data set we will be using are continuous (booth space, pre-

show promotion) while others are discrete (attention-getting techniques, type of show, firm size).

Therefore, the modeling framework and the nature of the functional forms we employ are limited

to constructs that can be calibrated with these data (Lilien, Kotler and Moorthy1992). Finally,note that the linkage between (3) and (4) implies that qis concave in the same variables that giveC its concave form.

Appendix A gives some theoretical, normative implications of the model. We show that

the optimal total level of spending decreases as 0(the carryover effect), and the value of ~0(fromthe previous show) increases. Following our earlier discussion, we would expect 6to be higherwhen consecutive shows in the same industry are closer together, attract more similar audiences

and are characterized by a lower rate of new product introduction. We will return to the latter

point below; the main idea is that in an industry with many new products and short product life

cycles, the effects of marketing instrument is likely to be short-lived.

Appendix A also provides analytic results on the deployment of resources required to

maintain a steady state or equilibrium level of 7. Inother words, we consider what level ofcommunications spending in each show is needed to permit 7to remain constant fromone showto the next. Our results indicate that for a given steady state value of 7, communicationexpenditures decline as the carryover parameter 8increases, and the extent of that decline is lowerwhen the steady state value of 7is higher.

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EMPIRICAL ANALYSIS

Data collection

The data for this study involves 40firms that participated in 142 trade shows heldbetween 1985 and 1991. Exhibit Surveys Inc., an independent exhibit research firm, collected the

data in two phases. In the first phase, Exhibit Surveys mailed a questionnaire two to three weeks

aftereach show to a probability sample of about 1100 show attendees taken fromthe showregistration list. Firms used the results of that audience survey to answer a number of key

questions. For example, a firmcould infer the number of visitors at the show who were interestedin its products fromthe question What products were you interested in seeing at the show?Similarly, the question Which booths did you visit at the show? provided an estimate of how

many visitors were attracted to a specific firms booth. We used these two questions to estimate

the number of attendees who were interested in a firms products and who also visited the firms

booth, giving us our dependent measure, the booth attraction efficiency (q). Several otherquestions about the visitors influence in the buying decision, his/her actual plans to buy specific

products etc. were also included in the questionnaire. These surveys represent routine audience

profile measurements which Exhibit Surveys conducts for hundreds of industrial trade shows.

Exhibitor personnel were excluded from the sample and an incentive was included with a

personalized cover letter to maximize the return rate (approximately 33%).

In the second data collection phase, Exhibit Surveys requested specific information from a

number of Grmsthat participated in each show. The requested information asked for data on anumber of show-related tactical variables, such as square feet of booth space, expenditure on pre-show promotion, the use of attention-getting techniques, objectives for exhibiting at the show, the

products exhibited at the show etc. Table 1provides descriptive statistics for some of the key

variables. ____________________----Table 1 about here____________________----

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We linked the firm-specific data fromthe second data collection phase with the data onthe audience profile fromthe first phase. The trade shows included in the database represented avariety of industry categories such as telecommunications, computers, food processing, housing,

medical and health care, nursing, paint, radio/TV/cable, welding and robotics. Since we are

interested in the dynamics of trade show participation, we identified f%rmsthat exhibited at shows(within the industry) on a fairly regular basis. Since the timing of each show was known

(month/year), we were also able to calculate the duration between two consecutive show

appearances by a firm. In our database, this inter-show duration ranged from 1 month to 20

months. Overall, we had 267 useable data points, each representing a firms experience at a showat a specific time.

Consistent with our focus on booth attraction, our empirical analysis considers the

impersonal promotional variables that affect this process. We do not consider personal

promotional variables such as number of booth salespeople, type of training given to them etc.

that are most relevant for objectives such as lead generation.

We review the three decision variables that we include in our empirical analysis --

attention-getting technique, booth space and pre-show promotion. The two other variables (type

of show and f rmsize) which moderate the impact of the decision variables on performance havebeen discussed earlier. Note, however, that we use a dichotomous variable (large versus small) to

represent firm size; exploratory analysis with other ways to operationahzefirmsize gave resultsthat were less robust than this simple dichotomy.

Attention-getfingtechni ques: Firms employ various methods of attracting visitors to theirbooths. These methods include sampling, giveaways, product demonstrations etc. which attempt

to selectively attract the attention of interested visitors to make them enter the booth and ask for

more information (Hatch 1991). We classified firms based on whether they used some form of

attention-getting technique to attract visitors to their booths (A=O)as opposed to not using suchtechniques (A=l). We hypothesize a positive effect onv.

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Booth space: The attractiveness of a firms booth is directly related to booth size, other

things being equal. Swandby (1982) has suggested that the amount of booth space should be

decided in relation to the size of the potential audience. In order to achieve the same attraction

efficiency, a larger booth would be required if the size of the potential audience is larger.

Similarly, for a given size of the potenti al audience, an increase in booth size results in a higher

attraction efficiency. Thus, we consider booth size (i.e. square feet of space) relative to the size

of the target audience and hypothesize a positive effect of this variable on q. This is analogous toretail product movement related to shelf space (Bultez and Naert 1988).

Pre-show promotion: Many firms promote the fact that they will be exhibiting at a

particular show well in advance (Business Marketing1990b).Based on advance registration listsavailable from show organizers and their own customer/prospect lists, firmsuse direct mail andadvertising in trade magazines to contact customers and invite them to visit their booth at the

show. The impact of such expenditure on qclearly depends on the size of the target audience.For example, a given level of expenditure will have a smaller impact on qwhen the audience sizeis larger, other things being equal. Therefore, similar to the discussion on booth space, we

considered total expenditure on advertising and direct mail relative to the size of the target

audience. We hypothesize a positive effect of pre-show promotion on q.It is important to point out that better measurements of some variables would be desirable.

For example, attention-getting techniques might be treated as a continuous variable if data on

actual expenditures incurred by the firms could be obtained. Also, pre-show promotion

expenditures can be analyzed in finer detail assuming information on direct mail, telemarketing

and press advertising. However, we have had to tune our empirical analysis to the richness and

quality of the data that we have had available.

Results

We have data on 40firms that participated in various trade shows on a regular basis.Most of the firms (37 out of 40) exhibited at shows within a single industry category, while three

firms--AT&T, Hewlett Packard and IBM--participated in shows in multiple categories (for

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example, Hewlett Packard exhibits at computer shows as well as at medical shows). In such

multiple category cases, we tracked each firms show activity separately within a specific industry

group. Also, in order to retain a firm for analysis, each firm we kept exhibited at least three times

in total and at least once in a calendar year. We used this rule to focus on the carryover effect

(i.e., dynamics) of trade show participation, eliminating cases involving long gaps in trade show

appearances by a firm. Overall, the average number of show appearances by a firm we retained inthe database was 5.68.

We used a nonlinear least squares estimation procedure (PROCMODEL, SAS/ETSUsers Guide, Version 6, 1988) to estimate the model parameters. We report our results in Table

2. ____________________~~~~~~~~~~~~~~Table 2 about here____________________~~~~~~~~~~~~~

We see from that table that the model fit is good and that the carryover effect parameter 6,the attention parameter cul,the show parameter CY~ the firm size parameter cu3,the booth spaceparameter &and one parameter for pre-show promotion (6) are all significant at the .OOllevel orbetter. The other pre-show parameter (f12) is significant at the .OS level. Based on theseestimates, we can infer that attention-getting techniques such as sampling, giveaways and audio-

visual demonstrations (aI)provide an increase in booth attraction efficiency of about 10%compared to not using such techniques (all else being equal). Similarly, vertical shows provide an

incremental efficiency of about 35% when compared to horizontal shows cr2),while large firmsappear to have a nearly three-fold advantage over small firms (cY~),again, with all else constant.The carryover parameter (0)suggests that a duration of 6 months between successive showappearances provides a carryover effect of 68% {(0.9@}while an inter-show duration of 12months provides a 46% carryover effect. The pre-show promotion parameter (6) indicates that

with all other independent variables at their maximum values, a firm with no expenditure on pre-show promotion would be able to achieve a minimum communication effect of 44% (l-6) with

positive expenditures providing an increase in the communication effect.

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Estimation Issues

Serial Cowelation:The model as specified in equation (3) contains a lagged dependentvariable. If the error terms are autocorrelated, OLS estimates will be biased, inconsistent and

inefficient (Judge et al. 1988). For each fkm,we tested for serial correlation using the Durbin h-statistic, appropriate for models with a lagged dependent variable (Durbin1970; Judge et al.1988). First-order autocorrelations were not significant (at the .OSlevel) in any of the 47 cases.

u Z t i c o Z Z i n e a r i t y The square root of the ratio of the largest to the smallest eigenvalue ofthe correlation matrix of the set of independent variables, called the condit i on i ndex,provides a

single measure of the severity of multicollinearity; a value of 30 indicates a high degree of

multicollinearity (SAYSystem or Regr essi on 1986, p. 8 1). In our analysis, the condition index is 5.6, suggesting that multicollinearity is not a sign&antproblem for our data.

To test for model reliability, we estimated the model separately using randomly split halves

of the data set. We split the data by randomly assigning each of the 47 t ime seri es to one of two

groups (as stated earlier, each time series represents one firms appearances at trade shows within

an industry category). Thus, we had 24 time series representing 171 observations in one group

and 23 time series with 96 observations in the other. The estimation results using the split-halves

appear in Table 3. Based on a pair-wise t-test of equality of the coefficients (Morrison 1983,

pl6l-172),the hypotheses of no difference in the parameter estimates for the twohalves couldnot be rejected (p-values ranged fromSO to .80).Also, the significance levels for the parametersfor the two halves are similar to that obtained for the entire sample. Therefore, the reported

parameter values appear to be stable.____________________~~~~~~~~~~~~Table 3 about here

____________________~~~~~~~~~~----Following our discussion above, our estimation so far has assumed the same carryover

effect parameter for every firm. However, our sample of scomprises two qualitativelydifferent categories -- those in the telecommunications and computer industry and those in other

industries like nursing, paint and welding. The short life cycles of the telecommunications and

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computer industries might suggest a much lower carryover effect for shows in these two

industries than in the other industries. Inorder to run this analysis we decomposed 6as follows:K(5) e= @k) ; zk= 1 if show belongs to industry k,0 otherwise (ok51)

k=l

A model run with K= 2 yielded eO.27 in telecommunications/computers and HI.97in the otherindustries. These values are different Tomeach other at the .OO 1 level and are both0 at the .O 1 and .OO 1 level respectively. Thus, not only do carryovereffectssignifkant,they also appear to vary by industry.

different from

appear to be

While these results are exploratory--we have come across no other results on the dynamics

of trade show effects--they are significant for several reasons. First, while the factors we have

identified that affect trade show performance are qualitatively consistent with previous results

(Gopalakrishna and Lilien 1992),our new dynamic results strongly supportthe existence ofcarryover effects. As we established earlier, theoretically (and will conf m empirically below),the optimal level of trade show spending and the allocation of that spending is intimately tied to

the carryover effect. In addition, our exploratory work has also established significant,

explainable differences in the level of that carryover effect by industry. Thus, while there may be

no, single value for the carryover effect (a universal value for 0),it does seem likely that, in thespirit of meta-analysis, and with enough follow-up research, a good predictive equation relating 8to firm and industry variables could be established.

MODEL USES

The model we have developed is a response model: it provides a prediction of the level of

a firms performance--as measured by booth attraction efficiency--as a function of severalvariables under the f?rmscontrol. The model and the findings here have a number of uses formarketing planning and control. We outline three such uses here: a performance audit, trade-off

analysis and multiple show performance optimization.

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Performance Audit: The model can be used as a norm for performance evaluation. In the

same spirit as ADVISOR (Lilien 1979) and PIMS (Buzzell and Gale 1987),the models we havedeveloped here allow a fitmto provide inputs on key decision variables, and compare the firmsactual performance with the performance the model would predict. The model can thus serve as a

benchmark against which performance may be compared. For example, consider the information

about two actual firms that participated in various computer shows in our data set in Table 4.____________________--------------Table 4 about here____________________-~~~~~~~~~-~

Firm Iappears to be doing quite well at the most recent computer show it attended, exceeding itsmodel-predicted performance level while Firm 2 seems to fall below expectations. The use of the

model as an audit-tool focuses managements attention on sub-optimal or superior execution.

Firm 1 is doing things better than expected, so its execution can be used as a benchmark for its

performance at other shows. Firm 2needs to carefully analyze its execution to determine why itis not getting the expected return on its trade-show investment.

Assessment of trade-offs: A f$-m can use the model to determine the least costcombination of pre and at-show activities that would result in a given level of efficiency.

Consider, for example, a large firm that is planning to participate at an upcoming, vertical show.

Assume that this firm had achieved an attraction efficiency (70)of 40% at the previous show, sixmonths ago, and that management has set a goal of 60% efficiency for the upcoming show. The

decision variables under consideration are the use of attention-getting techniques (Yes/No), the

amount of booth space and the expenditure on pre-show promotion. Table 5 provides the

solution to the mixed-integer programming costminimizationproblem, obtained using a non-linear optimizing routine (Modeling and Optimization with GINO, Scientific Press, 1988). Note

that the total expenditure as well as the relative emphasis on the three decision variables changes

considerably tihenthe carryover effect is taken into account. In this illustration, we see threeeffects when the carryover phenomenon is considered: (i) the total expenditure drops from$79960 to $46780 (a 41%decrease), (ii) booth space becomes relatively more important in the

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mix -the expenditure ratio of booth space to pre-show promotion increases from0.93 to 1.58 and(iii) the use of an attention-getting technique is deleted fromthe mix. Thus, it appears thatconsidering carryover effect can have a major effect on optimal firm behavior.____________________~~~~~~~~~~~~~

Table 5 about here____________________~~~~~~~~~~----Multiple future showsand profi t maximization: Given estimates of the model parameters

and the firms objective of maximizing its discounted profit stream over an n-show planning

horizon, the model can be used to calculate optimal expenditure levels of booth space, pre-show

promotion and whether or not to use attention-getting techniques for each of the n shows. In this

situation, given the attraction efficiency for the most recent show (SO),the optimal efficiency levelfor each of the n future shows under consideration, is determined endogenously.

As an illustration, we report results for a situation where no budget constraint is imposed

(see Table 6). However, if resource limitations or planning/budgeting

specific constraints, these can be incorporated into the optimization

constraint on the total communications budget for each show).

considerations dictate any

problem (for example, a

Using the parameter estimates along with other relevant data Corn the empiricalapplication, we obtain the optimal spending levels for booth space and pre-show promotion. We

report comparative results for a time horizon that involves four future shows considered jointly by

a far sighted

independently.

manager versus a myopic perspective, where each show is considered

~

Table 6 about here____________________~~~~~~~~~~~~

A comparison of the single-show versus four-show solutions illustrates the impact of the

length of the time horizon on the results. First, the long term optimization yields a different

solution (and higher profits) than the myopic (single-show) case because of the carryover effect of

communications captured via the parameter 6in our model. The smaller the value of 6(i.e., thesmaller the carryover effect), the closer the multi-show solution will be to the single-show one.

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(If 6=0,the multi-show problem is reduced to a series of single-show problems.) Second, theoptimal expenditure, in the multi-show case, will be higher initially than in the single-show case

because the far sighted optimize takes advantage of the carryover effect; a higher level of

efficiency in the current show helps in the future.

Table 7 formally states the general optimization problem for the scenario involving N

futureshows and illustrates an application of the model for the four-show situation. The resultsshow that as the carryover parameter increases, a long-term perspective leads to a

correspondingly higher average profit per show compared to the myopic view. In our illustration,

where the numbers are drawn from information about a specific firm in the database, we see that

average per show profit can be higher by as much as 35%._-______Y_--~____-_----~Table 7 about here____________________--------------

For an ongoing application we suggest that the multi-show solution be used for planning

on a rolling basis: that is, as new data become available, the model parameter estimates and

consequently its recommendations should be updated periodically. The recommendation for the

first show can be used directly as an input for budgeting for the next show.

Some caveats are in order. These results are illustrative; as with other optimizing models,

the recommendations should be used as an input to planning and not replace managerial

experience and judgment. The models include only a limited number of variables and do not

explicitly include operational constraints that a firm might see. But these calculations do illustrate

the potential use of our results for auditing trade show performance and for allocating trade show

resources more cost-effectively.

CONCLUSIONS AND FUTURE RESEARCH

We have developed and empirically evaluated a dynamic model

effectiveness. While we developed the model from first principles--trying to

of trade show

make the model

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simple but logically consistent--we found that even this simple model form gives some important

theoretical insights about the level and allocation of trade show expenditures. Our empirical

results support the general structure of the model--that several key variables largely determine the

short term effectiveness of trade show expenditures and that the carryover effect of trade shows is

both significant and appears dependent on industry type.

Our empricalillustrations show how results such as these can be used for performanceaudits, for show resource trade-off analyses and for long term trade show planning. Although the

results are exploratory, they suggest

quantitative investigation.

There is good news and there is

that important managerial questions are now open to

bad news here. The good news is that we have been able

to conceptualize and empirically validate a model involving carryover effects of trade shows. The

bad news is the same--as with any fistreport of such a phenomenon, the fulluse of these findingsmust await further study, verification and validation. Our positioning this work as exploratory is

critical--we believe that we have identified and made some preliminary measurements of some

important phenomena. But use of these early results requires caution.

Consider some theoretical and empirical limitations of this work. On the theoretical side,

we have suggested a fairly simple analytical form. That form was developed with an eye toward

available data, included only measurable variables and, indeed, was further limited by the specific

measures we had available. While the model looks at the dynamics and synergies of trade shows,

it views trade shows in isolation fromthe rest of the business marketing mix. Selling activity,product quality, pricing, brand image etc. are ignored here: while we have investigated trade-offs

within the mix of trade show elements, we have not modeled the trade-offs across the mix. The

dependent measure--booth trtic flow effectiveness-- is no more than a means to the true end of aprofitable sales relationship with a satisfied customer. Competitive activities have been ignored as

well.

Empirically, we used commercially available data, collected for other purposes. We have

commented on the limitations of the measures above; our investigations were constrained by those

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limitations. For example, the firm-size variable, among others, should be refined and we might

investigate other transformations of the measures. In addition, while other firms besides ExhibitSurveys collect such data, no data that we are aware of are available either in the volume or the

quality one might like. Finally, none of our data traces the financial return on these trade show

investments; such a study must await future work.

We believe this study contributes to the understanding and

effective use of an under-studied element of the business marketing

to a more dynamic and

communications mix--the

trade show. Future research in this area should consider experimental work, varying the levels of

effort in the activities under study here. Such research would allow an experimental validation of

these quasi-experimental results. Future work should also look at the trade show as an

investment, studying those who come to the show and those who dont, tracking the level and

timing of resulting sales. Finally, the diverse nature of the industries represented here might better

be replaced by studies of a larger number of firms in fewer industries to focus on the industry-specific nature of the carryover effect. On net, we hope that we have contributed in some small

way to the understanding and more dynamic and effective use of an under-studied element of the

business marketing mix.

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Table 1

DESCRIPTIVE STATISTICS OF KEYTRADE SHOW VARMBLES_ _ _ _ _ _ _ ~ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ . ~ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ - ~ ~ ~ ~Variable

MeanStd. Dev.

Range______________________________________________________________________________~~__~~~~~Target audience 12065 14208 508 -87959Booth space (sq. A.) 2541 2167 100 -15000Pre-show promotion ( ) 4303 10187 0 -85000Attraction efficiency 0.488 0.189 0.088 -0.866Attention-getting techniques 15%*Size of Firm 61%@Type of Show 88%

*Proportion of firms employing the variable@Proportion of firms classified as large (following investigation of sdeslshare histogram) Proportion of shows classified as vertical

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Table 2

PARAMETER ESTIMATES: EFFICIENCY OFATTRACTINGTARGET AUDIENCE TO THE BOOTH

_________________________________________~________________~~~~~~-~~~Variable Estimate____________________________________________________________~~~-~~-~~~Carryover effect (8)Attention (al)Showtype(cu2)Firmsize (cy3)Booth Space (/31)Pre-show promotion (a)Pre-show promotion(p2)

0.937*(0.012)0.919*(0.099)0.741*(0.158)0.373 *(0.065)10.725*(3.006)0.558*(0.056)0.068**(0.034)

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Table 3

SPLIT-HALF PARAMETER ESTIMATES SHOWING

RELIABILITY OF ESTIMATION PROCEDURE

Carryover effect (0) 0.930* 0.935*(0.016) (0.022)

Attention (cu1) 0.899* 0.987*(0.117) (0.134)

Showtype(a2) 0.711* 0.732*(0.196) (0.188)Firmsize (0~3) 0.334 0.369*(0.097)

Booth Space (/31) 6.679** (0.071)33.750(2.053) (31.795)

Pre-show promotion (6) 0.514* 0.541*(0.075) (0.082)

Pre-show promotion(fl2) 0.226*** 0.021(0.130) (0.030)-------_____________~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~---~~~-------------------

Number of Observations 171 96

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Table 4

ILLUSTRATIVE USE OF THE MODEL FOR

PERFORMANCE AUDIT FOR TWO FIRMS IN THE DATA BASE

Firm1 Firm 2

Present Show Educom Seybold

Month/Year October 199 September 1989

Previous show attended Educom PC Expo

Month/Year October 1990 June 1989

Inter-show duration (months) 12 3

Attraction efficiencyat previous show (~0) 80.3% 72.3%Current show details:Target audienceAttention-getting techniquesFirmsizeShowtypeBooth Space (square feet)Pre-show promotion

1250 15076

Yes No

Large Large

Vertical Vertical1200 600

$0 $400

Expected attraction efficiency (qexp) 64.7% 65.2%Actual attraction efficiency (Tact) 83.2% 47.4%Interpretation: Th i s table showshatfintt I per ormsbetter han expected ( A c t u a l Ekpectegwhi le the situation is reversedforjhm 2.

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Table 5

ILLUSTRATIVE IMPACT OF CARRYOVER EFFECT ON OPTIMAL, RESOURCE

ALLOCATION USING DATA DRAWN FROM ONE FIRM IN THE DATA BASE

Previous attraction efficiency (~0):40%Inter-show duration (t): 6 monthsTarget audience size (Q): 5000Target attraction efficiency for next show (9):60%Cost per square foot of booth space (k2): $30Cost of using attention-getting techniques (kl): $10,000

Solution to mixed integer programming cost-minimization problem:

Variable

Optimal Solution__________c________________________u____~~~~~~~~~~~~~-~~~~~~~~~~~$20 c o s t % of total e=O.93 cost % of total

Booth Space (sq.R.) 1124 $33720 42.2 956 $28680 61.3Pre-show promotion ($) 36240 $36240 45.3 18100 $18100 38.7

Attention-getting technique Yes $10000 12.5 No - ------_--- __W___ -_------- _W____Total Expenditure $79960 100 $46780 100--_------ __-_-- --------- ------Problem Formulation:

Find (A, S, P} to

MinimizeZ=klA+k2S+Psubject to:

7=et70 +(1 -&O)alAf1 -exp(-PlS/Q)} (1 -6exp(-p2PIQ))A=Oor

S,P 20Values for k 1,k2,q,~0,t and Q are indicated above.Values for 8, cul,P1, /32and 6are as reported in Table 2.

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31/39


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Figure 1THE BUYING AND SELLING PROCESS AND THE COMMUNICATIONS MIX

New Customer/Prospect Buying Phase

(Robinson, Faris Wind. 19671

A2. Developing product

specifications

J3. Search and qualification

of suppliers

&4. Evaluation

15. Supplier selection

46. Purchase feedback

Key SellerCommunications Objectives and Tasks

(Churchill, Ford, Walker, 1993)

CommunicationsObiectives T a s k Senerate awarenessFeature comprehension

Lead generation

Performance comprehension

Negotiation of terms/

Offer Customization

Reassurance

Prospecting

Opening relationship,qualifying prospect

Qualifying prospect

Presenting sales message

Closing sale

Account Service

Relative CommunicationEffectiveness

(Kotler, 1991)

W High

Advertising \Trade PersonalShow Selling

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APPENDIX A: SOME ANALYTICAL RESULTS ON

OPTIMAL TRADE SHOW EXPENDITURES

For ease of exposition, we consider the two continuous variables, S and P in equation (4),and sett=lin equation (3) without any loss of generality. We restate the model as:(Al) ~=8770+(1-8770)Ww(- S/Q))(l-~qW '~QNThe objective function is:

(A2) Maxn=gq-cS-Pwhere g translates qinto sales revenue (g is assumed constant) and c is the cost per unit of boothspace

First order conditions:

(~3) adas=gaqi as c= 0(A4) &r/aP= g &$aP- =0Equations (A3) and (A4) imply

(5) s = c pFrom equation (Al)

(A6) arl/aS=(1-orlo) {l-sexp -P2PIQ } P1jQ exp -P1S/Qa =Who>{l-exp -P1SjQ }G P2jQ exp -P2PjQ

Substituting (A6) and (A7) into (AS), we get

(A7a) {l-sexp -P2PIQ P1jQ exp -P1S/Q = c ~-~~~ -P~S/Q ~G P~/Q ~XP -P~P~Q(A'W l-Gexp -P2PjQ Plexp -P1S/Q =c l-exp -P1SIQ GP2exp -P2PjQwhich on simplification, yields

(A8) Pl(exp(P2PjQ) -S> = c6P2fexp(P@Q) -11Equation (AS) shows the relationship between P and S at optimality. Given a budget B, i.e.,

(A9) cS+P=Bequations (A8)and (A9)may be solved to get S* and P*.If the budget B is not specified, we find S* from equations (A4) and (A7)

(AlOa)gk(l-@TO)U-exp(-Pls/Q >G P2jQ exp -P2PjQ =29


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At a higher steady state efficiency level 72(say ~2 = k YQ;k>l),the communications effect isC2= (l-e)72 /(I-eT2) = (l-e) kql/(l-ekql). Therefore,(A22) C2Kl= k (l-8771) /(l-8k7Q)From equation (A22)iI(C+ lM =k (k- l)ql/(l-8kq 2 which is positive since k >1.

Result 2:As increases, increases.

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Documents

1. Trade Show Effectiveness