- Email: [email protected]
A dynamic industrial product model
Suhila
This article reports on the development of TVmarketing model for industrial psoducts to aid marketing managers ir. developing marketing programs on a quarterly and yearly basis. The system incorporates a market response model Mqhrch relies heavily on the involvement of managers in providing inputs. The model wa>:parameterized and tested in the COltFtiW of a large manufacturer of capital goods. The results Irlere supportive of tht* conceptual roundness of the mot 4 anti its utility in decision making.
Formal planning is relatively new on the business scene an&to date neither its theory nor methodology is well developed. Several conceptual papers [A-41 on corporate plarnningsystems have appeared in the literature but detailed methodological discussions are sparse. Naylor [5] repc#rted on a survey of planning in 346 firms in which hr::found that although most firms were turning to corporate: planning models, most models had major shortcomings such as lack of flexibility, poor documentation, or excessive data requirements. Literature on marketing plaaning has dealt mostly with packaged con-
Address comzpndence to: Sushi13Rao, bchool of Managemu& Boston university, 704 Commonwealth Aver’ue, Boston, MA 022 15. Management 10, 235-242 ( I98I ) @Elsevier North Holland, Inc., 1981 52 VanderbiltAve., New York, New York 10017
Industrial Markrimg
Rae
sumer goods (with exceptions such as Johansson md Sambare [631,Little [7], and Choffray and Lillien [?I]), amd models fi)r the mos:: part have remained simplistic and unreali&. Some of the more complex models have not been emipirically tested and their validity has not been established. ‘The need for comprehensive markeling planning mod& is obvious. Lotshaw [!I] wrote: “The emphasis on marketing planning for industrial products ;rppears to be destined to increase in future, although it is currently not well established. Forma marketing planning as a part of total business planning will assist in dzveloping and implementing the precise objectives which are required in today’s increasingly complex markets arcdin identifying the profit implications of various marketing actions ”
Given the complexity of the product and marketplace however, planning models have to be tailor made to each organization. Close: involvement of the managers in the development aLlso makes it easier to implement thie model. Aaker and Weinberg [ IO] suggest that interactive :!;ystemstend to create an invplved decision maker who in turn will contr bute to the model building process. This in turn will affect the probability of the model bei:lg accepted by the organization. This article reports on an attempt to dev:llop and test a realistic and comprehensi\,e marketing planning system for a firm dealing in capital
goods, which captures the complexity of the planning process while remaining flexible and simple enough to be acceptable ‘to, and implementable by, industrial organizations. The planning system described here focuses on marketing m’.x decisions and is designed to be of use to marketing managers, produce managers, or planning managers as an aid in designing the narketing program on a quarttzrly and yearly basis and in making strategy decisions for a three year period. The market response model used enables the manager to (examine the implications of alternative marketing mixes in terms of market share and profit. The desired levels cz” the marketing variables are determined by the users of the model and are not arrived at through any optimization procedures. The system is designed to adapt to changes in the enviibnment szndcan be extended to various levels within the organization. A description of the marketing system modeled would be in order here to aid the reader in understanding the complexity of the task and to explain the choice of the marketing variables. The marketing system as depicted in Fig. 1 is that ~3:a large ca&al goods manufacturer operaking in several markets. The
products are distributed directly to original equipment manufacturers (0.E.M.s) and the spare parts are distributed both through the dealers of the manufacturer and the dealers of the 0.E.M.s. The firm operates under stic market conditions and the marketing variables employed at each level of the systLm are shown. variables for industrial products
market segment. ELEIWWS
OF THE PLANNING SYSTEM
The Phnning Ho The plm‘ning horizon is a three year period divided into quarters. Industrial goods manufacturers generally view the planing task in this manner, since the nature of the demand for their products and their production con-
P,C,I
P,C
hS,C
A,Q:S,D
D z DISTRIBUTION P - PRICE Q - QUALITY
S - PERSONAL SELLING A _ ADVERTISING C- MTOMER SE:RV3 CE FIGbHE 1. The marketing syskm.
236
straints require planning beyond one year. At the same time, the economic environment which influences the demand for their products is not predictable with any level of confidence beyoncl a three year period.
Demand The demand for the product is dertved demand and it is assumed that the marketing effort of the industry has such a small effect on industry sales that it can be neglected for planning purposes. The forecasted demand for the industry is assumed to be available from forecasts made external to the system; a fairly reasonable assumption to make for large companies.
Output of the System Market share is the primary output since each firm’s marketing efforts only affect its share of the total market and not the demand. As Schultz [13] and Lambin [ 141 point out, this offers the advantage of eliminating the impact of socioeconomic factors that have the same influence on all the fiis in the industry. Competitive effects are also automatically taken into consideration. The company may not have the objective of maximizing market share, however, for several reasons. Regulatory constraints would set an upper limit to the level of market share desired by the company: Unit costs at higher levels of production (to meet hJgh? market share goals) may begin to increase, thus reducing overall profitability. Demand fluctuations as a result of economic cycles may make the building up of production capacity in periods of peak demand unde G-able in the long run. It ic therefore likely that the fiim would hiave a desirable range of values for market share and would attempt to remain within this range. Managers would therefore be interested in the allocation of marketing effort in order to achieve the satisficing level of market share. Through a knowledge ,r>fthe company ‘s marketing efforts and competitive marketing efforts or. market share, and the sensitivity of market share to changes in the levels of the vvious inputs, the manager would be able to Develop contingency plans for action. Profit or contribution by product is the other output of the planning system. This is obtained from market share results, demand estimates, and costs of production and marketing. Sensitivity analysis of the profit to variations in demand estimates and corn *ytitive activily enables managers to develop guidelines for changes in thj$:proposed plan under various contingerlcies. This is an important
requirement ;is pointed out by Hopkins [ 15 ] and Maylolr and Schauland [ 161.
Competitiolr Competiticn is modeled exogenously (see [ J7]) with1 the estimates of competitive behavior being provided by users of the model and other knowledgeable pple: within the compa:‘;jr . This is feasible, as was demonstrated in this study, since the product class is an established one and managers can be expecte judgmental inputs regarding compelition.
The Market ResponseModel This is a :;ingle equation multiplicative model that estimates market share by product, given as inputs both the company ‘zl and competitors’ activities in the areas of product qualit , price, distribution, promotion, and customer service. It is an aiggregate response mod/e1 incorporating carryover effects, interactive effects and competition of the form: (MS)ij
=
where i = 1, 2, u . . PZproducts and j = 1, 2, . . . n time periods, Q = i:ldex (If product quality, P = price, e, = index of advert ising effectiveness, A =: advertising expenditure, e, = indfex of selling effectiveness, S = personal sellin,g;expell,lditu.re,ed = index of distribution effectiveness, I) = nilmber of distribution outlets, C = level of customer service, and Q1, PI, AI, SI, DI,and Cl are the corre:iponding average values for the industry. The relevant attributes for obtaining measures of selling effectiveners, advertising effectivenless, distribution effectiveness, Iproduct quality ,, and customer se%ce will vary from industry to industry and have to be developed specifically for mch situations The values for the indices are the weighted average ratiugs on each ch,aracteristic for the compan!r and competitors. The values ofai,Pi,ri,6i,oi, and!&,iare interpreted as elasticities of relative quality, relative price ( etc., and _--_____-___ _____--SUSHILA RAO is Assistant Professor of Marketing at Boston University. She received her M.B.A. from the Indian Institute of Management and her D.5.A. from Indiana Universrb. _-__.____-p __ ___ _--p _----.--~
ere estimated through regression analysis of historical data. In the absence of historical data the marketshare Xsponse model may be specified by obtaining subjective inputs from the managers and using “decision calculus” (Little [ 181) procedures. These procedures have been used successfully in the past in several decision making situations (Lodish [19] Montgomery [Xl]) and ate well documented. Close involvement in building the model would increase lthe understanding of the managers and enhance the implementability of the planning system. STRUCTURE OF THE PLANNING SYW’EM The structure of the planning system is given in Fig. 2 as a3 outliue of the sequence of steps involved in the planning process. The forecasts of industry sales are provided by managers within the company and may or may not be pmvided by the managers involved in the planning process. It is assumed that the forecasts are acceptable to the users of the planning m&L The market share response model is paramcterized using regression analysis or decision calculus, and to ensure its acceptability is tested for its predictive power using the most recent data. The response coefficients are revised through triall and error until the predictions (as compared to actuals) are acceptable to the managers. Off-line review of market share and profitability is done by the users of the marketing system and new inputs are provided in the form of response coefficients and revised marketing plans. For sensitivity analysis, a credible range of values for the levels of marketing effort by the company aind competition are also provided by the users of the system. The advanta.ges of this approach, i.e., interaction of the managers with the computer, is that managers are continually involved in the process of arriving at the plan and are able to intervene whenever they feel new factors have to be incorporated. The interactive process significantly reduces the barrier between the decision maker and the model [ 101. The simulation of changes in market response and the competitive environment enables the managers to conduct “what if” expe, ~l’,zntsand answer “what is ’ * and “what has been *’ types of questions [ 161. This better prepares the manager ~OImaking cant ingency plans. The system relics heavily on empirical estimates of response coefficients as well as subjective inputs from the potential users OFthe planning system in updating response coefficients, etc. The rationale for usir.g a regression model is that objective data should be useu to the 238
extent possible, and the manag& should he provided some reference point on which they can base their judgmental inputs. The data may not be available in full immediately, but recognition of the need for such data would facilitate the development of the necessary management information systems. Once sufficient experience has been accumulated the model can lz extended to a probabilistic version. IMPLEMENTATION lrr implementing the planning system, the first step would be the clear delineation of the product classes and the relevant competition for each product. Agreement shoald be obtained on what level of aggregation (by cus!..omergroup, region, product, etc.) is most practical and useful for managers. In obtaining a credible range of values for the marketing variables, competitor activity, and demand changes, care should be taken to obtain a set large enough to represent the problem adequately yet small enough to be comprehended by the users. It is likely that more than one person is responsible for, and competent to provide, inputs in the different areas, and this is an importantproblem in arriving ai inputs to the model. Kotler [2 1] suggests three approaches to combining the various estimates: 1. let the group experts exchange vie 11sand come up
with a group estimate, rather than several i.ndependent estimates; 2. obtain individual estimates and combine them using some appropriate weighting scheme; and 3. use the ‘ ‘Delp%i Method, ” i.e., obtain individual estimates, supply to each person everybody else’s estimates, feed back the revised estimiates, and so on, until a consensus is reached. The method chosen depends on several firctors. Jolson and Rossow [22] suggest that “the techniqu.e followed is a function of the time available, importance of the decision, the number of qualified judges availsble, and the relative merit placed on alternative procedures by the decision makers. ’ ’ The relevant characteristics for obtaining measures (Tf selling effectiveness, advertising effectiveness, distribL tion effectiveness, and product quality may vary with the situaticsn, and these have to be developed specifically for such situations. The final values for the indices would be weighted average ratings on each characb:ristic for t company and competitors.
1
l
PARE FRalIcm I_!2
lwI(ET StMREs WITH XC’IUALS
“----“1
P_
I
I B,BBB........“...............................
---
rsjii*1SFACTORY MWET
1
SWlRE RESPONSEMIEL
? TIME PERIOD BY PROLIKT,:: -it ASPROVIDED BYME yzl4wNGmGER --_,,:__,,,,,,_,__=====,)I
1
Ei)I~M#t~sttti-tE
KlfttdE%r ‘-;
QUARTERS USING THE RESPCMSE K&EL. AM) INPUT9 OF CCWANY'S AM2COWEilTORS PWECIED MMMETING EFFORT
_--
--l-
*.--____m. I
i I I
I
f I I I
I
; I I I I
ORCONlRIBUTIONBYPRODJCT QmSWITHTtEPROPOSED
JI
SITIVIM ANALYSIS OF MARKET SH4RE To: CWNGES IN RESPONSE COfFFICIENTS -
ii!Kii: Ir:z
i$K%iRSEfF
y=~==:=======:==--Y============~~
I I 1 8 !
4 -==:======='==s='='========_===_ 3 F L FINALPW~ SBYPRODUCT f +!ElWKEI. ;jHARE TISFICING. c : : 84ECWTRI v IONSBYPROWCT, I L____,__,,_,,,,,_, 8 84TISFhClNG =',,3,,+~'~=~~~~-~~~,_---~-,-,,l(l I %NSITIVITY ANALYSIS OF PROFIT OR CWlRIBUllON TO CHANGES IN FORECASTED DEM4ND
l
FWJRE2. The plmnlng system Straight llne IndlcHes “on line”; dlouble fine lndlcates “off Ilne”; dotted line lndkMe:s “feedback.”
AN APPLICA’R’IONOF THE MODEL The market response model was parameterixe context of the marketing operations of a manufacturer of capital goods whose sales were approaching one bill ioni
dollars annually . The products had applications in several markets and the marketing operations were organized b!s market ~ with ii vice president for marketing heading each division. ‘The model was tested in the context of orae division accounting for over 60% of domestic sales and 239
having a sophisticated forecastingsystem in hi& the company executives had faith. The industrywas charact&zed by well established competitors(all three had been in the business for over ten years), fairly standard products and marketing practices. Data were available on market share by units for the firm and competition, by market, for a total of eleven yearsor forty fourquarters.The parameters of the model were estimated using data for thirty two periods and tested using data for the remaining twelve periods. Measures of product quality, advertising effectiveness, distribution effectiveness, and selling effectiveness for the company and competitors were obtained by (i) identifying the relevant attributes of each element thrc dgh preliminary discussions with executives, (ii) developing suitable questionnaires for obtaining the relative importance of the attributes and the ratings of the products on each of the attributes, and (iii) administering the questionnaires to the appropriate personnel within the fii. Ideally the ratings should be obtained from customers but time constraints did not permit this and ratings were obtained from company executives who were closest to the customers and ‘were responsible for the various rnz keting activities. Data were available on prices, advertising expenditures, and the number of dealer outlets. Since the data available did not permit separating the selling expenses by division, the si;:e of the sales force for the firm and co:mpetition was ueed instead. Discussions with company executives suggested that average order cycle time was the best measure of customer service levels in that industry. Dat’aon this were rGadi1yavailable. The data obtained were fitted by the mrthod of least squares to the model
Steps ise regression procedures were used. The results obtaincu were as follows Constant == - 1. 75 06 =I 0.008 ai Pi =: 4.132
yi
6, oI
0.179 = -1.064 = 1.264 = 0.0229
important.Furtherexaminationrevealedthata largepart of carry-overeffects were accountedfor by the strength of the distributionsystem.The variableof personalselling appeared with a negative weight and examination of the correlation matrix indicated that it was behaving as a sblppressor variable [23] in this situation. Althollgh economic theory suggests that “price *’ should appear with a negative weight, the absence of the negative weight was not surprising. In industrialgoods of this naturecustomeis are willing to pay higherprices for betterqualityand
after sales service and may be insensitiveto price differences. In tire firm understudy, executives stronglybelieved that price did not matter much because the differences were small in any case. In the period under investigatieln, product quality and price differences were minor and did not seem to influence market share to a significant extent. This may not persist if firms become more innovative and/or price competitive. Discussions with executives revealed that the delivery times in the industry varied a great deal due to production problems and no company had a sustained superior performance to others in the industry. Customers could not predict their delivery times and could not differentiate among them. They did acknowledge, however, that if any one of the members of the industry adopted the practice of shipping from inventory this would definitely become a relevant factor for decision making. Alternative models omitting the subjective inputs of the managers(product qua tiveness indices) reduced the R” to ,394 attesting to the value of subjective inputs in improving the goodness of fit.
The model explains and preciicts market phenomena.
=
R* = 0. ‘bre was no evidence of serial correlation and “distritvation” contributed most to R* explaining 52% of the variance. Lagged market share did not appear to be
The modei both explains and predicts market phenomena. The mode1 was tested to see how well it would predict the values for periods 36,40, and 44. The correla[ion betweer, estimated and actual market shares turned out to be only 0.623. The regression equation was upidated by adding more recent data (and dropping a similar number of periods from earlier data) in an attempt to
improve the predictive power. Predictions of’ market share for the periods 37-40 based on regression csefficients obtained from data for the periods 5-36 resulted in only a slight improvement in the correlation between estimated and actual market shares to 0.67 I. It was hypothesized that the parameter values were not stable but changed significantly over time, and the Chow test confirmed this hypothesis. Examination of the raw data revealed that the market share ,for the firm in the period 30 dropped abruptly by 12% (as it:turned out this was due to serious labor problems) with no corresponding changes of that order in the marketing v*tiables. Fitting the model to data for the periods 3 l-44 resulted in an R* of 0.824 and customer service, a hitherto i nsignificant variable explained 28% of the variance at significance level of O.O!i. Price became significant, adding 0.22 to the value of R* and personal selling and product quality became more dominant. Discussion with executives zvealed that there. was some activity on the price and product quality fronts at that time. Thus, one could conclude that the nature of competition had changed over the period and therefore the structural relationship had changed. In order to take these shifts into account the model has to use the most recent data to estimate the response coefficients, i .e . , sample from the right temporal period or adjust them through a process of trial and error. A computer program coded in Fortran IV was developed, following the structure of the planning system as outlined in Fig. 2. It was used only in the bate h mode (due to the circumstances under which the model was aperationalized) but can be easily converted to the inlteractive mode 1241. It provides for (1) adjusting the number of products. (2) sensitivity analysis of the impact IDffluctuations in industry demand on profit, (3) changes .n production costs per unit with change in the levels of production, (4) changing the response coefficient:; of any of the variables at any time during the planning period, iind (5) changing the values for the marketing variables of the firm and/or for competitors. Some experimenial runs ‘Jveremade by changing the values of the inputs to demonstrate the ease with which the user can investigate the impact of alternative marketing mixes on market share ilud profit to the executives of the firm under study. ALUA‘TION OF THE PLANNING SYSTEM The re;lc:ions of the executives of the fiim were sought in an attempt to evaluate the proposed planning system. They indic;ited that the model was comprehensi qe and in:orporated the relevant marketing variables without
getting overly complicated. They were comfortable with the procedures employed to o’rtain their inputs in tiperationalizing the ,model. The model could doubtlesn have been made mart: meaningful to the users if data were available at a lower level of aggregation, e.g., on a product category basis. The results clearly showed that the distribution variable is the single most important determinant of the market share, confirming the widely held view within the company that their strengG in distribution was responsible for their level of market share. Qualitative effects contributed to the explanation of variance as expected. These findings attest to the explanatory power of the model. The model performed fairly well in predicting market share for four periods (one year). Predictions after that were poor tut this was explained by the change in market condition s. The model can be made respcnsive to changes from one period to the next with inputs from the managers. An additicnal benefit lies in the exercise of trying to reconcile the differences between actual and predicted market shares. This would help to identify new problems and lead to the specification of new models and additional data collectioln. CONCLUSION As can be seen from the abdve description of the planning system. it is both desirable and feasible to study marketing problems in the industrial conter_t using mart: comprehensive models than have generally been used in the past. The model structure was acceptable to the executives and was c’perationalized with their close involvement in the pro~ss. It was adjusted to meet the specific organizational constraints and data requiremn-ts and was considered imple mentable. REFERENCES I. Starr, M. K., “Pliinning Models”, Munugement
Scrrnce, 115- 141 (De-
cember 1966). 2. Dickson, G. W., rdauriel , J. J., and Anderson. Computer Assisted Planning Models: A Fu lctional Analysis. in Curponzte Sidution Mociels. A. N. Screiber, Ed. Seattle: University of Washington Press. 1970. pp.
43-70. 3. Ackoff, R., A Concept of Corporate Planning, Long Rmge Pluming.
1-8
(Septemher 1970). 4. Mintzberg, H., Strategy Making in Three Modes, Chlforniu rnent Review. 44-!;3 (Winter 1973).
Munuge-
_5 . Naylor, T. H., alld Scheruland. H., A Survey of Users of Corp+Jrdte PLuming Models, ,Munugtment Science. 927-937 (May 1976).
6. Johansson, 3. K., and Sambare, S., Modeling the Mark&n
Industrial Product, Working Paper, University of Illinois, 1975. 7. Little, J. D. C., h’8odelsand Managers: The Concept of Decision Cdculus, Munagemenr Sviewe, 466-485 (April 1970).
t-
8. Choffkay, J., and LilLn, ‘3. L., AsseesingItespon~~to fnd ing Strategy, Jourrral oj%furkring, 20-3 I (April 1978). 9 Lotshaw, E. IP., hlustriai Marketing: T&Ids and sledges,
Murk&g.
Jownul
18. Little, J. D. C.,
of
22-24 &uwaty 1970).
19 Aaker, D. A.,
Planning .%ldels , Journu1
of Marketing, 16-;!3 (Gctober 1975).
11 Udell, J. G., SucceqV Marketing Strategies in American fn&st~. Stevens hints: Worzalla Publishing Co., 1972. 12 Lehmann. D. R., and O’Shaughnessy. J., Differewx in Attribute Importance fo, Different Industrial products, Journal of Murketing, 36-42 (April 1974).
21. Kotk,
15. Hopkins, D. S., T/w Shert Term Marketing Plan, Conference Board Report No. 565, New York, 1972.
Budtess Horizons *
1970).
22. Jolson, M. A., ad Rassow. G. L., The Delphi Process in Marketing Decision Making, Journd of Marketing Research. 443-448 (November
I 3. Schultz, R. L.. MEvketMeasurement and Planning With a Simul Equation MuxIel,Jrrurnal of Marketing Research, 153- 164 (May 14. Lambin, J. J., OprimalAllocation of CompetitiveMarketmgEfforts:An Empirical Study, 4ournaf of Business, 468-484 (October 1970).
P.. A. Guiie to Gathering Expert EStiMWS,
79-87 (0~~s
in Research and 24.
ctisc,
trial Products i’tilizing D Dynamic Competitive Market Response Model, Unpublished Doctoral Dissertation, Indiana University, 1977.
This article reports on the development of TVmarketing model for industrial psoducts to aid marketing managers ir. developing marketing programs on a quarterly and yearly basis. The system incorporates a market response model Mqhrch relies heavily on the involvement of managers in providing inputs. The model wa>:parameterized and tested in the COltFtiW of a large manufacturer of capital goods. The results Irlere supportive of tht* conceptual roundness of the mot 4 anti its utility in decision making.
Formal planning is relatively new on the business scene an&to date neither its theory nor methodology is well developed. Several conceptual papers [A-41 on corporate plarnningsystems have appeared in the literature but detailed methodological discussions are sparse. Naylor [5] repc#rted on a survey of planning in 346 firms in which hr::found that although most firms were turning to corporate: planning models, most models had major shortcomings such as lack of flexibility, poor documentation, or excessive data requirements. Literature on marketing plaaning has dealt mostly with packaged con-
Address comzpndence to: Sushi13Rao, bchool of Managemu& Boston university, 704 Commonwealth Aver’ue, Boston, MA 022 15. Management 10, 235-242 ( I98I ) @Elsevier North Holland, Inc., 1981 52 VanderbiltAve., New York, New York 10017
Industrial Markrimg
Rae
sumer goods (with exceptions such as Johansson md Sambare [631,Little [7], and Choffray and Lillien [?I]), amd models fi)r the mos:: part have remained simplistic and unreali&. Some of the more complex models have not been emipirically tested and their validity has not been established. ‘The need for comprehensive markeling planning mod& is obvious. Lotshaw [!I] wrote: “The emphasis on marketing planning for industrial products ;rppears to be destined to increase in future, although it is currently not well established. Forma marketing planning as a part of total business planning will assist in dzveloping and implementing the precise objectives which are required in today’s increasingly complex markets arcdin identifying the profit implications of various marketing actions ”
Given the complexity of the product and marketplace however, planning models have to be tailor made to each organization. Close: involvement of the managers in the development aLlso makes it easier to implement thie model. Aaker and Weinberg [ IO] suggest that interactive :!;ystemstend to create an invplved decision maker who in turn will contr bute to the model building process. This in turn will affect the probability of the model bei:lg accepted by the organization. This article reports on an attempt to dev:llop and test a realistic and comprehensi\,e marketing planning system for a firm dealing in capital
goods, which captures the complexity of the planning process while remaining flexible and simple enough to be acceptable ‘to, and implementable by, industrial organizations. The planning system described here focuses on marketing m’.x decisions and is designed to be of use to marketing managers, produce managers, or planning managers as an aid in designing the narketing program on a quarttzrly and yearly basis and in making strategy decisions for a three year period. The market response model used enables the manager to (examine the implications of alternative marketing mixes in terms of market share and profit. The desired levels cz” the marketing variables are determined by the users of the model and are not arrived at through any optimization procedures. The system is designed to adapt to changes in the enviibnment szndcan be extended to various levels within the organization. A description of the marketing system modeled would be in order here to aid the reader in understanding the complexity of the task and to explain the choice of the marketing variables. The marketing system as depicted in Fig. 1 is that ~3:a large ca&al goods manufacturer operaking in several markets. The
products are distributed directly to original equipment manufacturers (0.E.M.s) and the spare parts are distributed both through the dealers of the manufacturer and the dealers of the 0.E.M.s. The firm operates under stic market conditions and the marketing variables employed at each level of the systLm are shown. variables for industrial products
market segment. ELEIWWS
OF THE PLANNING SYSTEM
The Phnning Ho The plm‘ning horizon is a three year period divided into quarters. Industrial goods manufacturers generally view the planing task in this manner, since the nature of the demand for their products and their production con-
P,C,I
P,C
hS,C
A,Q:S,D
D z DISTRIBUTION P - PRICE Q - QUALITY
S - PERSONAL SELLING A _ ADVERTISING C- MTOMER SE:RV3 CE FIGbHE 1. The marketing syskm.
236
straints require planning beyond one year. At the same time, the economic environment which influences the demand for their products is not predictable with any level of confidence beyoncl a three year period.
Demand The demand for the product is dertved demand and it is assumed that the marketing effort of the industry has such a small effect on industry sales that it can be neglected for planning purposes. The forecasted demand for the industry is assumed to be available from forecasts made external to the system; a fairly reasonable assumption to make for large companies.
Output of the System Market share is the primary output since each firm’s marketing efforts only affect its share of the total market and not the demand. As Schultz [13] and Lambin [ 141 point out, this offers the advantage of eliminating the impact of socioeconomic factors that have the same influence on all the fiis in the industry. Competitive effects are also automatically taken into consideration. The company may not have the objective of maximizing market share, however, for several reasons. Regulatory constraints would set an upper limit to the level of market share desired by the company: Unit costs at higher levels of production (to meet hJgh? market share goals) may begin to increase, thus reducing overall profitability. Demand fluctuations as a result of economic cycles may make the building up of production capacity in periods of peak demand unde G-able in the long run. It ic therefore likely that the fiim would hiave a desirable range of values for market share and would attempt to remain within this range. Managers would therefore be interested in the allocation of marketing effort in order to achieve the satisficing level of market share. Through a knowledge ,r>fthe company ‘s marketing efforts and competitive marketing efforts or. market share, and the sensitivity of market share to changes in the levels of the vvious inputs, the manager would be able to Develop contingency plans for action. Profit or contribution by product is the other output of the planning system. This is obtained from market share results, demand estimates, and costs of production and marketing. Sensitivity analysis of the profit to variations in demand estimates and corn *ytitive activily enables managers to develop guidelines for changes in thj$:proposed plan under various contingerlcies. This is an important
requirement ;is pointed out by Hopkins [ 15 ] and Maylolr and Schauland [ 161.
Competitiolr Competiticn is modeled exogenously (see [ J7]) with1 the estimates of competitive behavior being provided by users of the model and other knowledgeable pple: within the compa:‘;jr . This is feasible, as was demonstrated in this study, since the product class is an established one and managers can be expecte judgmental inputs regarding compelition.
The Market ResponseModel This is a :;ingle equation multiplicative model that estimates market share by product, given as inputs both the company ‘zl and competitors’ activities in the areas of product qualit , price, distribution, promotion, and customer service. It is an aiggregate response mod/e1 incorporating carryover effects, interactive effects and competition of the form: (MS)ij
=
where i = 1, 2, u . . PZproducts and j = 1, 2, . . . n time periods, Q = i:ldex (If product quality, P = price, e, = index of advert ising effectiveness, A =: advertising expenditure, e, = indfex of selling effectiveness, S = personal sellin,g;expell,lditu.re,ed = index of distribution effectiveness, I) = nilmber of distribution outlets, C = level of customer service, and Q1, PI, AI, SI, DI,and Cl are the corre:iponding average values for the industry. The relevant attributes for obtaining measures of selling effectiveners, advertising effectivenless, distribution effectiveness, Iproduct quality ,, and customer se%ce will vary from industry to industry and have to be developed specifically for mch situations The values for the indices are the weighted average ratiugs on each ch,aracteristic for the compan!r and competitors. The values ofai,Pi,ri,6i,oi, and!&,iare interpreted as elasticities of relative quality, relative price ( etc., and _--_____-___ _____--SUSHILA RAO is Assistant Professor of Marketing at Boston University. She received her M.B.A. from the Indian Institute of Management and her D.5.A. from Indiana Universrb. _-__.____-p __ ___ _--p _----.--~
ere estimated through regression analysis of historical data. In the absence of historical data the marketshare Xsponse model may be specified by obtaining subjective inputs from the managers and using “decision calculus” (Little [ 181) procedures. These procedures have been used successfully in the past in several decision making situations (Lodish [19] Montgomery [Xl]) and ate well documented. Close involvement in building the model would increase lthe understanding of the managers and enhance the implementability of the planning system. STRUCTURE OF THE PLANNING SYW’EM The structure of the planning system is given in Fig. 2 as a3 outliue of the sequence of steps involved in the planning process. The forecasts of industry sales are provided by managers within the company and may or may not be pmvided by the managers involved in the planning process. It is assumed that the forecasts are acceptable to the users of the planning m&L The market share response model is paramcterized using regression analysis or decision calculus, and to ensure its acceptability is tested for its predictive power using the most recent data. The response coefficients are revised through triall and error until the predictions (as compared to actuals) are acceptable to the managers. Off-line review of market share and profitability is done by the users of the marketing system and new inputs are provided in the form of response coefficients and revised marketing plans. For sensitivity analysis, a credible range of values for the levels of marketing effort by the company aind competition are also provided by the users of the system. The advanta.ges of this approach, i.e., interaction of the managers with the computer, is that managers are continually involved in the process of arriving at the plan and are able to intervene whenever they feel new factors have to be incorporated. The interactive process significantly reduces the barrier between the decision maker and the model [ 101. The simulation of changes in market response and the competitive environment enables the managers to conduct “what if” expe, ~l’,zntsand answer “what is ’ * and “what has been *’ types of questions [ 161. This better prepares the manager ~OImaking cant ingency plans. The system relics heavily on empirical estimates of response coefficients as well as subjective inputs from the potential users OFthe planning system in updating response coefficients, etc. The rationale for usir.g a regression model is that objective data should be useu to the 238
extent possible, and the manag& should he provided some reference point on which they can base their judgmental inputs. The data may not be available in full immediately, but recognition of the need for such data would facilitate the development of the necessary management information systems. Once sufficient experience has been accumulated the model can lz extended to a probabilistic version. IMPLEMENTATION lrr implementing the planning system, the first step would be the clear delineation of the product classes and the relevant competition for each product. Agreement shoald be obtained on what level of aggregation (by cus!..omergroup, region, product, etc.) is most practical and useful for managers. In obtaining a credible range of values for the marketing variables, competitor activity, and demand changes, care should be taken to obtain a set large enough to represent the problem adequately yet small enough to be comprehended by the users. It is likely that more than one person is responsible for, and competent to provide, inputs in the different areas, and this is an importantproblem in arriving ai inputs to the model. Kotler [2 1] suggests three approaches to combining the various estimates: 1. let the group experts exchange vie 11sand come up
with a group estimate, rather than several i.ndependent estimates; 2. obtain individual estimates and combine them using some appropriate weighting scheme; and 3. use the ‘ ‘Delp%i Method, ” i.e., obtain individual estimates, supply to each person everybody else’s estimates, feed back the revised estimiates, and so on, until a consensus is reached. The method chosen depends on several firctors. Jolson and Rossow [22] suggest that “the techniqu.e followed is a function of the time available, importance of the decision, the number of qualified judges availsble, and the relative merit placed on alternative procedures by the decision makers. ’ ’ The relevant characteristics for obtaining measures (Tf selling effectiveness, advertising effectiveness, distribL tion effectiveness, and product quality may vary with the situaticsn, and these have to be developed specifically for such situations. The final values for the indices would be weighted average ratings on each characb:ristic for t company and competitors.
1
l
PARE FRalIcm I_!2
lwI(ET StMREs WITH XC’IUALS
“----“1
P_
I
I B,BBB........“...............................
---
rsjii*1SFACTORY MWET
1
SWlRE RESPONSEMIEL
? TIME PERIOD BY PROLIKT,:: -it ASPROVIDED BYME yzl4wNGmGER --_,,:__,,,,,,_,__=====,)I
1
Ei)I~M#t~sttti-tE
KlfttdE%r ‘-;
QUARTERS USING THE RESPCMSE K&EL. AM) INPUT9 OF CCWANY'S AM2COWEilTORS PWECIED MMMETING EFFORT
_--
--l-
*.--____m. I
i I I
I
f I I I
I
; I I I I
ORCONlRIBUTIONBYPRODJCT QmSWITHTtEPROPOSED
JI
SITIVIM ANALYSIS OF MARKET SH4RE To: CWNGES IN RESPONSE COfFFICIENTS -
ii!Kii: Ir:z
i$K%iRSEfF
y=~==:=======:==--Y============~~
I I 1 8 !
4 -==:======='==s='='========_===_ 3 F L FINALPW~ SBYPRODUCT f +!ElWKEI. ;jHARE TISFICING. c : : 84ECWTRI v IONSBYPROWCT, I L____,__,,_,,,,,_, 8 84TISFhClNG =',,3,,+~'~=~~~~-~~~,_---~-,-,,l(l I %NSITIVITY ANALYSIS OF PROFIT OR CWlRIBUllON TO CHANGES IN FORECASTED DEM4ND
l
FWJRE2. The plmnlng system Straight llne IndlcHes “on line”; dlouble fine lndlcates “off Ilne”; dotted line lndkMe:s “feedback.”
AN APPLICA’R’IONOF THE MODEL The market response model was parameterixe context of the marketing operations of a manufacturer of capital goods whose sales were approaching one bill ioni
dollars annually . The products had applications in several markets and the marketing operations were organized b!s market ~ with ii vice president for marketing heading each division. ‘The model was tested in the context of orae division accounting for over 60% of domestic sales and 239
having a sophisticated forecastingsystem in hi& the company executives had faith. The industrywas charact&zed by well established competitors(all three had been in the business for over ten years), fairly standard products and marketing practices. Data were available on market share by units for the firm and competition, by market, for a total of eleven yearsor forty fourquarters.The parameters of the model were estimated using data for thirty two periods and tested using data for the remaining twelve periods. Measures of product quality, advertising effectiveness, distribution effectiveness, and selling effectiveness for the company and competitors were obtained by (i) identifying the relevant attributes of each element thrc dgh preliminary discussions with executives, (ii) developing suitable questionnaires for obtaining the relative importance of the attributes and the ratings of the products on each of the attributes, and (iii) administering the questionnaires to the appropriate personnel within the fii. Ideally the ratings should be obtained from customers but time constraints did not permit this and ratings were obtained from company executives who were closest to the customers and ‘were responsible for the various rnz keting activities. Data were available on prices, advertising expenditures, and the number of dealer outlets. Since the data available did not permit separating the selling expenses by division, the si;:e of the sales force for the firm and co:mpetition was ueed instead. Discussions with company executives suggested that average order cycle time was the best measure of customer service levels in that industry. Dat’aon this were rGadi1yavailable. The data obtained were fitted by the mrthod of least squares to the model
Steps ise regression procedures were used. The results obtaincu were as follows Constant == - 1. 75 06 =I 0.008 ai Pi =: 4.132
yi
6, oI
0.179 = -1.064 = 1.264 = 0.0229
important.Furtherexaminationrevealedthata largepart of carry-overeffects were accountedfor by the strength of the distributionsystem.The variableof personalselling appeared with a negative weight and examination of the correlation matrix indicated that it was behaving as a sblppressor variable [23] in this situation. Althollgh economic theory suggests that “price *’ should appear with a negative weight, the absence of the negative weight was not surprising. In industrialgoods of this naturecustomeis are willing to pay higherprices for betterqualityand
after sales service and may be insensitiveto price differences. In tire firm understudy, executives stronglybelieved that price did not matter much because the differences were small in any case. In the period under investigatieln, product quality and price differences were minor and did not seem to influence market share to a significant extent. This may not persist if firms become more innovative and/or price competitive. Discussions with executives revealed that the delivery times in the industry varied a great deal due to production problems and no company had a sustained superior performance to others in the industry. Customers could not predict their delivery times and could not differentiate among them. They did acknowledge, however, that if any one of the members of the industry adopted the practice of shipping from inventory this would definitely become a relevant factor for decision making. Alternative models omitting the subjective inputs of the managers(product qua tiveness indices) reduced the R” to ,394 attesting to the value of subjective inputs in improving the goodness of fit.
The model explains and preciicts market phenomena.
=
R* = 0. ‘bre was no evidence of serial correlation and “distritvation” contributed most to R* explaining 52% of the variance. Lagged market share did not appear to be
The modei both explains and predicts market phenomena. The mode1 was tested to see how well it would predict the values for periods 36,40, and 44. The correla[ion betweer, estimated and actual market shares turned out to be only 0.623. The regression equation was upidated by adding more recent data (and dropping a similar number of periods from earlier data) in an attempt to
improve the predictive power. Predictions of’ market share for the periods 37-40 based on regression csefficients obtained from data for the periods 5-36 resulted in only a slight improvement in the correlation between estimated and actual market shares to 0.67 I. It was hypothesized that the parameter values were not stable but changed significantly over time, and the Chow test confirmed this hypothesis. Examination of the raw data revealed that the market share ,for the firm in the period 30 dropped abruptly by 12% (as it:turned out this was due to serious labor problems) with no corresponding changes of that order in the marketing v*tiables. Fitting the model to data for the periods 3 l-44 resulted in an R* of 0.824 and customer service, a hitherto i nsignificant variable explained 28% of the variance at significance level of O.O!i. Price became significant, adding 0.22 to the value of R* and personal selling and product quality became more dominant. Discussion with executives zvealed that there. was some activity on the price and product quality fronts at that time. Thus, one could conclude that the nature of competition had changed over the period and therefore the structural relationship had changed. In order to take these shifts into account the model has to use the most recent data to estimate the response coefficients, i .e . , sample from the right temporal period or adjust them through a process of trial and error. A computer program coded in Fortran IV was developed, following the structure of the planning system as outlined in Fig. 2. It was used only in the bate h mode (due to the circumstances under which the model was aperationalized) but can be easily converted to the inlteractive mode 1241. It provides for (1) adjusting the number of products. (2) sensitivity analysis of the impact IDffluctuations in industry demand on profit, (3) changes .n production costs per unit with change in the levels of production, (4) changing the response coefficient:; of any of the variables at any time during the planning period, iind (5) changing the values for the marketing variables of the firm and/or for competitors. Some experimenial runs ‘Jveremade by changing the values of the inputs to demonstrate the ease with which the user can investigate the impact of alternative marketing mixes on market share ilud profit to the executives of the firm under study. ALUA‘TION OF THE PLANNING SYSTEM The re;lc:ions of the executives of the fiim were sought in an attempt to evaluate the proposed planning system. They indic;ited that the model was comprehensi qe and in:orporated the relevant marketing variables without
getting overly complicated. They were comfortable with the procedures employed to o’rtain their inputs in tiperationalizing the ,model. The model could doubtlesn have been made mart: meaningful to the users if data were available at a lower level of aggregation, e.g., on a product category basis. The results clearly showed that the distribution variable is the single most important determinant of the market share, confirming the widely held view within the company that their strengG in distribution was responsible for their level of market share. Qualitative effects contributed to the explanation of variance as expected. These findings attest to the explanatory power of the model. The model performed fairly well in predicting market share for four periods (one year). Predictions after that were poor tut this was explained by the change in market condition s. The model can be made respcnsive to changes from one period to the next with inputs from the managers. An additicnal benefit lies in the exercise of trying to reconcile the differences between actual and predicted market shares. This would help to identify new problems and lead to the specification of new models and additional data collectioln. CONCLUSION As can be seen from the abdve description of the planning system. it is both desirable and feasible to study marketing problems in the industrial conter_t using mart: comprehensive models than have generally been used in the past. The model structure was acceptable to the executives and was c’perationalized with their close involvement in the pro~ss. It was adjusted to meet the specific organizational constraints and data requiremn-ts and was considered imple mentable. REFERENCES I. Starr, M. K., “Pliinning Models”, Munugement
Scrrnce, 115- 141 (De-
cember 1966). 2. Dickson, G. W., rdauriel , J. J., and Anderson. Computer Assisted Planning Models: A Fu lctional Analysis. in Curponzte Sidution Mociels. A. N. Screiber, Ed. Seattle: University of Washington Press. 1970. pp.
43-70. 3. Ackoff, R., A Concept of Corporate Planning, Long Rmge Pluming.
1-8
(Septemher 1970). 4. Mintzberg, H., Strategy Making in Three Modes, Chlforniu rnent Review. 44-!;3 (Winter 1973).
Munuge-
_5 . Naylor, T. H., alld Scheruland. H., A Survey of Users of Corp+Jrdte PLuming Models, ,Munugtment Science. 927-937 (May 1976).
6. Johansson, 3. K., and Sambare, S., Modeling the Mark&n
Industrial Product, Working Paper, University of Illinois, 1975. 7. Little, J. D. C., h’8odelsand Managers: The Concept of Decision Cdculus, Munagemenr Sviewe, 466-485 (April 1970).
t-
8. Choffkay, J., and LilLn, ‘3. L., AsseesingItespon~~to fnd ing Strategy, Jourrral oj%furkring, 20-3 I (April 1978). 9 Lotshaw, E. IP., hlustriai Marketing: T&Ids and sledges,
Murk&g.
Jownul
18. Little, J. D. C.,
of
22-24 &uwaty 1970).
19 Aaker, D. A.,
Planning .%ldels , Journu1
of Marketing, 16-;!3 (Gctober 1975).
11 Udell, J. G., SucceqV Marketing Strategies in American fn&st~. Stevens hints: Worzalla Publishing Co., 1972. 12 Lehmann. D. R., and O’Shaughnessy. J., Differewx in Attribute Importance fo, Different Industrial products, Journal of Murketing, 36-42 (April 1974).
21. Kotk,
15. Hopkins, D. S., T/w Shert Term Marketing Plan, Conference Board Report No. 565, New York, 1972.
Budtess Horizons *
1970).
22. Jolson, M. A., ad Rassow. G. L., The Delphi Process in Marketing Decision Making, Journd of Marketing Research. 443-448 (November
I 3. Schultz, R. L.. MEvketMeasurement and Planning With a Simul Equation MuxIel,Jrrurnal of Marketing Research, 153- 164 (May 14. Lambin, J. J., OprimalAllocation of CompetitiveMarketmgEfforts:An Empirical Study, 4ournaf of Business, 468-484 (October 1970).
P.. A. Guiie to Gathering Expert EStiMWS,
79-87 (0~~s
in Research and 24.
ctisc,
trial Products i’tilizing D Dynamic Competitive Market Response Model, Unpublished Doctoral Dissertation, Indiana University, 1977.