A simulation model for industrial marketing

OMEGA Int. J. of Mgmt Sci., Vol. 20, No. 3, pp. 323-335, 1992 Printed in Great Britain

0305-0483/92 55.00 + 0.00 Pergamon Press Ltd

A Simulation Model for Industrial Marketing B ARINZE J BURTON Drexel University, Philadelphia, Pennsylvania, USA (Received May 1990; in revised form October 199I) Developing an effective marketing mix is an important task for product planners seeking to gain competitive advantage in industrial markets. In these markets, product planning is made complex due to inadequate data sources, stochastic behavior, and the peculiar response of industrial markets to marketing instruments. By employing a simulation model as the heart of a marketing decision support system (MKDSS) it is possible to model the stochastic elements of the marketing mix, the interactions between marketing instruments, and competitive effects, to support marketing decision-making processes. This paper describes such a simulation model for aiding product planners in developing the marketing mix. The model utilizes Monte Carlo simulation in representing market dynamics, comparative marketing efforts, and competitive actions, in order to assess the effectiveness of combinations of marketing actions, that is, marketing policies. It is therefore viewed us a tool for improving decision-making effectiveness, and the basis of a marketing decision support system (MKDSS) for marketing managers. The methodology for applying the decision model is outlined, together with an illustrative example of its use, based on data from case study in a British firm.

Key words--industrial marketing, Monte Carlo simulation, marketing mix decisions, decision support systems

1. INTRODUCTION

ment, and the solution of these models using analytical techniques [1, 23]. THERE HAS BEEN AN UPSURGE in the Industrial markets are characterized by the application of sophisticated computer-based sale of products to business customers, who approaches to marketing research and decision often use them in turn to provide further goods making in recent years. As greater amounts of or services to consumers. The industrial market information have become available from store therefore consists primarily (but not exclusively) audits, etc., the challenge has been for product of organizational buyers [8, 19]. Some authors planners to make more effective use of this [20, 28] have described the comparative underinformation through computer models [5, 19]. development of methodologies in the area of There has been a wealth of methods and industrial marketing, and also refer to the techniques developed to handle product relatively small budgets allocated to the adverintroduction and marketing mix management in tising of industrial products. consumer markets. Such models provide The structure, activities, and dynamics of marketing managers with a powerful variety of industrial markets are recognized as being tools for these tasks. In the form of marketing inherently different from those of consumer decision support systems (MKDSS), managers markets [8, 18, 20,28]. As such, they require may employ these tools for collecting and specialized modeling techniques to handle assembling data, model-building and manage- differences in all components of the marketing OME 20/3--E

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enough to represent economic assumptions and managerial judgments, and which can handle the stochastic elements of the industrial marketing-mix problem. The simulation model proposed handles multiple marketing variables and captures the relationships between different marketing-mix variables. Simulation is used to handle both the complexity of the multiple submodels and their probabilistic aspects. This approach goes further than models which focus on single variables (e.g. [20]), and provides equal or greater flexibility than others which handle multiple variables, by dealing explicitly with competitive effects in the individual marketingmix elements. Furthermore, it provides the ability to cope with dynamic market size assumptions. The rest of this paper is organized as follows, Section 2 outlines major characteristics of industrial markets, while the basic simulation model and its submodels are discussed in Section 3. Data collection and model calibration are also described. Section 4 then outlines an example of the use of the simulation-based MKDSS. Section 5 discusses the model's comparative benefits and raises important implementation issues. Section 6 concludes the discussion and suggests possible areas for further research.

buyers or indirectly, via a pull-through effect, by focusing on the end-users of the industrial product [8]. Also, in many industrial marketing settings, the number of customers is so small that individual campaigns for each customer may replace the traditional marketing effort. While phenomena such as economies of scale, threshold effects, and interaction effects have been identified [20], they clearly require careful study in real-world modeling. Much industrial marketing is characterized by direct marketing, but may also involve distribution through agents or distributors. An added complication is that markets regarded as industrial may often be a mix of the consumer and the industrial, requiring a specialized marketing-mix. Conjoint analysis is often advocated for industrial product design and the selection of product features [4, 18, 19]. Another method [27] develops new industrial products using inputs from the product's leading users. In proposing the DESIGNOR model, Choffray and Lilien [6] point out the unsuitability of approaches from the consumer area and identify three relevant dimensions for industrial products, namely boundary, range, and discrete specification dimensions for industrial products to satisfy. One school of thought [18] asserts that most marketing models ignore competitive response 2. C H A R A C T E R I S T I C S OF INDUSTRIAL even though it is an important parameter in MARKETS marketing-mix decisions. A further framework [10] in addition, documents the extent to which There are several characteristics differentiat- structural variables determine the type of coming industrial markets from consumer markets, petition. Competitive response is subsequently necessitating modifications to modeling deemed important in deriving the marketing approaches used to support marketing-mix mix and may be handled in a variety of ways. decisions. For example, organizational buyers Much information is typically available to are generally more price-sensitive than pur- (consumer) market researchers in the form of chasers of consumer goods, and the purchasing consumer-purchase-panel data, e.g. MRCA, process usually features multiple participants and from retail store audit firms such as AC with different responses to selling and advertis- Nielsen [5]. These provide information on sales, ing efforts. The longer-term relationships market share, availability, etc., for different between buyers and sellers in industrial markets, brands. Although some statistical problems and the value-added components of many have been noted [19], e.g. smoothing of industrial products add further dimensions to responses, the data available in such markets is price setting. Furthermore, competitive bidding very reliable for many uses. In industrial [8] and direct pricing negotiations [18] charac- markets, such information is often partially or terize the industrial setting, wholly absent due to less reliable monitoring With respect to communication, advertising mechanisms. Other reasons for the dearth of channels are typically very specialized, and industrial marketing data include real and advertising is seen as playing a smaller role [20]. imagined concerns about confidentiality and the Advertising may be aimed directly at industrial effect of data disclosure on competitive position

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[28]. Market estimates often must be created from surveys, many of which are unreliable, incomplete or both. Data calibration for MKDSS used to market industrial products must therefore be more painstakingly carried out, and a means provided to support both subjective judgments [7] and a "decision calculus" [21].

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model does not incorporate competitive effects. An approach targeted specifically at industrial marketers however, is described in [2], and is an adaptation of Hauser and Shugans' [14] "Defender" consumer model (see also [13]) to the industrial marketplace. This model involves the transformation of product characteristics into a consumer "perceptual space" which allows a modeling of competitor market share. Both the Defender model and the subsequent 3. THE SIMULATION MODEL AND RELATED APPROACHES adaptation [2] focus on defensive strategies for new products, but may also be used to defend A number of modeling approaches have been market share from existing competitors. developed r.o improve the decision-making This paper outlines a simulation model for effectiveness of marketing managers in both supporting industrial marketing decisions consumer and industrial marketing contexts, believed to incorporate a wider range of capaFor example, several authors [21,23,25] bilities than the above-mentioned approaches. propose models based on the multiplicative First, it provides a means for dealing with derivation of product sales through "effect indi- multiple competitors in an industrial market. ces". Little's BRANDAID consumer model [21] While it is based on the multiplicative "effect represents a well-known example of this type of indices" mentioned earlier, significant extenmodel, in which the effect indices represent sions have been made to the basic model. steady-state or reference values of the marketing Specifically, it incorporates stochastic assumpmix variables. These variables may then be tions into the model to facilitate the use of modified to reflect specific marketing actions, managerial experience and judgments, unlike a and their impact on sales and profits, majority of the discussed approaches. A further Other models (e.g. [1 l] and [20]) focus on a distinction from multiplicative approaches is the single marketing instrument. The former work handling of competition in each submodel or [ll] in particular, enables the testing of varying element of the marketing mix, rather than levels of advertising expenditure on product sales, aggregating it, as is typically done. This enables A different approach is to estimate potential a more precise modeling of competitor response success of a new industrial product (see [6]) with in the elements of the mix in which they occur. the model's output representing the fraction of It further introduces the notion of modeling the market that would find the product comparative effort (us vs our competitors), "acceptable", based on estimates of consumer representing a more realistic and accurate awareness, market segmentation and product approach. In addition, it is able to handle the characteristics. Further work [7] attempts to real-world phenomenon of market expansion or provide decision support for new product contraction over the period simulated. While launches in various markets using diffusion this comparative modeling approach requires rates for these markets. They demonstrate that considerable data amounts for calibration, it these rates may be empirically derived through avoids the unrealism involved in grouping all of surveys of the R&D process, market introduc- a competitor's marketing actions into one tion strategies, and product penetration for variable or focusing on one instrument to the companies in the market segment over a histori- exclusion of all others. cal time period. These diffusion rates may then be used to estimate the impact of a new product. 3.1 The model structure A method for assessing the risks of financial The simulation model and flowchart are investment in firms using the expected value of illustrated in Figs 1 and 2, respectively. The financial returns is described in [15]. The "key objective is to maximize profits as shown; the input factors" (e.g. operating and fixed costs, outputs are the profits and the associated useful life of facilities, etc.) employed (p. 29) are marketing mix. Controllable inputs to the at a somewhat higher level than micro-market- model are price, advertising, product (features) ing instruments focused upon here, and the and selling effort, while the uncontrollable

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i;ngntrollable innuts: Price Communication Selling Product Features

Obiectlve: Derive Profits

Outout:

Maximize Profit subject to:

Profits Market Shares

Q(t) : f[X(t), H(t), Q(t-1), E(t)]

t

Uncontrollable inputs: Competition mix Seasonslity Environment Fig. 1. The market simulation model.

inputs comprise competitor responses and environmental effects e.g. demand variability, which is employed in this model. The general form of the model is illustrated in Appendix 1. The product sales rate is represented as a reference value derived from recent sales and marketing activities. This reference or base value, Q(0), is modified in a multiplicative process by effect indices, representing marketing activities of interest. This results in an estimated future sales rate (i.e. Q(t)--the sales rate at time t). The product sales rate model is shown in Appendix 2. Given K effect indices for influences such as price, advertising, etc., the index e(k~(t) (k = 1,2. . . . . K) represents the function of the ratio of the firm's efforts and the competitor's efforts in that period (t), divided by the same ratio at the base period, t = 0. For example, if initial prices are 110 and 90 for the firm and one competitor, and the corresponding prices in period t are 120 and 100, then the price effect is the appropriatefunctionevaluatedat(120/100)/ (110/90). The development of the function used is based on an appropriate curve fitting for the effort ratios (a quadratic one was used in our example), 3.1.1 Submodelstructure. The submodels representing the contributions of the various influences (price, advertising, etc.) on sales have similar forms, as illustrated in the submodel structure in Appendix 3. As shown in the

appendix, each submodel represents X~)(t) as the firm's effort in influence k at time t relative to that of the total industry. The effect index for a marketing influence e(k)(t) depends therefore on x(k)(t), using a fitted function G(k)( ) which reflects, among other characteristics diminishing returns from market influences. In addition, a memory effect, q~, is also used within the model to produce a carryover in a particular influence from a prior sales period, e.g. remembered advertising. This memory effect (a constant) may be set either by managerial estimation or fitted using historical company or industry data (e.g. see [21]). The following subsections identify unique characteristics of each of the submodels. 3.1.2 Advertising submodel. Advertising spending for the base period is used to derive the effect index for advertising spending. For the industrial marketing context, since industrial buyers are more involved in the purchase decision and less prone to buyer brand loyalty than consumers, an advertising exposure and forgetting model has been utilized, with no carryover effect per period. The model assumes that this rate of spending maintains sales at the current level, given no changes in marketing effort (and assuming no changes in the competition's efforts). The curve describing the sales response to advertising is illustrated by Fig. 3, and shows diminishing returns from increasing advertising. The effect index for advertising is as shown in the general

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Calibration

[ I

set policy for T periods

I I

1 number of runs [ desired

-- N

< /

Slmulatscompetitor's actions in period t

I

L [ -

calculate results from

period t

Lt

=t + 1

I ]

No

I output policy results I

I

I

n=n +1

[

No

State slat. ) results Fig. 2:. The market simulation flowchart,

form in (4) with ~ = 0, assuming no memory effect, The calibration of the sales response curve to advertising shown in Fig. 3 is discussed further on, As relative advertising spending increases, ~XI2~(t) denotes effort in the price influence, k takes values 1, 3, and 4 for advertising, product development and selling influences respectively (see appendix),

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sales also increase until a point of diminishing returns is reached. 3.1.3 Price submodel. The model in addition, assumes an effect index that reflects changes in the price of the product. The price-index submodel corresponds to the general form in (4) with ~b = 0, implying no memory effect. However, since X'(2)(/) I is not known for t > 0, it will be approximated by X~2~(t - 1). A quadratic function is used to generate a curve passing through a set of points representing a range of product prices and corresponding sales estimates. This curve is illustrated in Fig. 4. 3.1.4 Product development submodeL The next component of the model handles product enhancements or upgrades. It assumes the product is an existing one, and not a new product. The effectiveness index for product features is derived from a comparison of competing products by their features, using attached weights (representing importance). Both the weights attached to features and product scores on these features may be derived by means of managerial judgment (e.g. through the Delphi method) or empirically, from market surveys. The product features index is modified through the implementation of new features. The submodel again corresponds to the general form in (4) with ~ = 0, implying no memory effect. The firm's effort with regard to product features, X"3)(/), is calculated by scoring each product feature and multiplying by associated weights to produce an overall score (see Appendix 4). The model may, in addition, be calibrated for a diffusion effect, i.e. the time lag between the implementation of new product features and the sales response. The sales response curve for product features is fitted by the quadratic, and shows diminishing returns as features are added. This is illustrated in Fig. 3. 3.1.5 Selling submodel. The submodel described below aids the determination of salesforce size. In industrial markets, selling effort is often determined as a fixed percentage of sales, or by the breakdown method (dividing sales estimates by average sales per salesperson) to derive the required salesforce [18, 19]. Both these methods have limitations e.g., viewing sales effort as a result of sales, among others. The salesforce allocation decision is modeled using an effect index, e(t). Unlike the other effect indices, an exposure and forgetting model is viewed as unsuitable in the industrial

Arinze, Burton--Industrial Marketing

328 Solos (unite)

current sales

~

i

<1

> Relative Market Instrument Effort (Ratio)

Fig. 3. Sales response to advertising, selling and product features instruments. Sales

(unne)

\

current sales

I I I I

<1

1

n

>1

Relative Price (Ratio)

Fig. 4. Sales response to price.

marketing context, due in part, to the earlier described personalized nature of the selling effort. The effect index is as the general form in (4) with 0 < ~b < 1, i.e. assuming a memory effect for remembered selling effort, a realistic assumption given the personalized nature of the industrial marketing context, The shape of the curve generated by the response function demonstrates diminishing returns for increased selling effort and is shown in Fig. 3. 3,1.6 Model calibration. Calibration for each

of the four submodels is done in a similar fashion. For a specific submodel, given the base period (t = 0) values for the firm's and the competitor's efforts, the base ratio (firm's efforts/competitor's efforts) is determined, and the known sales volume is noted. Two other values of the firm's efforts are specified and the corresponding sales response is estimated by the user, with the competitor's effort unchanged. These three ratios are divided by the base ratio, and then these three points (effort ratio/base ratio, sales) are used to fit a quadratic function. 2 During simulation, the ratio of the firm's effort and the competitor's effort in period t are 2While a quadratic fit was used here, other functional representations could have been used in a similar divided by the base ratio, and this value is manner, entered into the quadratic effort function.

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3.2 Competitive effects and a modeling procedure

4. A NUMERICAL EXAMPLE

Each of the four previous submodels incorporates the element of competitive response, This stochastic element is handled using probability estimates for specific competitive actions. Use of the simulation model will consist of a six-step procedure, as follows:

We now proceed to demonstrate the use of the industrial-product simulation model as a tool for decision support. The model is coded in Pascal, illustrating its use for the support of a single product in a duopolistic industrial market. Much of the model dynamics represented are based on a case study carried out in a British software firm. The indices of effectiveness, each representing a submodel are evaluated in sequence, and used multiplicatively in the central equation (2). Each of the submodels apply stochastic assumptions for competitive effects. Model inputs include price, advertising, selling and (product) features while its outputs are sales, revenues, and profits.

(1) Calibration of the model with " h a r d " data, which includes the current and historic levels of the marketing mix variables for all the competitor companies, and estimates of the total market size (our share plus the shares of competitors). This information m a y b e automatically downloaded from a corporate resource and/or directly entered by the user.

4.1 Evaluating the price effect index

(2) Deriving the effect functions for each element of the marketing mix. (3) Calibration of the model with "soft" data, or the probability estimates of likely competitor responses to our market actions (or inaction), for each element of the marketing mix. Initially, these will be directly entered or generated by the system, but can then be permanently stored in the MKDSS data base and periodically updated as necessary. (4) The model may then be run with the initial data set. (5) The decision maker will then typically adjust and readjust the model calibration through several iterations, going back to and through step 3. (6) Various policies may then be modeled through changes in sets of marketing mix elements and corresponding effect indices to determine an appropriate marketing strategy. With this technique, the consequences of both specific policies and the effects of competition may be modeled. This process is now illustrated in the example described next.

The market size is assumed to be 5000 units, and increasing at 5% yearly in the example described. The current price of the product is $1200, while that of the competitor is $1100. The calibration of price elasticity is illustrated in Fig. 4, and may in practice, be derived empirically (historical or industry-based), or by the subjective judgments of experienced managers. Further, as shown in Table 1, probabilities for different competitor responses are illustrated for positive, negative or zero price movements of the product. The price effect curve charts the price of our product (relative to that of the competition) against sales. For example, assuming reduced price is matched by the competitor, the comparative price and corresponding sales remain unchanged, as the price change is effectively nullified. It is further assumed that no more than four price changes are allowed each year (i.e. quarterly), in increments that are multiples

Table 1. Calibrationfor the price submodel Acti°n~ Estimatedcompetitorresponseb Raise price p(Match) 0.2 p(Nothing) 0.8 Lowerprice p(Match) 0.5 Unchanged price

p(Nothing) p(Match) p(Lower) p(Higher)

0.5 0.7 0.2 0. I

aCurrent product price is $1200: initial yearly sales are

23oounits.

bCurrent competitor price is $1100: initial yearly sales are 2700 units.

Market growthis 5% yearly.

Arinze, Burton--Industrial Marketing

330

of $100. As with other restrictions stated in the

Table 3. The matrix of productfeatures Product Competitor Feature Featureclass' score score weight

other submodels, this need not apply in the general model, which may be individually calibrated.

4.2 Evaluating the advertising spending effect index The current advertising expenditure is $42,000, while competitive spending is estimated at $35,000. The calibration of advertising elasticity is illustrated in Table 2 (see also Fig. 3), and is derived in a similar fashion to the price elasticity. Competitor response is also evaluated in a similar way; and advertising spending may vary in each time period, in multiples of $1000.

4.3 Evaluating the product development effect index Calibrating the product development submodel involves assumptions for competitor response in the event we upgrade our product or not. For this example, given that:

Table 3 illustrates four general feature areas in which the products are rated. The current scores for our product and the competitive product are 129 and 150, respectively. Figure 3 illustrates diminishing returns for increased product features and the elasticity associated with an increase in product features,

4.4 Evaluating the selling effect index

7

6

5

F2 F3

6 5

8 6

9 8

129

150

Total score

~Feature cost = $5000/step.

the competitor possess one salesperson. The average cost of a salesperson is $35,000 annually, and Fig. 3 illustrates diminishing returns for (comparatively) increased amounts of selling effort (through adding new salespeople), and the selling elasticity. Competitor responses to adjustments in our selling effort are estimated and an upper limit of 3 salespersons is assumed. Calibrating the selling submodel also involves estimating the competitive response in the event we add a new salesperson or not. In this example we assume that if: (1) we add a salesperson, p(similar action by our competitor)=0.7, and p(no matching actions)=0.3;

(1) we upgrade our product, p(similar upgrades from our competitor)= 0.9, and p(no matching actions)= 0.1; (2) we do not upgrade our product, p(upgrade by our competitor)= 0.1, and p(no competitor upgrade)= 0.9.

FI

(2) we do not add a salesperson, p(similar action by our competitor)= 0.8, and p(competitor adds a salesperson) = 0.2.

4.5 Results The simulation program was used for the example described in this section. The initial parameter values (price, advertising, salesforce, and features) are as given in the previous section. The submodel's functions were calibrated using the data in Tables 4(a)-4(d). Six policies were simulated 100 times each, with replication of random numbers across the models. The simulation period was 12 months. The results of the simulation are given in Table

For the derivation of the selling effect index, the assumption is made that both the firm and

Table s 4(a)-(d). Modelcalibration Table 4(a)

Table 2. Calibration for the advertising submodel Action a

Estimated competitor responseb

Raise spending

p(Match) p(Nothing) p(Match) p(Nothing) p(Match) p(Lower)

0.6 0.4 0.2 0.8 0.8 0.1

p(Higher)

0.1

Decrease spending Do nothing

aCurrent advertising spending is $45,000. bCompetitive advertising spending is $32,000.

Table 4(b)

Price

Units sold

$800 $1200 $1600

3000 2300 1900

Advertising ($/yr)

Table 4(c) Salespeople 1 2 3

Units sold

$32,000 $42,000 $52,000

2050 2300 2450 Table 4(d)

Units sold 2300 2500 2600

Feature score 0.8 base Base 1.2 base

Units sold 1900 2300 2500

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Table 5(a). Outputs of the simulation model Policy 1

No changes from the base

2

Increase price $I00 in month 1

3

As 2 and increase advertising $1000 in month 6

4

As 2 and increase salespeople by 1 in month 3

5

Increase price, sales, and advertising as in 2, 3, and 4

6

Same as 5 with feature 1 increased 2 units in month 4

Comp.

Average market

Final market

Protit

Original Passive Active Original Passive Active Original Passive Active Original Passive Active Original Passive Active Original Passive Active

2226 2316 2328 2153 2207 2259 2157 2212 2259 2270 2329 2377 2275 2334 2384 2366 2399 2779

2175 2311 2352 2089 2197 2272 2098 2205 2281 2243 2358 2437 2252 2367 2248 2364 2494 2560

2,595,390 2,698,339 2,717,398 2,722,631 2,792,919 2~853,223 2,722,624 2,798,109 2,858,731 2,875,698 2,921,376 2,984,363 2,851,115 2,927,006 2,990,339 2,921,179 3,002,500 3,068,179

Table 5(b). Probability calibration for sensitivity analysis Action Raise price Lower price Unchanged

Comp.

Original

Passive

Active

P(Match) P(Nothing) P(Match) P(Nothing) P(Match) P(Lower) P(Higher)

0.2 0.8 0.5 0.5 0.7 0.2 0. I

0.1 0.9 0.4 0.6 0.8 0.1 0. I

0.3 0.7 0.6 0.4 0.6 02 0.2

5(a). Average Market represents the mean market share for the 12-month period, final market is the firm's market share at the end of 12 months, and average profit is the mean annual profit for the simulations. The first row for each policy represents the base competitive posture we have assumed (i.e. competitor posture = "original"). The following two rows relate to sensitivity analyses, discussed in the following subsection, The results for the original market assumptions indicate that additional marketing efforts increase the firm's profits. The results are dependent on the calibrations used, and in general, we cannot expect this pattern. However, simulation would allow the user to experiment with various policies, to determine a satisfactory, if not optimal, policy.

4.6 Sensitivity analysis Table 5(b) illustrates the form of sensitivity analysis performed in the numerical example, Given the different policies, and the associated profitability for each, it is instructive to test the sensitivity of these profits to competitor profiles, The competitor profile implies higher or lower probabilities of competitor responses to the user's marketingactions. Higher probabilities of reaction indicate a more aggressive competitor,

while lower probabilities denote more passive competitors. The sensitivity analysis indicated in Tables 5(a) and 5(b) shows profits resulting from the given probabilities for both aggressive and passive competitor postures. Interestingly, as seen in Table 5(a), both aggressive and passive profiles uniformly fare worse (for the competitor) than the original probability calibration, indicating that this is either a local or a global maximum strategy for the competitor. This may be a source of comfort to the marketing manager, as profits are in fact improved by 2.7 and 5.03% for passive and aggressive competitor stances respectively, using the most profitable policy.

5. DISCUSSION This paper has described a simulation model representing the basis of a marketing decision support system or MKDSS for supporting industrial marketing decision-making. The utility of any new model lies in the benefits and new insights it offers over existing modeling approaches. In this respect, it is believed that the proposed model offers a significant set of capabilities over those described in Section 3.

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The major improvement over the decision calculus-type model advocated in Refs [21, 25] is the use of Monte Carlo simulation to model competitive effects within the submodels. This enables both the incorporation of subjective managerial experience (or in fact, empiricallyderived assumptions--depending on data availability) into the model, and avoids the difficulty of grouping all competitive efforts into a single variable. Regarding the latter issue, the proposed approach models competitive effects for each marketing instrument, so that the distinction between different aspects ofcompetitive effort may be more accurately made. The model described here has obvious advantages over single-instrument models, such as those described in Refs [1 l, 20]. Although there are unique emphases in industrial marketing instruments, the need to incorporate multiple marketing instruments is an overriding one. Similar to this model, the Hertz and Thomas model [15] utilizes simulation to assess the risks of various investment strategies in a firm, but more closely resembles feasibility analysis. This investment decision is at a higher level than the marketing instruments used here, and furthermore, their model treats firms in isolation, ignoring direct competitive effects. Thus, the proposed model appears more suited to precise competitive modeling, Further benefits offered by the simulation model include the ability to easily incorporate assumptions of expanding or contracting markets. Both multiplicative and perceptual mapping approaches fail to incorporate this ability in their basic forms. In addition, only the Hertz and Thomas model [15] handles stochastic assumptions. The use of Monte Carlo simulation helps both to increase the confidence level of the decision maker and the sophistication of the sensitivity analyses performed. In the latter case, insights are offered through estimating the consequences of more "passive" or "aggressive" competitor postures, 5.1 Model usage considerations

In the simulation, several assumptions have been made, which must be clarified for the purposes of the implementor. These assumptions are: (a) While a duopoly was used in the given example, the model extensions described support pure competition,

Different weights may be attributed to each competitor's market instruments, e.g. a lower competitor price will carry a greater weight than a higher one. (b) In the simulation model, we have again employed a short-term horizon, simulating market behavior over a 12month period. One overlooked issue is that of long-term vs short-term goals. If for example, high spending on some market actions is taken early on, the effects of these actions may feed through only in the middle- or longterm. Obviously, the remedy would then be to perform the market simulation over a longer period of time, bearing in mind the deterioration of market predictions and forecasts made farther into the future. (c) With some market instruments, it is again obvious that over the short-term, to maximize profits, one could implement all features as soon as possible, particularly, the "large-step" instruments such as product features or salespeople. To counteract this, the simulation model utilizes constraints in the form of minimum time intervals between the usage of both market instruments mentioned above. Implementors will also need to determine their context-specific constraints. (d) Finally, a time interval typically exists between the use of a marketing instrument and its resultant effects. For example, employing additional salespersons will involve a learning curve or training period, before the salespeople attain maximum effectiveness. In the example, we specified a month as the learning curve for salespeople. In the same manner, there will be a time interval for other market actions involving price, advertising, and product development to take effect. The implementor of the model will again be required to calibrate the model to specifically take these into consideration.

5.2 DSS development and implementation considerations The simulation-based marketing mix model discussed in this paper forms the basis of a

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MKDSS for the support of this activity. In order for this to take place, the DSS developer must be aware of the requirements for successful MKDSS implementation. These requirements may be envisaged at two levels, At one level, at the start of the DSS development cycle, attention must be paid to the feasibility of the proposed system, namely, whether the MKDSS will be cost-effective, technologically feasible, and organizationally acceptable. In relation to cost-effectiveness, the two-stage model of economic feasibility proposed in [17] is viewed as more effective than traditional methods. The first stage involves a focus on benefits, while the second, carried out later in the development cycle, focuses on more rigorous cost justification, Given the exploratory nature of DSS, the benefits may not be immediately apparent, Technological feasibility will merely check for ease of access to corporate and external data bases and that the necessary computational capacity is available. The model itself may be implemented using a wide range of tools, ranging from IFPS, through PC Excel, to SLAM. A micro-based system with a downloading capability would be suitable for system implementation, If the MKDSS is to be "institutionalized", then the necessary structures and resources must be set in place. The MKDSS must be integrated into the organization, with specific procedures designed for regular updating of the DSS database, and responsibilities allocated to specific individuals. The final issue of testing relates to the thesis (see [22]) that DSS outputs are less intuitively testable by developers because they do not have a frame of expectation for the results of the often novel processes. This calls for more rigorous testing by the DSS development team. One capabilitity that will be required of the DSS is parametric sensitivity analysis, as illustrated in the example provided. The MKDSS user will need to perform such analyses to determine the sensitivity of the model output to variances (i.e. best and worst-case assumptions) in the model's inputs. The sensitivity analyses will require parallel (additional) data entry in DSS calibration, and serve to indicate also, the risk associated with the adopted policies,

6. CONCLUSIONS Improved modeling techniques and data quality continue to offer marketing managers fresh opportunities to use computer-based tools to gain competitive advantage. The increasing use of marketing decision support systems or MKDSS by these managers reflects a growing appreciation of the potential benefits offered by such systems. In determining suitable marketing mix levels within industrial markets, MKDSS may provide marketing decision support by estimating the consequences of a range of considered actions. In this paper, we have described a market simulation model that interactively represents multiple marketing mix elements. In addition, it explicitly incorporates competitive assumptions in considering each marketing instrument, and their implications, in terms of sales and profits. Monte Carlo simulation was used to model these competitive effects; and using a range of marketing actions, helps to identify good, though not necessarily optimal marketing mix levels. An example was used to illustrate the model, based on duopolistic assumptions. An approach is used in which sales response curves for marketing instruments involve the quadratic fitting of relative effort (i.e. of the firm vs that of its competitor(s)) against sales, in order to incorporate competitive effects. In addition, a number of hybrid policies were tested to demonstrate the use of the model for different types of policies. In summary therefore, the simulation model offers product managers the opportunity to test multiple-instrument strategies in the industrial marketplace with an improved level of confidence. This results from the incorporation of both managerial experience and stochastic assumptions into the decision model, and the ability to perform extensive sensitivity analyses. It more finely distinguishes between different aspects of competitor effort and may be used for both static and dynamic markets. Future research will involve the development of distinct competitor profiles, e.g. "aggressive" vs "non-aggressive" competitors, and the testing of various policies against these profiles to determine suitable countering strategies. In addition, duopolistic assumptions will be relaxed to allow multiple-competitor assumptions, and to develop techniques to model

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competitive effects, with greater weight given to better-placed competitors. APPENDIX

on sales have similar forms, as follows: Define

f ( k , t ) = the firm's effort (expenditure, etc.) in influence k in period t; I(k,t) = the industry's effort in influence k in period t;

1

Overall Model Structure

X(k)(t ) = f ( k , t ) / I ( k , t )

The form of the model is as follows: T

f(k,O)/l(k,O)

Max ~ P(t)

,=, r = ~ [g(t)'Q(t) - C(t)] ,= ~

(1)

such that

this is the firm's effort relative to the total industry in period t compared to the base period, 0; G~k)( ) = the fitted function of the sales response to influence k.

Q(t) = f [ x ( t ) , H(t), Q(t - 1), E(t)]

Then, the submodel representation for e~k)(t), the effect index, in (2) is given by:

where

P(t) = profit from the product in period t; g(t) = per unit profit contribution of the product in period t, excluding marketing expenses; C(t) = marketing expenses associated with the product in period t; Q( t ) = product unit sales in period t; X ( t ) = product marketing policy in period t; H ( t ) = competitive effects affecting the product in period t; E(t) =environmental economic variables affecting the product in period t.

e~k)(t) = dp"G~k)(X~k)(t --1))+ (1 --~p)G(k)(x(k)(t))

(3)

where ~b is the factor representing the prior period's effect (4~ = 0 implies no prior memory). APPENDIX

4

Effort in the Product Features Influence Overall effort in the product features influence is product profile or overall score is calculated as follows:

x~3~(t)= ~ score (j).w(j) j=t

(4)

where APPENDIX

2

score (j) = score for product feature j; w ( j ) = weight for product feature j.

The Product Sales Rate Model The multiplicative model structure for the product sales rate is as follows: x Q(t) = Q(O) l-I etk~(t) k= ~

(2)

where Q(0) = reference value for product sales (i.e. in period 0); etk)(t) = the kth effect index of sales influence on the product in period t.

APPENDIX

3

The General Form o f the Submodels The submodels representing the contributions of the various influences (price, advertising, etc.)

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