forecasting: issues & challenges for marketing management · pdf fileforecasting: issues...

24 / Journal of Marketing, October 1977

Spyros Makridakis and Steven C. Wheelwright

Forecasting: Issues & Challengesfor Marketing ManagementA framework for relating the available techniques to specific situations.

F orecasting plays an important role in everymajor functional area of business management.

In the area of marketing however, forecasting isdoubly important; not only does it have a centralrole in marketing itself, but marketing-developedforecasts play a key role in the planning of produc-tion, finance, and other areas of corporate activity.The importance of forecasting has become morewidely acknowledged in the recent past due tosubstantial changes in the economic environment.The shortages and the increased inflation of theearly 197O's, followed by a major recession, havefocused renewed attention on forecasting and thebenefits it can provide.

At the same time, there still exists a substan-tial gap between applications and what is bothdesirable and attainable."«' ••»• ^^ An examination ofthe forecasting and marketing literature suggeststhat one of the things that is needed, if the fullpotential of forecasting is to be realized, is a struc-ture for dealing with the issues that the practitionermust address. The purpose of this article is to bringtogether much of what is already known and tosupply a framework that will provide guidance forthe marketer in applying this knowledge to eachsituation and in focusing on what additionalknowledge is needed.

Toward this end, the article is divided intofour sections:

About the AuthorsSPYROS MAKRIDAKIS is Professor of ManagementScience at INSEAD, Fontainebleau, France.STEVEN C. WHEELWRIGHT is Associate Professor ofBusiness Administration, Graduate School of Busi-ness Administration, Harvard University, Boston.

1.

2.

The first deals with the range of forecastingmethodologies available and their characteris-tics. The framework used for this is somewhatdifferent from those frequently suggested, andis one that the authors have found to be par-ticularly useful for dealing with marketingforecasting problems.

The second section then builds upon thatmethodological classification in examiningthose issues that relate to the selection of aforecasting methodology for a particular situa-tion. Since any marketing application of fore-casting requires some explicit decision as tothe methodology to be used, this is one of thekey areas where the marketing manager canexert leverage on the potential effectivenessthat forecasting can have in his own situation.The third section deals with major issues andchallenges that are more broadly based thanmethodological selection questions. These re-late to the organizational, behavioral andtechnical characteristics of the environmentwithin which forecasting for marketing musttake place.

Building on these key issues and challenges,the fourth section outlines areas of researchthat are central to continued improvement inforecasting for marketing.

Available MethodologiesThere are several dimensions that can be used ingrouping existing forecasting methodologies. Manyof these are technical in their orientation. For ex-

3.

4.

Forecasting: Issues & Challenges / 25

ample, the distinction between statistical methodsand non-statistical methods might be considered,or that between time series methods and causalmethods. Still another technical distinction can bemade between those methods that are quantitativein their orientation and those that are qualitative.

A somewhat different framework is that sug-gested by Chambers, et al,""' which is based moreon the functional use of forecasting than on themathematical characteristics of the techniques. Thisframework uses the marketing concept of the prod-uct life-cycle to identify the important characteris-tics of forecasting situations at different stages in aproduct's development. Those characteristics arethen matched with the characteristics of differentmethodologies to determine the methodologiesmost appropriate for each different stage. Whilesuch a structure provides some insight into therange of situations where marketers can take advan-tage of forecasting and many of the techniquesavailable, the overlap among methodologies makesit difficult to progress very far in selecting thosemost appropriate for various stages in a product'sdevelopment.

Structure for CategorizingBased on our own experience in marketing andother functional areas, we have found a more tech-nical structure, like that shown in Exhibit 1, to beparticularly useful in categorizing forecastingmethodologies.

As can be seen from the left-hand side of theexhibit, a number of levels can be used in distin-guishing such techniques. The most general dis-tinction is between informal forecasting approachesand formal forecasting methods. The former arebased largely on intuitive feel and lack systematicprocedures that would make them easily transfer-able for application by others. The formal forecast-ing methodologies seek to overcome this weaknessby systematically outlining the steps to be followedso that they can be repeatedly applied to obtainsuitable forecasts in a range of situations.

At second level, formal methodologies can bedivided into those that are qualitative in natureand those that are quantitative. The quantitativemethods, in turn, can be subdivided into the cate-gories of time series techniques and causal or re-gression techniques. The qualitative segment alsoincludes two categories:

• Techniques based on subjective assessment(the judgment of managers).

• Techniques based on the forecasting of tech-nological developments.

To understand the range of forecasting meth-odologies available, several aids are at hand. Threeof these are summarized as part of Exhibit 1:1. First is some understanding of the historical

development of different methodologies.Generally, those techniques developed bystatisticians are quite different in their proper-ties and in the situations for which they arebest suited, from those developed by econo-mists or operations researchers. Exhibit 1 in-dicates the major field of development for eachof the various forecasting methodologies andprovides references from the literature that il-lustrates the development of each.

2. A second item of value in understandingavailable forecasting methodologies is empiri-cal research on their frequency and range ofapplication. Exhibit 1 summarizes the resultsgiven in four separate studies. These studiessuggest that there are substantial differencesamong organizations as to their knowledgeand application of various methodologies, aswell as a wide range in the applicability ofthose methodologies generally.

3. A third item of benefit to the practitioner seek-ing to further develop his own ability at fore-casting is available literature that provides anoverview of existing methodologies and theircharacteristics. Exhibit 1 includes several ref-erences that have been chosen because of theiremphasis on the significance of meth-odological characteristics for the forecastingpractitioner. These are to be distinguishedfrom more technical literature that concen-trates on describing the mathematical charac-teristics of available methodologies.

Matching Situation & MethodologyA key reason for seeking to understand a range ofmethodologies is that the effective utilization offorecasting requires matching the characteristics ofthe marketing situation with the characteristics ofan appropriate methodology. A number of differentcriteria have been suggested as a basis for makingsuch a selection decision. These include accuracy,the time horizon of forecasting, the value of fore-casting, the availability of data, the type of datapattern, and the experience of the practitioner atforecasting.

One of the key characteristics of a forecastingsituation that can often be captured in the timehorizon dimension is the type of data pattern.Forecasters have found it useful to distinguish fourmain types of pattern: trend, seasonal, cyclical, and


EXHIBIT 1Approaches to Forecasting

Approaches Short Description

Informal Forecasting

L F

OR

EC

AS

TIN

G M

ET

HO

DO

LO

GIE

SFO

RM

A

1 O

uan

tita

tive

Met

ho

ds

1 O

ual

itat

ive

Met

ho

ds

1 C

ausa

l or

Reg

ress

ive

Tim

e S

erie

sS

ub

ject

ive

Ass

essm

ent

Tec

hn

olo

gic

al

Single and MultipleRegression

Econometric Models

Naive

Trend Extrapolation

Exponential Smoothing

Decomposition

Filters

Autoregressive/MovingAverages (ARMA), (Box-Jenkins Methodology)

Decision Trees

Salesforce estimates

Juries of ExecutiveOpinion

Surveys AnticipatoryResearch

Exploration

Normative

Ad hoc, judgmentai or intuitive methods.

Variations in dependent variables are explained byvariations in the independent one(s).

Simultaneous systems of multiple regression equations.

Simple rules such as: forecast equals most recentactual value or equals last year's same month + 5%.

Linear, exponential, S-curve, or other types ofprojections.

Forecasts are obtained by smoothing, averaging, pastactual values in a linear or exponential manner.

A time series is "broken" down into trend,seasonability, cyclicality and randomness.

Forecasts are expressed as a linear combination ofpast actual values. Parameters or model can "adapt"to changes in data.

Forecasts are expressed as a linear combination ofpast actual values and/or past errors.

Subjective probabilities are assigned to each eventand the approach of Bayesian Statistics is used.

A bottom-up approach aggregating salesmen'sforecasts.

Marketing, production and finance executives jointlyprepare forecasts.

Learning about intentions of potential customers orplanes of businesses.

Uses today's assured basis of knowledge to broadlyassess conditions of the future.

Starts with assessing future goals, needs, desires,objectives, etc. and works backwards to determinenecessary developments to achieve goals, etc.

Statistics

6, 98

74, 80

Engineering

38, 39

• A Immediate (less than one month) B Short (one to three months) C Medium (three months to less than 2 years) D Long (two years or more)


Major Field of Development

OperationsResearch

8, 33

8, 97, 33

Economics

18, 36

36, 42

Long RangePlanning

81, 82,51, 59

34, 48, 26

34, 84, 101

Practice ofForecasting

54, 62

88, 99, 93

88, 48, 37

16, 79, 63

% Usage inDifferent Surveys

15

17%

36%

37%

48%

52%

37%

95

76%

65%

75%

40%

74%

82%

88

50%

40%

42%

4%

23

50%

50%

FurtherReadings

LBibllographyJ

73, 55, 18, 36

73, 55, 89, 36,42, 43, 19

J

y 62, 64, 54,55, 96, 8

81, 82, 62, 59,55

1, 55, 96, 63,31, 32

49, 67, 66, 546

76, 80, 96

J

. 48, 88, 99

. 4, 9, 23, 37,74

Time Horizonsof Forecasing

A• BI

1Jc

11

D

_

_

—

_

_

_

1Boldface numerals refer to Bibliography. Legend: | H Extensive H Medium use Hil l Limited use


An Ov2xv\zw of FrequentiK Used Quantltatli^e Forecasting/MethodsThe most frequently used time series methods of forecasting are

based on the notion of assigning weights to recent observations of theitem to be forecast (e.g., sales or shipments) and then using the weightedsum of those observed (actual) values as the forecast. Much of the varietyin available time series methods is simply due to the number of differentapproaches for determining the set of weights that will be applied.

Nali^e/MethodsOne of the simplest time series

methods is Naive I. This method usesthe most recentiy observed value as aforecast. Thus, if product demand forthe coming weei< were to be predicted,the observed vaiue of demand for themost recent weei< would be used asthat forecast. This is equivaient to giv-ing a weight of 1.0 to the most recentobserved vaiue and a weight of 0.0 toaii other observations.

Since the data series for many itemsthat are forecast exhibit a seasonal pat-tern, a somewhat more sophisticatedmethod, Naive li, might be appiied.This method uses the most recentlyobserved value as the basis for theforecast, but adjusts it for seasonaiity.This is done by deseasonaiizing themost recent observation and then re-seasonaiizing for the period that is tobe forecast.

An important application of suchNaive methods is to use their forecast-ing accuracy as a basis for comparingaiternative approaches. It is not un-common to find that one of theseNaive methods may provide adequateaccuracy for certain situations. It mayalso be the case that more sophisti-cated methods (which are usuallymuch more costly) do not give suffi-cient improvement in accuracy overthese methods to justify their use.

When the time horizon for forecast-ing is fairly short, it is usually the ran-domness element that is major con-cern. One way to minimize the impactof randomness on individual forecastsis to average severai of the past vaiuesrather than using only a single value (aswe did with the Naive methods). TheMoving Average approach is one of thesimpiest ways to reduce the impact ofrandomness. This method consists ofweighting N of the recentiy observedvaiues by 1/N. (Note that the N most

recent terms are thereby included inthe average.)

Forexampie, if a regionai sales man-ager were forecasting monthiy ship-ments to a certain geographicai region,it might be appropriate to use a movingaverage invoiving 12 terms. In forecast-ing the expected shipments for thenext month, each of the vaiues for thepast 12 months wouid be given aweight of 1/12th and that weightedsum wouid be the forecast.

As new observations become avail-able, they can be used in the average,making it a "moving" one throughtime. It should be noted that when aMoving Average is chosen that has thesame number of terms as a compieteseasonai pattern (for exampie, 12terms if the data are monthly and thereis an annual seasonal pattern), the sea-sonaiity is effectiveiy removed in theforecast since an observation for eachperiod in the season is inciuded in theaverage.

Exponential SmoothingThis approach to time series fore-

casting is very simiiar to the MovingAverage approach but does not use aconstant set of weights for the N mostrecent observations. Rather, an expo-nentiaiiy decreasing set of weights isused so that the more recent values re-ceive more weight than oider vaiues.This notion of giving greater weight tomore recent information is one thathas strong intuitive appeal for man-agers and mai<es sense based on

studies of the accuracy of exponentiaismoothing methods. Additionaily, thecomputationai characteristics of thismethod mai<e it unnecessary to storeail of the past values of the data seriesbeing forecast. The oniy data requiredare the weight that wiil be appiied tothe most recent vaiue (often calledALPHA), the most recent forecast andthe most recent actual value.

There are actualiy severai differentapproaches to Exponential Smoothing

that have been described in the litera-ture. Buiiding on the most basic ap-proach of simpiy applying decreasingweights to previous values, thesevariations seei< to make adjustmentsfor such things as trend and seasonaipatterns. When such adjustmentsare made, they are often referredto as higher forms of ExponentialSmoothing.

Forecasting: issues & Challenges / 29

>1uto-Regressii/'e//Mok'ingThe most sophisticated of the single

time series approaches to forecastingare known as ARMA methods. Thesefollow the same philosophy as themethods mentioned above, but use adifferent procedure for determininghow many of the past observationsshould be included in preparing theforecast, and in determining the appro-priate weight values to be appiied tothose observations.

The most commonly used of thisciass of methods is the procedure de-veloped by Box and Jenkins. This pro-cedure consists of a set of rules foridentifying the most appropriate ARMAmodel (that is, determining the numberof observations to be inciuded in themodel) and specifying the weights (theparameter vaiues) to be used in thatmodel. The basis on which the param-

eters are determined is statistical andis done in such a way that the error(the difference between the actualvalue and the forecast value for anytime period) wiii be a minimum. Thismethodoiogy aiso provides statisticsabout the forecast as weii as an ex-pected vaiue for the forecast.

An aiternative to the Box and Jen-kins methodoiogy that wouid aiso fallinto the ARMA ciass of methodologiesis that of Fiitering. These methods aremuch iike Box-Jenkins; oniy the meansby which the appropriate modei (thatis, the terms to be included) and theweight vaiues determined are different.

ARMA methodoiogies make a basicextension to the methods describedabove. In the simpler methods, onlypast vaiues of the variable to be fore-cast are weighted in developing the

forecast. For example, if a companywanted to forecast its shipments on aquarterly basis, only the past vaiues ofquarterly shipments wouid be includedin preparing the forecast. In the ter-minology of ARMA methodoiogiessuch a model would be referred to asautoregressive.

However, it may be possibie to ob-tain more accurate forecasts if pastvaiues of the forecasting errors areweighted as well. When past vaiues ofthe errors are weighted to obtain a fore-cast, the modei is referred to as a mov-ing average one (not to be confusedwith the method, Moving Average, asdescribed above). Thus, an ARMAmodei is one that inciudes in itsweighted terms both past vaiues ofthe variabie to be forecast and pastvalues of the forecast errors.

//lultiple RegressionIn its simplest form this forecating

methodoiogy can be thought of as adifferent way to determine the weightsthat wiii be appiied to the past vaiuesof a variabie. However, as normallyused in marketing forecasts, themodeis are generaiiy Muitipie Regres-sion forms that inciude more than asingie variabie. In Muitipie Regression,

the forecast is based not oniy on pastvaiues of the item being forecast, buton other variabies that are thought tohave a causai reiationship. For ex-ampie, if a product manager wants toforecast monthly demand for his prod-uct iine, he might use Muitipie Regres-sion so that his forecast wouid con-sider not oniy past observations of

product demand but aiso such thingsas his advertising budget, and perhapsthe price differential between his ownproduct and competitors' products, inthis way, Muitipie Regression aliowsone to determine the causal reiation-ship between several variables and theitem being forecast.

EconometricsIn strict technical terms, regression

equations iike that described above arepart of econometrics. However, whenmost managers and practitioners talkabout econometrics they are not taik-Ing about singie regression equations(either simpie or niuitipie) but are talk-ing rather about sets of two or moreregression equations. Thus, an Econ-ometric Modei that a company mightdevelop of its Industry would include

severai equations to be solved simui-taneousiy.

One of the advantages of Econ-"ometric Modets is that the interreia-tionship among the independentvariabies in any single equation can beinciuded in other equations and theirvaiues determined simuitaneousiy.This tends to give a mucn better repre-sentation of reality since it begins tocapture the complex interrelationships

among factors, in a single equationmodel, values for each independentvariabie must be specified by the fore-caster.

The compiexity of econometrics addgreatiy to the cost of such models andmakes them generaiiy attractive oniyfor highly aggregated data (such ascompany, industry or nationai fore-casts) or for iong range projections.


randomness. In the very short-term randomness isusually the most important of these four. As thetime horizon is lengthened, seasonality takes onincreasing importance, followed by trend. For thevery long-term time horizon, seasonality becomesless important, and trend and cyclical patterns play aprimary role.

We have found that the time horizon forwhich forecasts are being prepared can often serveas a surrogate for many of these criteria includingthe type of data pattern. A summary of availablemethodologies and their appropriateness for vari-ous time horizons appears in Exhibit 1. These timehorizons reflect such correlated characteristics asthe value of accuracy in forecasting, the cost ofvarious methodologies, the timeliness of their re-sults, and the types of data patterns involved in theforecasting situation. As a first cut in selecting aforecasting methodology for a marketing situation.Exhibit 1 has proven most useful in practice."*' **• **'

Selecting a MethodologyEvery application of forecasting requires an explicitselection of a methodology to be used, and there area number of major issues that recur repeatedlywhen making this selection decision for marketingsituations. These issues cannot be handled withthe simple framework provided in Exhibit 1. Theyrequire a much more detailed analysis of the situa-tion and a statement of forecasting objectives in thatsituation. Three of the most important of theseissues and relevant research on them will be con-sidered in this section:

• Time Series versus Causal methodologies.

• Continuation of a Historical Pattern versusTurning Point forecasting.

• The empirical performance of available meth-odologies as measured by their accuracy.

Causal vs. Time SeriesThe first of these issues might appear on the surfaceto be the most straightforward. The question ismost often asked as to whether a causal method,such as regression analysis, will give better, moreuseful results than a time series method, such asexponential smoothing or Box-Jenkins. During thedecade of the 196O's and moving into the early197O's, causal or regression methods of forecastingbecame extremely popular, leading Naylor, et al,'**'to suggest that it might be appropriate to label the196O's as "The Age of the Large-Scale EconometricModel."

The initial success of econometric models gen-erated considerable optimism about their forecast-ing performance over the longer term. LJnfortu-nately, however, the 196O's turned out to be a ratherspecial period of economic activity. That period in-cluded 105 months of uninterrupted growth andprosperity, longer than any other similar periodsince 1850."" The fact that econometric models per-formed well during the 196O's turned out to be anincomplete indication of their level of accuracywhen economic conditions were changing, as in theearly and middle 197O's.

As would be expected, when structuralchanges are occurring in the economy, econometricmodels are not superior to time series approaches toforecasting."" Even a study conducted in the stable196O's'*" found that econometric models were notentirely successful in improving accuracy in fore-casting. In another study, Coopei^'*' concluded that"econometric models are not, in general, superiorto purely mechanical (time series) methods of fore-casting." Naylor, et al,"*' made a more extensiveand detailed comparison of alternative methods andexamined the Box-Jenkins approach in contrast tothe Wharton econometric model for the years 1963through 1967. The results of this study, summarizedin Exhibit 2, indicate that the accuracy of ARMAmodels of the Box-Jenkins type was considerablybetter than the accuracy of the Wharton economet-ric model for the time period examined.

A more recent study by Nelson'*^' comparedeconometric (regression) and time series (ARMA)methods for an even longer time horizon. Thiscomparison was made usirtg the FRB-MIT-PENNeconometric model. Nelson concluded that "thesimple ARMA models are relatively more robustwith respect to post-sample prediction than thecomplex FRB-MIT-PENN models. . . Thus if themean squared error were an appropriate measure ofloss, an unweighted assessment clearly indicates

EXHIBIT 2Comparison of the Wharton Econometric Modelwith the Box-Jenkins Approach (1963-1967)

Items Compared

/p (investment in biiiions)

P(GA/P price defiator in %)

Un (unemployment in %)

GA/P(in biiiions)

Average Absolute Error

Wharton

1.09

0.22

0.186

2.51

Box-Jenkins

0.59

0.11

0.109

2.01

Source: Naylor, etal. (66].


that a decision maker would have been best offrelying simply on ARMA predictions in post-sample periods."

We are not aware of any studies that reachconclusions substantially different from those re-ported by Nelson, Naylor, Cooper, and others.However, the marketing manager faced with select-ing a forecasting methodology in his own situationmust still deal with the time series versus causalmodel. The real question is whether the additionalinformation provided from causal models is worththe additional cost. Since the benefits of accuracy,often felt to justify the additional costs of suchmethods, do not appear to exist consistently, theirbenefit must lie in the knowledge they give man-agement of the situation and the interaction of vari-ous factors.

Since much of what the marketing managerdoes involves decisions designed to affect sales, thevedue of a causal model is often justified because ofthe understanding it can provide the manager as tothe causal effect of those decisions on sales perfor-mance. However, given a lack of extensive re-search to support such a claim, it is important thateach situation be considered on its own meritswhen trying to decide between a time series tech-nique and a causal or regression technique, ratherthan always going one way or the other.

Continuing Pattern vs. Turning PointA second major issue in selection, that of determin-ing whether the forecasting problem is one of pre-dicting a continuing pattern as opposed to predict-ing a turning point in the pattern, is also related tothe topic of selecting a methodology for a specificsituation. The majority of work that has been doneon forecasting uses accuracy measurements that aredesigned to evaluate a method's ability to identifyand predict a continuing pattern in a data series,rather than to handle turning points in the series.As many first discovered during the recession of themid-1970's, the prediction of turning points, whileoften difficult, can have a major impact on the firm'splanning and ability to respond to its environment.

Often, what is required in a specific situationinvolves use of one methodology for on-going pre-diction of an existing pattern, than a separatemethodology for tracking turning points. When aturning point is identified, a change is then made inthe basic methodology being applied. Some of theresearch that has dealt with this subject includesthat reported by McLaughlin and Boyle,'* '̂McLaughhn,'«" and Trigg.'"'

Most quantitative forecasting methods basetheir predictions on the assumption of constancy.

Sudden changes in pattern violate this assumptionand can cause significant deterioration in the accu-racy of a forecasting method.

Patterns can also change in a less dramatic butstill significant manner. When this happens, thegreat majority of forecasting techniques performpoorly. However, there are some promising excep-tions where adaptation of the parameter values inthe forecasting model and/or changes in the modelitself are incorporated to allow the methodology todeal with changes in the basic pattern."'-'''*'•*"However, the full implications of such approachesand their performance when patterns do not changeis not well understood as yet.

An example of pattern change that is familiarto many marketers is that of a change in growthrate. For a product that has been growing at 10% ayear, a decline in growth may have disastrous re-sults for the company as a whole if the change inthat pattern is not recognized quickly. If the prod-uct is one of 10,000 whose demand is forecast bysome quantitative method, it will be particularlydifficult to identify such changes in pattern at anearly stage. What is needed is establishment of atracking system as part of the formal forecastingprocedure so that the build-up in error values canbe automatically identified and brought to market-ing management's attention. Some of these controlprocedures are straightforward and simply a matterof incorporating such measures as an integral partof the forecasting system.'"-"-»"

Another situation where predictions are notbeing made for continuance of an existing pattern isthat which focuses on a single event. Subjectiveand/or informal methods have generally been mostappropriate in such cases. However, some qualita-tive techniques are seeking to make such ap-proaches more systematic and further improve theperformance of forecasting.'*"'

Qualitative vs. QuantitativeA third issue related to the selection of a methodol-ogy for a marketing situation is that of accuracy. Amajor question in the marketer's mind is whichmethodology will give the most accurate results.Although accuracy is not the only criterion forselecting a forecasting method,'**' it is usually giventop priority and used as a measure that reflectsseveral other criteria.

It is extremely difficult to assess the accuracyof informal and qualitative forecasting approachesin a way that allows meaningful comparison amongtechniques. This is due to the fact that these meth-ods are not standardized in the type of forecasts that


EXHIBIT 3Sales Forecast Error Comparison(Mean Absolute Deviation)

Year

1968

1969

1970

1971

1972

Average

MAPE*

CompanyForecast

5749

3858

4013

6033

9782

5887

15.9%

ExponentialSmoothing

5974

4470

2958

5657

8958

5603

15.1%

HarmonicModel

5408

4013

2998

5311

8384

5222

14.1%

Box-Jenkins

4755

4403

3284

4785

8748

5195

14.0%

Source: Mabert [49]. * Mean Absolute Percentage Error

they provide; they rely heavily on the ability ofexperts; and they simply provide a general frame-work for channeling judgments into a forecast. Afew studies have been reported that deal with in-formal and qualitative methods of forecasting andtheir accuracy. Most often these studies seekthrough comparison to bridge the gap betweensuch methodologies and more quantitative ap-proaches.

A comparison of qualitative and quantitativeforecasting results has been reported by Mabert.'**'In this instance, the researcher selected a companywhere sales forecasts had been based historically onopinions of the sales force and corporate executives.The accuracy of those forecasts was compared withthree different quantitative forecasting methods,both in terms of mean absolute deviation and meanabsolute percentage error, as shown in Exhibit 3.All three of the quantitative methods gave moreaccurate results over the five-year time period cov-ered by the study than did the company forecasts.In addition, Mabert found that in terms of timeli-ness and the cost of preparing forecasts, the quan-titative techniques were more attractive than thequalitative approaches.

Anticipatory SurveysA major form of marketing forecasts that has re-ceived considerable attention in the literature isthat of anticipatory surveys. In one recent study,Rippe and Wilkinson'^*' examined the forecastingaccuracy of the McGraw-Hill Anticipatory SurveyData dealing with investment, sales and capacity.Those researchers concluded that the McGraw-Hilldata were generally less accurate than the BEA-SECSurvey of anticipated investment for a one-yeartime horizon. However, the McGraw-Hill data were

more timely and found to be more accurate thannaive approaches and even than some sophisticatedeconometric models. In addition, the researchersreport that anticipatory surveys done in the mid-1970's tend to be more accurate on average thanolder surveys.

One other study that considered the forecast-ing accuracy of technological methods was that re-ported by Kiernan.'^' That particular research ex-amined the performance of airline industry fore-casts of domestic revenue passenger-miles from1959 through 1968. The results are summarizedgraphically in Exhibit 4. As is evident from thegraph, the industry continually underestimated thegrowth in revenue passenger-miles throughout thisentire period. Since the early 197O's, however, justthe opposite has been true, with industry forecastsexceeding actual values.

In this particular instance, the major problemcan be traced to a change from an exponential

EXHIBIT 4FAA Six-Year Forecasts & Actual, since 1961(Billions of Domestic Revenue Passenger-Miles)

85

75

LES

Stt

SE

NG

ma.UJ55

:VE

NU

Ku.045

LIO

NS

n35

A

/

Actual

/

1968-/I1974 #

/ A

As

1959 1960 1961 1962 1963 1964 1965 1966 1967 1968

FISCAL YEAR

Source: Kiernan [40].Legend: Actual 777; Forecasts


growth pattern to one best described by an S-curve.From the Exhibit, it appears that the forecasts beingmade throughout the 196O's by the airline industryassumed that the mature stage of product demandwould be reached shortly. Then, in the early 197O's,when the industry finally decided that maturity wasnot imminent (when it appeared that in fact it was),forecasts exceeded actual values. These are some ofthe dangers of long-term forecasting with which themarketing manager must deal.

Comparing Individual MethodologiesWithin the category of quantitative forecastingmethods, several studies have been reported thatcompare the relative accuracy of individual meth-odologies. In the case of regression and econometricmodels, both Cooper"''^ and Fromm and Klein'^"conclude that no single econometric model isoverwhelmingly superior to all others. These re-searchers recognize that differences may exist in theforecasting performance for single items over a lim-ited time horizon but, on the average, these differ-ences and accuracies do not consistently favor onemodel over another.

A comparison of various time series methodsin regard to their relative accuracy is more difficultthan that done for econometric and regressionmodels. The difficulties arise because there aremany more methods to compare and because dif-ferent studies have arrived at different and oftenconflicting conclusions, depending on the situa-tions examined.

In a study reported by Kirby,''"' three differenttime series were compared: moving averages, ex-ponential smoothing, and regression. Kirby foundthat in terms of month-to-month forecasting accu-racy, the exponential smoothing methods per-formed best. Both moving averages and exponentialsmoothing had similar results when the forecastinghorizon was increased to six months or longer. Theregression model included in that study was thebetter method for longer term forecasts of one yearor more. (These results support the use of the timehorizon as a criterion for method selection as shownin Exhibit 1.)

In a study reported by Levine,'*^' the samethree forecasting methods examined by Kirby werecompared. Levine concluded that while there wasan advantage of simplicity associated with the mov-ing average method, exponential smoothing offeredthe best potential accuracy for short-term forecast-ing. Other studies reported by Gross and Ray,'̂ ®'Raine,""' and Krampf'''" have arrived at conclusionssimilar to those of Levine and Kirby. Essentially,these researchers have found that exponential

smoothing models are generally superior in short-term forecasting situations, although among theseresearchers there was not much agreement as to thebest specific exponential smoothing model.

Exponential Smoothing vs. Box-JenkinsUnfortunately, comparisons among alternative de-composition methods and other techniques of fore-casting have not been reported in the literature.However, studies have been published that com-pare exponential smoothing with Box-Jenkins mod-els. Both Reid"»' and Newbold and Granger"**' con-clude that the Box-Jenkins approach of ARMAmodels gives more accurate results than exponentialsmoothing or step-wise regression methods. Whenthe comparison was made for a single period timehorizon, the Box-Jenkins results were found to bethe most accurate of the three in 73% of the cases.When the lead time for the forecasts was increasedto six periods, Box-Jenkins models still gave thebest results of the three, but in only 57% of thecases. (These results are summarized in Exhibit 5.)

EXHIBIT 5Comparison of Box-Jenkins (BJ), Holt-Winters(H-W) and Stepwise Autoregressive (S-A) Forecasts:Percentage of time first named method outper-forms second for various iead times

Comparisons

BJ : HW

B-J : S-A

H-W : S-A

Lead Times (in time periods)

1

73%

68

48

2

6 4 %

70

50

3

6 0 %

67

58

4

58%

62

57

5

58%

62

55

6

57%

61

56

7

58%

63

58

8

58%

63

59

Source: Newbold and Granger |69j.

Several studies'^*' have concluded that expo-nential smoothing can give results that are almost asaccurate as those of the Box-Jenkins methodology,and sometimes more so—a result that may be sur-prising to many marketing forecasters. In a studyreported by Groff *̂' it was concluded that the Box-Jenkins methodology gave results that were approx-imately equal in accuracy to those achieved usingexponential smoothing, or slightly worse. Thatsame conclusion was also reached by Geurts andIbrahim,""' although this latter study was some-what limited in that it dealt with only a singlemarketing situation. The fact remains that exponen-tial smoothing models, extremely simple as theyare, compete in accuracy with ARMA models.


Consequently, knowing that the Box-Jenkins meth-odology does at least as well as large econometricmodels may lead one to wonder whether the use ofeconometric models is ever justified.

Challenges to Greater EffectivenessAs indicated previously, there are at least threemajor areas that represent significant challenges tothe marketing manager if more effective forecastingis to become a reality. These supersede the questionof selecting a methodology and deal with the practi-cal problems of successful forecasting.

Technical DifficultiesThe first of these challenges deals with what mightbe termed technical aspects of the available forecast-ing methods. Some of the challenges that the au-thors would include in this category are oftenviewed as lack of flexibility on the part of the man-ager. However, these are actually technical prob-lems that need to be overcome in relation to themethodologies rather than expecting the managerto adapt his own way of thinking and decision-making simply to accommodate inflexibility inexisting techniques.

One such technical challenge is that whenformalized forecasting is first introduced into asituation, it requires steps associated with obtain-ing a forecast through application of a methodol-ogy, but does not explicitly alter decision-makingprocedures to permit those forecasts to be used ef-fectively. Thus, forecasting may not necessarily getthe marketing manager to make better decisions.What it does is require the manager to adapt to thefixed form and limitations of the techniques them-selves. Clearly, this presents special problems bothin terms of adoption of such formalized meth-odologies and in terms of limiting the usefulness oftheir resultant forecasts.

Several researchers in the marketing area haverecognized this particular problem and have re-sponded to it by developing what might be termedcomprehensive decision-making systems. For ex-ample, the NEWPROD approach suggested byAssmus,"' as well as work by Massy,"^'' Urban,'**'and Shoemaker and Staelin,'*^' have sought to inte-grate the preparation of forecasts with the makingof specific marketing decisions. Such an integrativeapproach to forecasting overcomes many of theproblems that arise when a marketing manager issimply given forecasts and then left to personalsubjective procedures for incorporating those intothe decision-making process.

Another technical challenge in forecastingconcerns the inadequacy of available meth-odologies for dealing with turning points in datapatterns. Since marketing deals extensively withproducts that follow what is frequently referred toas the product life cycle, identification and predic-tion of such turning points is essential if formalforecasting methodologies are to meet the completeneeds of the marketing manager.

Still another technical challenge facing themarketing manager is the fact that existing meth-odologies suffer from several fixed-form lim-itations. Formal forecasting methodologies requirethat data be available in a specific format (e.g.,reported for each of several time periods of uniformlength and consistent importance).

Generally it is difficult to acquire the data thatare needed to initiate application of formalizedforecasting. Even when they are obtained, the formthat they require and the format of the forecastinginformation that they provide may be very restric-tive to the marketing manager. Marketing managerswho do not use systematic forecasting proceduresgenerally have not defined, as part of theirdecision-making procedures, the collection of his-torical data for preparation of a forecast, and thenthe subsequent use of the forecast in the decision-making process. Rather, their procedure tends to bemuch more informal and intuitive, necessitatingsubstantial changes before formalized meth-odologies can be successfully adopted.

Behavioral ProblemsA second category of challenges confronting themarketing manager seeking to make more effectiveuse of forecasting are behavioral in nature. Manybehavioral problems associated with quantitativedecision-making techniques have been studied inthe general area of management science.'^"' " ' Thesehave only recently begun to attract attention fromthose focusing on forecasting and its application inmarketing.

For the marketing manager, an important as-pect of behavioral challenge involves the interfacebetween preparers of forecasts (specialists) and theusers of forecasts (marketing managers). Literaturereporting empirical studies of such interactionssuggests that what is required is better knowledge,respect, and understanding of the role and valuethat preparers have for users and vice versa. Basedon this kind of understanding, there is then a needfor a clear definition of tasks and priorities withregard to forecasting applications. A recognition ofthese disparities between the perceptions of pre-parers and users of the marketing forecast is impor-


tant if the full benefits of forecasting are to berealized, and if the potential failures in such appli-cations are to be avoided.'^*'

An important form of the behavioral chal-lenges are those caused by characteristics inherentin the techniques themselves and in the tasks forwhich the forecasting is intended. While these mayappear trivial when stated in an article such as this,they often become the major roadblock to successfulapplications of forecasting.

One such roadblock is the failure to recognizethat getting started in formalized forecasting, de-veloping additional applications to complementexisting forecasting, and transforming an occa-sional forecast into a routine application are all verydifferent tasks. The role of the marketing manageris different; the role of the specialist is different;and the needs and requirements for support fromothers in the organization are different for each ofthese. Many failures in forecasting can be tied sim-ply to a failure to recognize the type of situationinvolved and the most sensible procedure for han-dling it.

Organizational RoadblocksA third category of challenges that must be over-come by the marketing manager in order to realizeeffective forecasting are organizational in nature.For example, a roadblock frequently overlooked bythose wishing to increase the use of systematic fore-casting is that the benefits of forecasting often ac-crue to organizational units other than the market-ing group, and yet the bulk of the forecasting sup-port must come from marketing. While everyoneknows that, in theory, this may work to the good ofthe organization, in any given situation it may beextremely difficult for the marketing manager togive up some of his own scarce resources for such a"worthy cause."

Another organizational issue that frequentlyhinders forecasting is the fact that the results usedto measure the performance of forecasting appearlong after the expenditure of the effort for obtainingthose forecasts. Even in short-run situations itmight take several time periods to build up a his-tory of performance that can be used to evaluatethat application. Getting managers to adopt for-malized procedures for forecasting on the promiseof some often misunderstood and frequently unbe-lieved pledge of future good is not an easy task.

One of the major areas not understood bymany marketing executives seeking to apply fore-casting is that there are certain stages that organiza-tions go through in the application of forecastingmethodologies. In a study by Wheelwright and

EXHIBIT 6Use of Methodologies Given the ForecastingStatus of the Company

Methodology

Time Series Smoothing

Box-Jenkins

Regression Analysis

Index Numbers

Econometric Models

Juryof Exec. Opinion

Sales Force Composite

Customer Expectations

Other

Preparers Placing Company:

BehindIndustry

32.1%*

10.7%

39.3%

35.7%

25.0%

67.9%

50.0%

28.6%

17.9%

Average

70.6%

17.6%

70.6%

41.2%

52.9%

84.3%

64.7%

47.1%

23.5%

Ahead ofIndustry

65.9%

31.7%

75.6%

41.5%

63.4%

70.7%

70.7%

51.2%

31.7%

* Read: Of the companies placing Ihemseives behind the industry inforecasting, 32.1% useTime Series Smoothing.

Source: Wheelwright and Clarke [95].

Clarke,'^^' these were identified in terms of themethodologies actually applied. Exhibit 6 sum-marizes, for firms at three different stages in fore-casting experience, the percentage using variousmethodologies. While this research is empirical innature, more qualitative aspects of this study indi-cate that an evolutionary approach, as frequentlysuggested for the application of computer models,is particularly appropriate in the adoption of fore-casting.

Understanding the importance of such phasescan have a significant impact on the way in whichforecasting support groups are organized and thetype of forecasting resources that are sought. A bet-ter understanding of this aspect in forecasting canalso aid in its integration with other marketing,planning, and budgeting procedures.

Directions for Future ResearchWhile forecasting is always a risky business, thosefamiliar with research on other management topicsare aware that the groundwork for such researchusually appears several years before the major im-pact of its results is felt. Based on existing work andthe issues and challenges outlined in the literature,the need for research that can and should be con-ducted in the coming decade can be identified.

The first and perhaps easiest area to predictrelates to the need for additional methodologies fortime series analyses. The present state of develop-


ment of time series techniques suggests that severalmethodologies will be further developed andapplied to marketing problems in the coming de-cade. Particularly important are the techniques offiltering, such as those suggested over a decade agoby Kalman,"8i Kalman and Bucy,"^' and others.Also, while bivariate ARMA techniques are cur-rently available, their extension to true multivariatetime series analysis is likely in the next fewyears.'^' ^'' Finally, additional work is needed to im-prove existing time series methods, such as devel-oping computer packages like that suggested byMakridakis and Wheelwrightf^"' and developingmore automated decision rules for parameter spec-ification within time series models as suggested byTrigg and Leach,'̂ '̂ and McClain and Thomas.'^*'

Development of several technological methodsof forecasting would also be particularly useful tothe marketing manager. From the range of qualita-tive methods currently available, it would seem thatthe surface of what is possible has only beenscratched. Methods like the CATASTROPHE the-ory,'""" as well as other techniques, will undoubt-edly be more fully developed in the coming decadeand provide a much wider range of methodologiesfor use in marketing. A specific area where thesewould be especially useful would be in new productforecasting.

Another area in which research is likely tofocus in the coming decade is the integration ofqualitative and quantitative forecasts. This integra-tion is required in order to more fully utilize thejudgment of experts (particularly managers) and theresults of systematic analyses of the environment.Some work has already been done in this

Special ProblemsThere are a number of special problems in market-ing requiring forecasts where existing meth-odologies seem to hold much promise but wherelittle empirical work has been done. These includesuch things as forecasting cumulative demand(rather than demand for each of several sequentialtime periods), applying forecasting methods to dataseries where the periods are not of uniform lengthor where data values are missing, and mixing quan-titative methodologies so that a different methodol-ogy is applied when the pattern is stable than whena turning point is occurring.'^^'

As organizations build their base of experi-ence, and the number of marketing managers andspecialists with forecasting skill is further ex-panded, it is likely that administrative and pro-cedural guidelines for effective forecasting also willbe developed and tested. Much of'this work willlikely use techniques already available and will seekto develop more effective and efficient ways toutilize those in a wide range of marketing settings.

In conjunction with developments in theabove areas, it is likely that the literature will beginto focus more on the problem-oriented or applica-tions side of forecasting, such as sales forecasting,price forecasting, supply forecasting, etc., and lesson the development of completely new meth-odologies. This will help to disperse forecastingknowledge and skills more broadly within market-ing departments, and will undoubtedly lead to astronger base on which marketing management caneffectively use forecasting knowledge.

area,"' but more is needed.

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