76

ASSESSING INDUSTRIAL

MARKETING MIX EFFECTIVENESS:

A PROCESS SIMULATION

by

MARYSE J. BRAND

Correspondence to:

Drs. M.J. Brand, Doctoral Candidate in Marketing, University of Groningen,

School of Economics, P.O. Box 800, 9700 AV GRONINGEN, The Netherlands, tel.

31-50-63369^.

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ASSESSING INDUSTRIAL MARKETING MIX EFFECTIVENESS:

A PROCESS SIMULATION

Traditionally, marketing mix effectiveness on consumer markets is assessed

by using quantitative methods. On industrial markets, specific problems

demand specific methods which take into account the complex industrial

buying situation and data limitations. One of these methods is process

simulation which main features will discussed in this paper.

INTRODUCTION AND OUTLINE

On both consumer and industrial markets suppliers want to realize their

organizational objectives and to survive the growing competition. With

respect to consumer products, the importance of marketing in attaining these

goals is generally accepted. The industrial market place however, has been

dominated by product and production oriented managers for a long period.

Since a number of years the attention to the quality and allocation of

marketing efforts on industrial markets has been increasing. A first and very

important development was the idea that the traditional marketing mix

elements (product, price, place and promotion) do not suffice to describe

industrial marketing behaviour. The concept of relationship was added. In

1972 Kotler already stated that a marketing transaction can be seen as an

'exchange relationship'. Some years later Bonoma, Zaltman and Johnson (1978)

introduced the 'buyer/seller dyad* while other researchers developed the

'interaction approach' (Hakansson, 1982). In this respect Storm (1987)

stresses the importance of relationships and reputation among others.

Wildenberg (1988) mentions the importance of service, speed, solidity and

specialization ( the 4 S's) for suppliers of industrial goods. The concept

of two-firm interaction evolved into a more extended 'network* approach

(Hakansson, 1987) which concentrates on interaction among a large number of

interconnected organizations.

The development of concepts such as relationships and networks stresses the

idea that industrial marketing and organizational buying are closely linked.

A second factor supporting this idea is the complexity of the organizational

buying process. This complexity, consisting of e.g. a multi-person buying

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centre and a multi-phased buying process, causes that more knowledge about

the buying organization is needed. Both factors imply that the industrial

buying process should be integrated when studying industrial marketing

phenomena.

As a consequence of the considerations above, in this paper attention will be

paid to both the buyer and the seller, the presence of a buying centre and

the different steps of the buying process.

In recent years the marketing department of the Groningen University in the

Netherlands has performed several studies on effectiveness measurement. Some

of these concerned traditional consumer products. On this products large data

bases were available and econometric modelling was used. Other studies

concerned markets where data were hard to get (e.g. small retailing

organizations, Zwart (1983) and the Dutch heroin market, Hoekstra (198?)).

Using a process simulation of the decision making on these markets it was

possible to assess the effectiveness of different policies. In this context

the project described here was initiated to explore how to assess

effectiveness of marketing instruments on an industrial market.

This research project explores the possibilities to obtain insight in the

effects of marketing instruments of industrial suppliers. In addition to the

classical instruments we will include more recently stressed concepts such as

relationships, service and reputation. This special attention to the efforts

of industrial suppliers is needed because the 'typical industrial market 1 is

very different from the well known consumer market. Industrial markets are

generally characterized by a small population, technically complex products,

a complex decision process, and a lack of 'good* data.

The outline of the paper is as follows. Firstly, we will briefly present some

theoretical background of organizational buying behaviour. Then we will go on

to discuss a number of efforts to measure the effectiveness of different

marketing instruments on industrial markets. The focus will be on the

availability of data and the aptness for managerial use (answering 'what-if

type of questions). Next, the consequences for our research project will be

derived and we will discuss the main characteristics of this project.

Attention will be given to the objectives of the research, the methodology,

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the model formulation, and the procedure for data collection and analysis. At

present, only part of these activities have been completed.

THEORETICAL BACKGROUND

Models of buying behaviour

The models of buying behaviour discussed in literature can be divided into

several groups. Figure 1 presents a categorization of existing buying

behaviour models. A first distinction can be made between general models and

situation specific models. The category of general models can be divided

further into comprehensive models and partial models which account for

certain factors within the comprehensive models. These factors relate to

subjects such as source loyalty, make or buy decisions, ego-enhancement of

individuals, development of relationships and risk-reduction. The

comprehensive models can be subdivided into buyer oriented models and more

recent interaction models.

Figure 1. A classification of buying behaviour models.

Buying behaviour models

General models

Comprehensive models Partial models

Situation specific models

Decision flow models

Buyer oriented

models

Interaction

models

There are two well known buyer oriented models, viz. the Webster and Wind

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framework (1972) and the Sheth model (1973). These models present an overview

of the factors that influence buying behaviour and sometimes indicate the

direction of these influences. Two very important categories of factors are

the environment of the organization (stressed by Webster and Wind) and the

individual within the organization (stressed by Sheth).

Generally known interaction models are the I.M.P.-interaction model and the

Network model. Examples of models which include, but do not focus upon

interaction are presented by Bender (1983) and by Choffray and Lilien (1978).

Although both the comprehensive models and the interaction models are based

on the way organizations behave in practice, we classify these two model

types as conceptual models since they offer a general conceptual basis

which helps us to understand specific problem situations.

Decision flow models are primarily based on observations, so we call them

empirical models. Using methods like Decision Systems Analysis it is

possible to give a detailed descriptive model of a specific buying process

(see e.g. Vyas and Woodside (1984)). Another situation specific model has

been developed by Cardozo (1983). Cardozo distinguishes six stages which

must be completed by a potential supplier in order to sell his product. The

probability of the final sale can be assessed by estimating the probability

of succesfully concluding each phase. Whe will return to this model in the

section about our research project.

For our research we want to use a framework which allows us to incorporate a

number of buying behaviour properties, such as environmental influence,

individual behaviour, buying centre functioning, interaction and the

existence of a multi-phased buying process. This is obtained by integrating

the main features of the comprehensive models of Webster and Wind (1972) and

Sheth (1973) with the concept of interaction. This results in the framework

outlined in figure 2.

The framework should be interpreted as follows. If we want to analyze a

certain industrial buying situation, a description of the setting offers a

good starting point (box I). In this description attention should be paid to

the environment, the organization and the individual (all including task and

non task related factors). In box II we study how the buying centre

translates the organizational and individual objectives into more or less

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Figure 2. A framework for identifying influences on the industrial buying

process.

I. Situation - societal environment (cultural and economic)- organizational environment (cultural and structural)

(internal and external)- individual characteristics

II. Objectives of the organization and the individuals result in objectives of the buying centre

III. Properties of the buying process- buying centre - composition

- roles- decision criteria- information search- number of alternatives- decision rules

IV. Outcome for each separate stage of the buying process

concrete buying objectives. This translation is influenced by the contents of

box I: the setting. The next step is to combine the influences of box I and

the objectives of box II into a systematic description of the buying process

(box III). This description consists of five dimensions, which may change

during the stages of the buying process. Therefore box IV has been added,

representing the outcome of the decision process for each separate stage.

Stages that are of special importance to the supplier are the identification

of potential suppliers, the assignment of requests for quotations (RFQ's) and

finally the closing of the contract. To illustrate the influence of

situational factors, consider the impact of a change in the buying centre.

These kinds of changes are not unlikely to occur during the extended time

period of the buying process and are situated in box I. A new individual with

its own background is replacing a member of the buying centre. This

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individual has personal (im)material objectives which influence the

objectives of the buying centre. In addition to this, this individual has its

own personal contacts, information behaviour and criteria evaluation. A

typical situational factor to influence the outcome (box IV) is the number

and strength of the competitors.

The framework can be of assistance in arriving at a systematic, complete

description of a buying situation. At this point we would like to return to

our typology of figure 1 where we discerned empirical and conceptual models.

These two types of models should not be seen as mutually exclusive but rather

as complementary. Empirical observations are necessary to formulate a

conceptual model and a sound conceptual base is essential when developing

empirical models. This complementarity has consequences for our research.

Having studied the existing literature we formulated the model presented

above (figure 2). Based on this model we collected data to arrive at a

systematic description of the buying process. We will return to this topic

later when we describe the stages of our research project.

Quantitative measurement

In every market, marketers want to know why a (potential) customer does or

does not buy certain products. Only when these reasons are known, the

marketing mix can be adapted to the specific needs of a market segment.

Although this holds for consumer and industrial markets, industrial markets

are often characterized by very small segments, sometimes consisting of only

one organization.

For every segment one must try to answer a number of questions. Do we use

the right marketing instruments? In the right quantities? In the right

order? To be able to answer these questions we need research about the

effectiveness of the various instruments and their interaction. Usually

sales are used as indicator of the marketing instruments' effectiveness.

Still, in some cases other measures can or should be used. Examples are the

use of 'recall' for measuring advertising effectiveness or coupon redemption

in case of a direct mail campaign.

A first indication of what the customer thinks is important can be obtained

by asking the customer which attributes influence his/her buying process and

to what extent. Examples of this approach are the studies of Lehmann and

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O'Shaugnessy (1982) and Pitt and Nel (1988). If one wants to know in more

detail to what extent the buying process (i.e. sales) is influenced,

quantitative research is necessary.

Data problems

Theoretically, methods like regression and variance analysis are most

suitable to assess effectiveness. We call them 'quantitative* methods. In

this section we will present some results obtained through the use of these

methods, the problems that arose, and the alternative methods that can be

Table 1. Data problems in industrial markets.

reason resulting problem

1. industrial markets or segments

usually are very small

2. situations of firms

vary considerably

- cross section analyses are very

difficult

3. no complete systematic records

are kept by individual firms

4. no systematic data gathering is

done by commercial marketing

research agencies

- no time series can be estimated

5. products, prices and service

are customer specific and

usually not public

6. actions of competitors are

unknown

7. the buying process is very complex

because of multiple buying influences

and stages, and formal procedures

- difficult to define identities

and to make an appropriate

model structure that can be

quantified

Source: this table is partially based on Webster (1978).

8.4-9-

thought of. The main difficulty of doing quantitative research on industrial

markets is the gathering of data. For several reasons industrial marketing

data are harder to get than data about consumers (table 1).

These problems can be only partly relieved by making organizations more

conscious of the value of complete and correct data. When developing methods

for industrial marketing, special attention to the feasibility of the data

requirements is always needed.

In the introduction of this paper we indicated that the traditional

marketing instruments account for only a part of the marketing mix available

to an industrial marketer. Factors like service, relationships and reputation

should be included too. However, up to now most quantitative research

concentrated on only one or two of the traditional P's. On the one hand this

is due to the data problems mentioned in table 1, while on the other hand

conceptual difficulties and newness contributed to this situation. Reviewing

published effectiveness studies of the traditional four P's, only

instruments in the class of promotion turned out to be suited for

quantitative analysis.

Promotional effectiveness

The 'P f of promotion stands for a number of promotional tools, such as

personal selling, advertising, direct mail and sales promotion. The

relatively successful application of quantitative modelling in this field is

mainly due to the relative ease of experimenting with for example salesmen or

advertising (both in time and in territories). These experiments enhance

data availability and variation. A second favourable factor refers only to

advertising research. Because advertising is public, competitive effort can

be estimated which allows us to compare between different companies. We will

briefly summarize some results of quantitative research regarding the

effectiveness of a) the sales force, b) sales promotion and c) advertising.

a) Within the area of promotional effectiveness, we see that the majority of

studies is about the sales force. This is mainly due to the fact that company

records contain a considerable amount of usable data. Another reason is that,

due to increasing competition, growing product complexity and higher quality

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purchasing skills, the importance of personal selling on industrial markets

is growing. Studies about sales force effectiveness can be classified in

three groups, i.e. studies emphasizing the influence of objective factors on

performance, studies stressing the influence of psychological factors, and

finally studies formulating normative insights from 'agency theory'. The main

characteristics of these three groups are summarized in table 2.

Table 2. Main characteristics of 3 groups of sales force effectiveness

studies.

studies focussing on: I. objective |ll. psychological |lll. agency

influences I influences I theory

1. Kind of variables

included

2. Examples of

independent

variables

3. Dependent

variables

4. Techniques used

5. Results

6. Examples of

studies

(Clearly defined jUnclearly defined, |Salesman's

|and measurable. (subjectively [related.

|Sales environment or(measured. Focus on (Rewards and

(sales person related|motivation and (supervision

| (effort |

(Workload, territory (Salesman's charac- (Environmental

|potential,salesman 1 s(ter, incentives, (uncertainty,

(experience, adverti-(satisfaction, (salesman's

(sing expenditures (motivation (character

|Sales(-quota), (Sales/quota, sales (Optimal com-

|management ratings (and manag.ratings (pensation plan

(Linear and multipli-|Linear regression,

(cative regression, (structural equa-

|simulation |tion models

(Market potential ('Psychological'

(shows clear positive)variables show

|influence,salesman's|direct and indirect(depend on or-

|traits only limited (influences (ganizational

| | (objectives JRyans & Weinberg |Lament & Lundstrom (Basu e.a.

|(1979), Parasuraman ((1977), Aaker & ((1985), Lal &

(and Day (1977) (Bagozzi (1979) (Staelin (1986)

|Mathematical

derivation

compensation

plan should

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Considering the results summarized in table 2 we can conclude that despite

the favourable circumstances mentioned above, no 'lawlike relationships' can

be found. This is in contrast with studies on consumer markets e.g. Leone and

Schulz (1980) and Tellis (1988). We would like to stress that the three types

of studies mentioned above are not mutually exclusive but rather

complementary. In order to get a complete and realistic view of a specific

situation the use of more than one 'approach* is to be recommended.

b) Sales promotion refers to a collection of various tools. The AMA defines

sales promotion as:

'..those marketing activities, other than personal selling, advertising, and

publicity, that stimulate consumer purchasing and dealer effectiveness..'.

A sales promotion is a temporary change in one of the marketing instruments

and is especially successful in the growth stage of the product cycle. In the

literature we can find three ways to assess the importance of different sales

promotional tools: the importance 1) as perceived by buyers (Parasuraman,

1981), 2) as perceived by sellers (Bellizi and Hite, 198?), and 3) as

indicated by sales records (Zinkhan and Vachris, 1984). Information thus

collected may be valuable to management, noting that in most cases the data

are subjective ratings of buyers and/or sellers.

c) Advertising can be a powerful tool to increase sales and market share.

However, problems arise when the effectiveness of advertising is to be

assessed. The ultimate objective of advertising is sales. Lagged advertising

effects and interactions with other instruments complicate the measurement of

the advertising effects. Given the severe data problems on industrial

markets, quantitative analysis will become very difficult. In the next

section we will briefly discuss how to take lagged effects and interactions

into account.

Direct measurement of the effects of advertising uses intermediary variables

like recall and readership. It should be noted that much research in the

field of consumer-advertising is be applicable to industrial markets too.

The information processing of both consumers and industrial buyers is

influenced by attractive headings, the use of colour, etc.. Examples of

studies designed to measure the effectiveness of advertising on industrial

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markets are presented by Hanssens and Weitz (1980), Soley and Reid (1983),

Soley (1986), and Korgoankar, Bellenger and Smith (1986). The methods used

for analysis are multiple regression and variance analysis. Independent

variables are mainly content related, while the dependent variables are e.g.

recall, inquiries, readership and attitude. The findings of these studies

suggest that the effect of advertising depends on the type of product

promoted (Hanssens and Weitz, 1980) and the intended result of the individual

ad or campaign (Hanssens and Weitz, 1980; Korgoankar, Bellenger and Smith,

1986). This implies that when management wants to use analyses like these, it

needs to formulate its communication objectives explicitly.

Quantitative interaction approach

The literature contains substantial support for different types of marketing

mix interactions. However, results in the industrial marketing context are

very scarce. Interactions observed in industrial markets are those between:

1. sales calls and samples or hand-outs (Parsons and vanden Abeele, 1981);

2. sales calls and prior advertisements (Morill, 1970).

For interactions between advertising, image and price, and between

advertising and availability, only limited evidence is found. In this section

we investigate the possibilities of including interactions in quantitative

analyses.

The general form of econometric marketing mix interaction models is 0 =

f(Mmix t ). 0 is the objective of the firm, like market share or sales, and

Mmixfc consists of the values of the different marketing instruments of the

supplier. These models can incorporate the suppliers' marketing mix of

previous time periods or the competitors' marketing mixes. By using a

multiplicative model structure, interactions between independent variables

can be included too. These studies depend on the presence of good data. In

this respect the characteristics of good data are availability, quality,

variability and quantity (Naert and Leeflang, 1978. p.206). This is where

problems arise in industrial marketing research (see table 1). Usually, data

on industrial markets and marketing efforts are not collected

systematically; neither by the suppliers themselves, nor by marketing

research agencies like Nielsen and MRCA which concentrate on consumer

markets. On top of this, products, prices and promotional efforts on

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industrial markets are often customer-specific, which makes the collection of

data difficult-, which holds in particular when competition is fierce. The

possibilities of this type of modelling, if good data are available, is

illustrated by a number of research efforts concerning U.S. Navy Sales force

effectiveness. In these cases sufficient and exact data were available on

advertising, coupon redemption and sales force efforts in a number of regions

over several years. Competitors were known and limited (viz. other military

institutions) and the dependent variable was easy to define (the number of

new recruits) (see Gatignon and Hanssens (198?)).

Limitations of quantitative measurement

The limitations of a quantitative approach as described in this section are

important. We would like to mention four topics.

1. Quantitative studies often require too much data to be feasible on

industrial markets. These data requirements relate to availability, quality,

variability and quantity.

2. The influence of relationships with suppliers or competitors is not

accounted for. This is due to conceptual and data requirement problems.

3- Interactions between instruments are difficult to take into account. Where

interaction can be included theoretically, data requirements become even

stricter. In order to develop an effective marketing program, insight in the

individual marketing instruments and the interactions between them is

necessary.

4. No attention is paid to the managerial use of the model (implementation).

It is very difficult for a manager with limited quantitative education to

interpret the model's outcomes and even more difficult to use the models in a

creative way.

Alternative approaches for quantitative measurement

Because of the limitations of quantitative measurement that we mentioned in

the previous section, other approaches have been developed during the last

decades. These approaches are mostly 'problem oriented'. The main features

of these methods are:

1. limited historical data is supplemented with subjective managerial input.

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2. adaptive, interactive modelling is possible.

In this way the manager can answer 'what if types of questions. The

subjective data can fill up two voids. In the first place it can substitute

for historic or actual information about different marketing tools that is

lacking for all kinds of reasons. In addition to this, other factors than the

traditional marketing mix are known to be important. Adaptive, interactive

models give the manager the possibility to decide him/herself what is

important regarding a certain marketing problem.

We will briefly discuss two categories of problem oriented methods, namely

i) cross sectional analyses, and

ii) process simulation.

i) Cross sectional methods are based on data about a large number of

organizations during a limited time period. The starting point of these

methods is the average organization in a certain industry. This average

organization may develop a kind of equilibrium behaviour that seems to be

reasonable, especially when applied in a consistent way. On top of this,

efficient-market theory implies that existing organizations have to approach

optimal behaviour, just because they survived. In this light it is very

useful for a company to compare its own market(ing) behaviour with the

average industry's behaviour. Two American applications of the cross

sectional approach are based on the PIMS program (1972), and the ADVISOR

project (see Lilien and Kotler, 1983, p.668ff, for a more detailed

description). Although these two applications produced useful insight in the

way businesses are run, some drawbacks must be mentioned. The first point of

discussion is fundamental, viz. the fact that average industry performance

may be very different from optimal behaviour. A practical problem is the

necessary cooperation of a substantial number of firms in order to collect

enough data. Furthermore important differentiating factors like pricing

policy , the quality of distribution and product features are not included,

ii) Another problem oriented approach which is flexible and capable of using

different types of information is process simulation. Simulation is a well

known method in production planning and logistics. Until now only little

attention has been paid to the possibilities of simulation in marketing. The

potential of this type of analysis can be shown, looking at two relatively

simple examples of industrial marketing research using the simulation

principle.

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Cardozo (1983) distinguishes 7 buying-phases in the organizational buying

process. The probability of a certain outcome of this process equals the

product of the probabilities of all 6 necessary sub-decisions. By assessing

these sub-probabilities (using historical, experience or research data), the

resulting probability of a purchase can be estimated. In this way causes of

certain sales can be traced and the impact of certain policies on sales

calculated.

The study of Parasuraman and Day (1977) focusses on the estimation of sales

revenues for different policies. Parasuraman and Day collected subjective

estimates of salesmen's characteristics and ability, carry-over effects, and

the sales response function of different segments. A series of experimental

runs with the model were made to investigate the sensitivity of estimated

sales revenue to changes in input variables, and to evaluate the model's

validity.

An extension of this type of analysis to more stages of the buying process, a

multi-person buying centre and the use of different decision rules, could

offer valuable insights in the effects of the industrial marketing mix. Once

the model frame is built, the input can be varied and the effects calculated.

Interactions between marketing instruments can be incorporated by modelling

the weights of information sources and decision criteria explicitly.

Moreover, through process simulation it is possible to take into account

untraditional 'instruments' like relationships, service and reputation. The

research project we will describe in the next section employs such an

extended simulation approach.

THE RESEARCH PROJECT

In the preceding sections we sketched a theoretical foundation of our

research. First we indicated which factors should be taken into account when

analyzing industrial marketing problems. Then we investigated the

possibilities of using quantitative methods to assess industrial marketing

mix effectiveness. These possibilities appeared to be limited, mainly because

of data problems and the nature of variables that could be included. In order

to estimate the industrial marketing mix* effectiveness alternative

approaches are needed. In this section we will indicate the implications of

the foregoing for our research project. In our study the following stages can

be discerned:

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1. formulation of objectives of the study

2. development of conceptual framework

3. definition of market to study

4. construction of process descriptions

5. identification of variables

6. model construction

7. additional data collection

8. simulation

Stated very briefly the approach we use consists of describing the buying

process, identifying the key variables that can be influenced by the

supplier, translating this to the supplier's marketing mix, and assessing the

suitability of the marketing instruments to influence buying behaviour on

this market.

To assess the effectiveness of the different marketing instruments we use

process simulation. This approach has several advantages. Firstly, the

number of variables that can be included in the model is very large. This is

important because a considerable number of variables influence the decisions

made in the different stages of the buying process. Secondly, the simulation

model indicates in what stage of the buying process certain things went wrong

or right for a specific supplier. A third advantage is that subjective

personal opinions of the model user (e.g. the manager) can be included very

easily in the model. A final benefit of this approach concerns the data

required to calibrate the model. Process simulation models do not need data

collected during a long period of time or in a large sample like econometric

models.

1. The objectives of the study are described at the outset of this paper.

Concisely put, the objective of the project is to investigate the

possibilities of measuring the effectiveness of marketing instruments in a

untraditional environment: an industrial market.

2. As a first step, the factors and relations that should be included in the

study needed to be identified. Based on the existing literature we developed

a conceptual framework reproduced in figure 2 and discussed in the section on

models of buying behaviour.

3. The next thing to do was defining the market to study. Given the

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objectives of the study, the market should differ from traditional consumer

markets in a number of ways. The following desired product and market

features were recognized. Firstly, the product should be of moderate

complexity and value, the purchase being of the modified rebuy type. Thus one

is assured of a thorough decision process in which several organizational

departments are involved. Secondly, the number of buyers should be limited

but not too small. When this holds, it is almost certain that quantitative

analysis is not feasible while special situations, like monopsonies, are

excluded.

We have chosen the market for a certain type of heat-exchangers, frequently

used in the Dutch food industry. Within this heterogeneous industry we

selected the diary sector for our study. The buyers in this sector consist c:

ca. 35 organizations, of which five large cooperations control the market. We

included two of these cooperations in our study. To get a first indication of

this market, we interviewed a technical and a commercial employee of a

seller. These interviews took about two houres each and included topics like

the level and nature of competition, technological developments,

concentration, buyer/seller relationships and product characteristics.

k. As stated before, it is important for a seller to understand the buying

process in order to implement a successful marketing strategy. To attain

this understanding, we constructed process descriptions based on semi-

structured interviews and documents in two organizations. In both cases three

to four key members of the buying centre were interviewed. In the first

organization these members were the vice president of production, the plant

manager, the senior technical manager, and the technical manager responsible

for heat-exchangers. Note that the buying department did not participate in

the buying process in this organization. In the second organization the

members interviewed were the plant manager, the technical manager and the

head of the buying department. Both process descriptions were integrated into

one diagram. In the interviews we paid attention to the five dimensions we

mentioned in our framework (figure 2, box III).

5. Based on the integrated diagram of the previous stage we were able to

identify the variables that were important during the different process

stages. Eventually we arrived at 53 variables to include in our process model

(see appendix 1). We divided these variables into three groups, each

determining the outcome of one of the three crucial stages of the buying

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process. These stages were: identification of potential suppliers, the

assignment of request for quotations and finally the closing of the

contract.

Figure 3« Structure of the process model.

Informationl

SUPPLIER Information2

Informations

Need

Buying centre - composition I - roles

Bidlist- criteria & scores- decision rules- number of alternatives

BIDLIST

Request for quotations- criteria & scores- decision rules- number of alternatives

RFQ's

-^ Quotations and additional information

Contract- criteria & scores- decision rules- number of alternatives

Negotiations

CONTRACT

Feed back

6. The construction of the process model including the 53 variables is

fundamental to our study. This construction has not been completed yet, but

at this stage we can offer an outline of the model's structure. The

information used in this stage is still taken from the interviews mentioned

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in stage 4 above. In figure 3 the basic model structure is given. The model

is formulated using a Pascal based discrete simulation program, called

DESIMP.

7. When the simulation model is built, additional data will be collected.

These data concern the values and weights of the 53 variables, the importance

of the different buying centre members and the validation of the model

structure.

8. The actual simulation will consist of running the model under different

circumstances. Both input variables (such as the marketing instruments of a

supplier), and 'model variables'(like weights, decision rules and buying

centre composition), will be systematically varied. Sensitivity and scenario

analyses will give indications of the effectiveness of different supplier

activities to achieve the three objectives: attaining the bidlist, receiving

a RFQ and getting the contract.

Using this model, the management of an industrial supplier will be able to

investigate a number of problems. Firstly, it can evaluate selling efforts in

the past, and try to indicate whether the right marketing instruments have

been used at the right moment. Secondly, the model can be helpful in

developing future strategies. Given the specific buying situation of a

(potential) customer, management can evaluate individual selling strategies.

CONCLUSION

Companies that want to formulate effective marketing programs need to know

their markets. To obtain this information, ad hoc samples and 'permanent*

panels can be used, and a range of analyzing techniques has been developed. A

number of these methods and techniques are included in day-to-day marketing

practice of companies and have proven their value. These companies constitute

a very special group, almost without exception they sell consumer products.

This is because most of the methods and techniques mentioned before, have

been developed with these markets in mind.

The last decades several attempts have been made to fill this lack of

quantitative methods to analyze industrial markets. A considerable number of

studies have been undertaken to gain insight in the effects of the

traditional marketing mix elements when used on industrial markets. If data

95-20-

were available quantitative techniques were used, while other situations

asked for more behavioural or exploratory types of studies. Some examples of

these studies have been presented in this paper, though important limitations

were found.

Alternatives are offered by 'problem oriented methods*, which allow the

model user to adapt the model (interactively) to special circumstances or

objectives. Two problem oriented approaches are discerned, viz. a

comparative and a process (simulation) approach. An important limitation of

the comparative approach is the way normative implications are based on these

studies.

The process (simulation) approach has a different base. Here the optimal

marketing mix is considered to be dependent on the buyers' decision process,

which can be described formally and consists of a number of stages. Studying

each of these stages in their right order, the effect of different factors on

the decisions made can be assessed. These effects and the results of

different scenarios can be simulated using a computer. The data needed for

this analysis consists of company records and subjective opinions of buyer

and/or seller. Process simulation offers the additional benifit that insight

in the decision process of the individual firm is acquired. This knowledge is

a prerequisite for a coherent, effective allocation of marketing efforts. A

final but important advantage of these flexible problem oriented approaches

is that the analysis is not limited to the traditional 4 P's. As indicated in

the introduction, industrial marketing can also be characterized by a number

of R's, S's or by the concept of industrial relationships and networks. In

this paper we outlined a research project which investigates the possiblities

of measuring the effectiveness of the industrial marketing mix including

these additional concepts. We have chosen to simulate the buying process in

order to assess the influence of organizational specific factors and

marketing efforts on three different decisions during this buying process.

This knowledge is very valuable for suppliers of industrial products in order

to evaluate past and future selling strategies.

96

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appendix 1. Variables influencing the 3 main stages of the decision process

measureI. Bidlist1 National representative 0,1-52 Trade shows frequency 1-53 Trade shows quality 1-54 Direct mail frequency 1-55 Direct mail quality 1-56 Sales effort frequency 1-57 Sales effort quality 1-58 Sales effort relationship (years) yrs.9 Trade journals frequency 1-510 Trade journals quality 1-511 Experience with supplier y/n12 Exper. with supplier (years) yrs13 Exper. with supplier frequency 1-514 Exper. with supplier aver, price 1-515 Exper. with supplier aver. del.time 1-516 Exper. with supplier gen.impression 1-517 Exper. with supplier reputation 1-518 Exper. with supplier references 1-519 Exper. with supplier demonstrations 1-520 Exper. with supplier tests 1-5

II. RFQ's21 Experience with product y/n/q22 Exper. with product (years) yrs.23 Exper. with product frequency 1-524 Exper. with product aver, price 1-525 Exper. with product aver. del. time 1-526 Exper. with product gen.impression 1-527 Exper. with product reputation 1-528 Exper. with product references1-529 Exper. with product demonstrations 1-530 Exper. with product tests 1-531 Technical knowledge 1-532 Technological knowledge 1-533 Quality software 1-534 Finishing 1-5

III. Contract35 Technical specifications 0,1-536 Technological specifications 0,1-537 Heat regeneration 1-538 Cleaning 1-539 Attentiveness 1-540 Flexibility 1-541 Promised delivery time (months) mnts.42 Experience with del.time (x %) %43 Cost-price (guilders/fl) fl.44 Costs of power supply 1-545 Maintenance costs 1-546 Exper. of plant with supplier y/n47 Exper. of plant with representative y/n48 Exper. of plant with product y/n49 Service 1-550 Ease of maintenance 1-551 Personal relationship 1-552 Negotiation athmosphere 1-5

97

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