Forecasting sales for a B2B product category - case of auto component product

Forecasting sales for a B2B product category:case of auto component product

Conway L. Lackman

A.J. Palumbo School of Business, Duquesne University, Pittsburgh, Pennsylvania, USA

AbstractPurpose – The purpose of this paper is to improve the capability of managers to forecast revenues and develop marketing plans for B2B componentproducts.Design/methodology/approach – The methodology used is a dynamic market simulation at the product level. A previously developed consumergoods speciality product forecasting model is extensively modified to capture the different parameters (i.e. direct selling) relevant to a business-to-business (B2B) component goods product category. A dynamic simulation is developed using a set of equations developed to capture the marketingmix. Using just the demand equation (total supply exogenous) and employing the entire model (supply endogenous), sales are predicted.Findings – The key findings are that the simulation produced more accurate (lower error) forecasts. The dynamic simulation for total demand for B2Bauto components produced a mean absolute percentage error (MAPE) of 8.5 percent, comparing favorably with the average MAPEs of 30 percentachieved by 168 companies forecasting B2B products.Research limitations/implications – The main research limitation is that the model is limited to B2B component products.Practical implications – The practical implication of the model is that it improves the ability of marketing managers to successfully reach revenuetargets.Originality/value – This improved ability adds value to the B2B component marketing manager’s planning process by providing a method ofspecifying a marketing plan that is likely to result in revenue that achieves or exceeds the target revenue and knowledge of what marketing mix levelswould move present sales to meet or exceed target.

Keywords Business-to-business marketing, Industrial marketing, Forecasting, Simulation

Paper type Research paper

An executive summary for managers can be found at

the end of this article.

1. Introduction

Despite the rising importance of business-to-business (B2B)

products and the need to improve ability of B2B marketing

managers to more accurately forecast B2B product sales, not

enough attention has been paid to forecasting methods

applied to specific B2B products. Fildes and Makridakis’

(1995) study claimed that only 21 percent of the forecasting

research produced in the Journal of the American Statistical

Association from 1971 to 1991 even addressed forecasting

issues. Previous studies have focused largely on overall

company sales or product line sales. Early approaches

(Armstrong et al., 1987; Dalrymple, 1987; Hanssens et al.,

1990) focused on forecasting total company sales using a few

simple independent marketing variables, such as price and

advertising. Subsequent efforts have focused on logistic

models of specific products with high dependency on lagged

independent variables such as promotion and sales indexes

(Bass et al., 1994; Bass, 1995; Clarke, 2001) or on a series of

variables focused on buying characteristics of consumers, i.e.

number of users, customer concentration, attitudes toward

product (Cohen, 2002). On the dependent variable side,

recent efforts have focused on disaggregating from total

company sales to product lines and specific products or

brands (Armstrong, 1999). The focus for independent

variables shifted to a broader range of independent

marketing variables (DeKimpe and Hanssens, 1995;

Armstrong et al., 1998) such as research and development

expenditures, product-related variables, dealer allocation, and

place-related variables as well as pricing and promotion.

Promotion usually included advertising and first-level sales

staffing, such as the manufacturer’s sales force, but not the

sales force of each channel member in the channel network.The simulation model in this paper addresses deficiencies

on both the dependent and independent variable sides. On

the dependent variable side, forecasted sales by product line

received little attention. A credible methodological argument

supporting reluctance to disaggregate to the product line level

is that the model becomes too restrictive and loses robustness.

However, disaggregation is needed to meet the manager’s

need to forecast specific product lines.On the independent variable side, the referent model better

balances parsimony and comprehensiveness. In determining

the structure of marketing models, trade-offs arise between

parsimony and comprehensiveness. Parsimony refers to

minimizing the number of variables to achieve simplicity

and clarity, subject to sufficient comprehensiveness.

Comprehensiveness refers to identifying and including a

sufficient number of variables to capture the dynamics of the

market. There is a credible argument for parsimony in order

to achieve better predictability. More variables can make a

forecasting model less tractable and are likely to increase the

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Journal of Business & Industrial Marketing

22/4 (2007) 228–235

q Emerald Group Publishing Limited [ISSN 0885-8624]

[DOI 10.1108/08858620710754496]

228

cost as a result of the larger amounts of data collection and

processing required. The referent model’s attempt to balance

parsimony and comprehensiveness was guided by the “Keep

It Sophisticatedly Simple” rule (Zellner, 2004). Zellner uses

Jeffreys’ complexity rule (JCR) to pursue parsimony. The JCR

attempts to minimize the sum of order, degree, and

normalized coefficients cleared of factors common to all in

order to achieve simplicity. The JCR implies that while

including too many terms in a relation can improve fit, it may

lose accuracy in prediction, or simply that the simplest law

chosen is likely to give correct predictions (Jeffreys, 1961).Zellner (2004) rightly cites the failure of large

macroeconometric models to predict accurately and the shift

in their design to simple aggregate models. The JCR is

applicable particularly to difference and differential equations.

He argues that it is easier to determine the causes of

inadequacy and remedy them with a simple model than with a

complex one. One straightforward method proposed to

simplify a model is to reduce the degree of the equation(s),

i.e. reduction from degree one to degree zero by eliminating

the intercept. For example, yðtÞ ¼ byðt 2 1Þ is less complex

(or more parsimonious) than yðtÞ ¼ a þ byðt 2 1Þ. Other

methods include exogenizing variables, eliminating error

terms, and aggregating up rather than disaggregating down

(Zellner, 2004).The argument for more parsimony is that the essence of

modeling is making approximating assumptions treating

parsimony with the prudent application of Occam’s razor.

Models can only accommodate a limited amount of

complexity (Shugan, 2002). However, there is a valid

argument that recent demands of the global marketplace are

pushing modeling toward comprehensiveness: marketers

should take an expanded view and capture political,

economic, and social interdependencies from a global

perspective (Kahle et al., 2003).The referent model is far less complicated than the

macroeconometric models Zellner indicts for lack of

parsimony. The referent model contains nine independent

variables including two exogenous variables – 12 variables

when we include a lag variable in advertising, an intercept

term, and an error term. Dropping the latter three variables

and exogenizing one or more marketing mix variables clearly

reduces the complexity. However, a troublesome trade-off

with both comprehensiveness and predictability arises.

Exogenizing any of the marketing mix variables is at odds

with the reality of the market. Dropping the lagged advertising

variable substantially reduces predictability, i.e. mean average

percent error rises from 8.5 percent to 11 percent without

simplifying interpretation of the model. Finally, eliminating

the error term could pose problems especially in this (and

most) B2B applications where heavy reliance on

measurements from survey data have to be elaborated to

take account of systematic reporting errors, the design of the

survey, and other features of the process of data generation

such as non-random attrition and missing observations.There is also an argument that parsimony ensures frugality.

While more complicated models require more data, which

adds to costs, the costs are coming down as a result of

information technology advances, especially in marketing

intelligence systems including Internet surveys to capture

product ratings and scanner use to capture data (DeKimpe

and Hanssens, 2000).

The referent model offers a market simulation that attempts

to address these deficiencies in an effort to improve the

application of simulation to business situations. First, on the

dependent variable side, company sales are defined as a

product that fits an accepted marketing category or product -

B2B component goods (Kotler, 1994). Disaggregation is

mandated by management’s need to forecast specific product

lines. Second, on the independent variable side, the model’s

framework is a practical balance of parsimony and

comprehensiveness. An effort at parsimony was made by

applying the rule “aggregate up rather than disaggregate

down”. Aggregation up of three product variables to two

variables (combining competitors’ product ratings) improved

the referent model’s predictability and simplified the

interpretation of the model. The result is a more accurate

sales forecasting model (8.5 percent error) than the average

error (30 percent) of 168 B2B forecasts (Jain, 2004). The

dynamic regression model (Goodrich, 2003) as well as

extensions of this approach (Jose del Moral and Valderrama,

1997) influenced this study’s models.

2. The model

A consumer product market simulation model (Lackman,

1995) is utilized but extensively modified to capture the

different parameters and relationships relevant to a B2B

component goods product category (Herbig et al., 1994).

These differences include different promotional emphasis

(direct selling in B2B versus advertising in the consumer

case), lower price elasticity, importance of product quality,

absence of a place variable (because distribution is direct from

manufacturer to user), different lag specifications for

advertising, and the addition of competitors’ prices in the

model. A market simulation for the B2B component product

category is built and tested incorporating these parameter

differences. Assessment of the model’s performance is based

on its ability to predict sales over a three-year period with

superior accuracy relative to the typical B2B product

forecasting error of 30 percent (Jain, 2004). In marketing

management, such simulation models are useful planning

tools because they forecast revenue outcomes from a certain

policy, i.e. a given marketing mix (Sanders and Manrodt,

1994).In the following sections, the case study used to test the

simulation model will be introduced. In the next section,

which involves Everware Corporation, a producer and

distributor of high-quality tires, seat covers, and accessory

components for automobiles and trucks, the data sources and

parameter definitions will be delineated. Next, the case study,

which involves a test of the model, will be explained. Finally,

conclusions are drawn.As with a simulation model for consumer markets, a

simulation specifically tailored to the market for B2B

components can be based upon the traditional “four Ps”

marketing model:1 product;2 place (availability);3 promotion; and4 price (Kotler, 1994).

These four elements are also said to make up the “marketing

mix” as they are factors under the marketer’s control when

operational strategy is planned. The dependant variable on

Forecasting sales for a B2B product category

Conway L. Lackman


Volume 22 · Number 4 · 2007 · 228–235

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the demand side is quarterly unit sales of Everware

Corporation’s auto component. The general demand

equation takes the form:

Qt ¼ a þ b1Prit 2 b2Prjt þ b3Nst þ b4Adt þ b5SP 2 b6Pit

þ b7Pij þ b8Ydt þ b9Cat þ e; ð1Þ

where Pri is the the overall rating of product attributes

assigned to Everware’s own product by consumers; Prj is the

average overall rating of product attributes assigned to the

competitors’ product by consumers; Ns is the constant dollar

outlay on direct selling; Ad is the constant dollar outlay on

advertising; SP is the constant dollar outlay on sales

promotion; Pi is Everware’s own price; Pj is the competitor’s

average price; Yd is the real US disposable income; Ca is the

real US consumption of autos; and e is an error term.

3. Data

Everware’s internal database provided the data necessary to

estimate the Everware demand equation. The time series data

related to this model were found to be not stationary.

Correlation time series methods are built on stationary

conditions (conditions where means and co-variances are

independent of time origin; Goodrich, 2003). Therefore a

Box-Cox routine was used to make the data stationary and

thereby, meet the condition required for forecasting. This

routine is described in Appendix 2.The sample period for the demand equation is from the first

quarter of 1970 to the fourth quarter of 2002. Seasonally

adjusted quarterly data were used for all the variables

estimated in the model. The supply equation is based on the

same sample.Because marketing managers need estimates of future values

of the independent variables in order to provide forecasts

supporting their strategic marketing plan, an additive Holt-

Winters model (Lawton, 1998) was used to forecast these

variables. Quantity (Q), the dependent variable in the demand

equation, was based on the company’s quarterly sales records

for their component product line. Product ratings for the study

period were obtained from a quarterly Dun and Bradstreet

(D&B) study based on a national representative sample of

business buyers (major automobile manufacturers) of the

component product. TheD&B study elicited ratings (on a 1-10

scale) of eight product attributes for Everware and its two rivals.

The Everware product rating (Pri) for a given quarter is the

unweighted average of eight product attributes. The two rivals’

ratings (Prj)were computed the sameway and averaged into one

rival product rating. The sales data came from the company’s

annual report. Promotion variables are constructed from total

dollar expenditures (1970 dollars) on sales representatives’

salaries, advertising, and sales promotion. Advertising and sales

promotion outlays are both based on 1970 dollars. The source

of the promotion outlays is the company’smargin report. Prices

are represented by an index based on the revenue-weighted

average annual price of all products (1970 ¼ 100). Everware’s

own price data came from their sales invoices. Rivals’ prices

were determined by a D&B survey of buyers. By valuing all

variables in constant dollars, potential scale difference problems

were avoided. The supply data came from Everware’s quarterly

production report of units produced.

4. Estimation results

This section presents the structural model of the component

product market. The demand side as shown earlier consists of

three (product, promotion, and price) of the “Four Ps” and

two exogenous variables (real US auto consumption and real

disposable income). The supply side is represented by

quarterly unit production. Following a brief description of

variable behavior, tables are presented with a listing for each

equation, the variables employed (Table I), and the sign of the

coefficient (Table II) with their associated marketing

implication.The estimated equations representing the structural model

of the auto component sales are shown in the Appendix 1.

The t value (absolute value) is shown under the coefficient of

each variable. Other statistical measures include coefficient of

determination (R2), a measure of the percent of variance

explained; standard error of the estimate (SE); the Durbin

Watson (DW) statistic, an indication of the presence of

autocorrelation (Durbin, 1970); and the F ratio. The results

of these statistical significance tests for the model were

satisfactory and can be found in Appendix 1.

5. Findings

In general, the demand equation corresponds to traditional

marketing theory as applied to B2B product markets. Product

quality is the strongest determinant of sales: it explains the

largest percentage of variance in sales. Direct sales

expenditures are the second strongest determinant of sales.

Advertising and sales promotion budgets are based on sales

targets; therefore these outlays correlate highly with sales.

However, in the model, these outlays make only a small

contribution to the variation in sales. The average over the

forecast period of the computed point elasticities for

advertising and sales promotion is 1.34 and 1.27,

respectively. Everware’s own price changes make a small

contribution to sales, inferring a low price elasticity normally

found in B2B component markets. The average over the

forecast period of the computed point elasticity for own price

is 0.55. These elasticities are consistent with previous findings

for industrial products (Tellis, 1988, 1999).Overall the estimates furnish satisfactory results. As

estimated, the Everware marketing mix coefficients are

Table I Variables employed

Variable Description

Pri An overall rating of product attributes assigned to

Everware’s own product by consumers

Prj An overall rating of product attributes assigned to the

competitors’ product by consumers

Ns Constant dollar outlay on the direct selling

Ad Constant dollar outlay on advertising

SP Constant dollar outlay on sales promotion

Pi Everware’s own price

Pj Competitors’ average price

Yd Real US disposable income

Ca Real US consumption of autos

Qs Company supply of auto components

Inv/R Company inventory-to-sales ratio


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characteristic of B2B products. Both of the product ratingcoefficients are relatively large. The product variablecommands relatively greater strength because of theimportance of product performance in the buy team buyingdecision, as represented by the relatively greater importanceof the product rating in the marketing mix. For example, anunacceptably high probability of brake failure for anautomobile manufacturer normally outweighs price savings.The price coefficients are relatively smaller, reflecting thenon-price orientation of B2B marketing and inferringgenerally lower price elasticity over the period studied thanin consumer models.

6. Simulation results

The market clearing identity is:

Qdt ¼ Qst ð2Þ

for the demand simulation. Absolute percentage error(MAPES) is 8.5 percent compared to the typical 30 percentfor industrial product and higher for buyer-intention-surveys-based forecasts (Jain, 2004; Remus et al., 1998). Theestimated Everware revenue very closely approximates theactual Everware revenue in this case study.

7. Conclusions

Four of the forecasting problems (see Table III) cited byArmstrong (2001) with either strong or moderate need forremedy are addressed by this model. The first problem,identifying outcomes before forecasts, is addressed by themodel’s ability to enable managers to close sales variances.Management in B2B components markets may find thismodel useful for forecasting the revenue results of itsmarketing policies and developing a market plan in whichthose results are made consistent with marketing goals. This isa critical and essential function of the firm, because the mainobjectives are reaching or exceeding sales and profits targets(Barron and Targett, 2000). These revenue forecasts point

out the variance between the values of the marketing mix

variables currently in effect and those values that achieve

target revenue. The model provides a method to specify a

marketing plan that is likely to result in revenue that achieves

or exceeds the target revenue. Therefore management learns

what marketing mix levels would move its present sales to

meet or exceed target (Mathews and Diamantopoulos, 1986).

The second problem, use of different data types, is addressed

by successfully combining heterogeneous data (ratings for

product, budget outlays for promotion) in the independent

variable set. The third problem, test situation matching the

problem type, is addressed by developing the model to fit the

product type (components). The fourth problem, comparison

of different forecast methods, is addressed by comparing the

model’s forecasting performance with the average

performance over a large sample of B2B forecasts in Jain’s

sample. By providing insight into dealing with four

troublesome forecasting problems, the model contributes to

the advancement of applied forecasting method and

application.

8. Managerial implications

The referent model arms the B2B marketing manager with a

better management planning tool, comparable to that

developed by the author for managers responsible for

choosing the marketing mix for consumer specialty goods.

B2B components market management may rely on the model

to forecast the revenue results of its marketing policies and to

develop a market plan in which those results are made

consistent with marketing goals. This is a critical and essential

function of the firm, because the main target is reaching sales

and profits targets. These revenue forecasts point out the

variance between the values of the marketing mix variables

currently in effect and those that produce the target revenue.

Therefore management learns what changes would most

likely move its present sales results closer to target. Its task

then is to decide if it is advantageous to execute these changes.

Table II Relationships among variables

Dependent variable Independent variable Sign Implication

Q Pri þ Sales directly related to own product rating

Q Prj 2 Sales inversely related to rivals’ product rating

Q Ns þ Sales directly related to outlays on direct selling

Q Ad þ Sales directly related to direct outlay on advertising

Q SP þ Sales directly related to outlays on sales promotion

Q Pi 2 Sales inversely related to own price

Q Pj þ Sales directly related to rivals’ price

Q Ca þ Sales directly related to real US auto consumption

Q Yd þ Sales directly related to US real disposable income

Table III Critical forecasting research issues addressed

Research issues Need level Treatment

1. Identify outcomes before forecasts Moderate Capable of closing sales variance

2. Estimation used different data types Strong Product rating budget outlays

3. Test situation matches problem Moderate Model developed to fit product type

4. Compare different forecasting methods Moderate Outperformed Jain’s sample


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Simulation models like the one developed here are

manageable, even for the data challenged firm. The product

rating (Pri,Prj) data set has become a standard MIS item as

competitive pressures force firms to track and react to

customer satisfaction levels measured by ratings such as Pri

(Davidow and Uttal, 1989). The inventory-to-sales ratio (Iw)

has become standard at the vast majority of firms with the

rapid development of logistics systems (Lackman and

Hanson, 1999). Sales expenses have become standard MIS

items as firms need to track and control the rising cost of a

sales call (Saban, 1997). In essence, competitive business

pressures force firms to collect data needed for simulation

modeling. Although these data are not error free, incorrect

information is the bane of organizations. However, studies

(Remus et al., 1998) show that incorrect information results

in no less accurate forecasts than no information at all. Any

successful application of a marketing model must be based on

three components:1 the phenomena in the marketplace;2 the technical skills of the modeler; and3 the options open to the manager (Roberts, 2000).

By providing the balance between simplicity and

comprehensiveness, the referent model makes it practical for

use. The data demands are modest and “what if” simulation

capability (estimating the revenue outcomes of different

marketing mixes) is straightforward. Managers should have

confidence in using this model, based on the resulting mean

average error of 8.5 percent over the three-year time horizon.

This model’s accuracy compares favorably with normal

product sales mean average forecast errors of 30 percent for

all models (Jain, 2004).

9. Limitations

Any new model has disadvantages. The product life cycle

(PLC) of a particular good can cause the relationships

between its sales and its marketing mix variables to change

over time. For example, in the maturity stage of the PLC,

there is normally more price competition because there are

more competitors. Therefore price becomes a dominant

variable in the marketing mix in terms of the effect of price

changes on sales. It is also important to keep in mind that the

life cycle may shift its course suddenly without the planner’s

cognizance. When this happens, projections made earlier and

applied to one PLC stage may no longer be an adequate

prediction of the actual developments because the product is

now in a different PLC stage. To remedy this deficiency, the

simulation should be run on the life-cycle stage relevant to the

product under analysis.A second limitation of the model is the abundance of data

required for operational use. The marketing manager has all

the information at his disposal, but a separate area of his/her

department must be dedicated to the inputs of the model,

raising its usage cost. Fortunately, the rise of fifth-generation

computers and increasing sophistication of corporate

databases likely will reduce this particular limitation. Also,

the input procedure for running simulations, uploading a few

basic data files, is not that difficult. This model is a useful

managerial instrument for a company to develop product-

marketing policy when operating in the B2B components

market.

Finally, a model of the same basic structure as this model is

not likely to be applicable to other B2B products. For

example, pricing and product design decisions are based on

an understanding of the differences among consumers in price

sensitivity, and valuation of product attributes among

different product categories (Allenby and Rossi, 1999).

There are significant differences among B2B product

categories with respect to marketing mix variables (four Ps)

that drive sales (Schoell and Guiltinan, 1995; Kotler, 1994;

Herbig et al., 1994; Lackman, 1978). A vivid example is that

of components and installations. Heavy personal selling,

advertising in trade magazines, and good product ratings are

important for both product categories in all stages of the

product life cycle, but there is a fundamental difference in the

use of components and the use of installations. Components

are entering products (Schoell and Guiltinan, 1995) used as

part of the finished product; installations are support products

purchased for use over an extended period of time and are

depreciated. The buy criterion for components is usually

quality and dependability. The key buy criterion for

installations is rate of return on investment (Schoell and

Guiltinan, 1995). In addition, while the performance of the

product is very important in the buy decision, the level of

service required with installations associated with the product

is generally higher than the components’ (Saibal, 2005;

Kotler, 1994; Herbig et al., 1994; Lackman, 1978). Also,

differences in the relative importance of the marketing mix

variables between the two product classifications are notable.

While product performance of components is important, the

amount of investment required is considerably less than for

installations. Therefore the economic implications per unit

purchased influence less the buy decision. Product

performance is critical to delivering the return on

investment of installations. As a result, price sensitivity is

less for installations than components (Brierty et al., 1998;

Herbig et al., 1994).Furthermore, the type of industry can differentiate the

impact of marketing mix variables. In capital-intensive

industries, economic implications take on more importance

in the buy decision for installations than for components. As a

result, price sensitivity is relatively less for installations

compared to components.Different supply chain structures in different industries can

affect the impact of the place variable in the marketing mix.

For example, in the consumer package goods industry, supply

chain is consolidated through “big box” retailers, giving the

place variable more impact on sales. However, in the drug

industry, the supply chain balance of power is more equally

distributed among manufacturers, chemical suppliers, and

drug wholesalers which tends to diminish the relative impact

of the place variable (Kiely, 2004).As a result of these differences among categories of B2B

products, the regression coefficients will vary among different

goods and, therefore, the precise weight will vary among

different models of different B2B goods. Lower-end B2B

goods such as raw materials are also significantly different to

B2B components (Lynn et al., 1999; Lynn and Green, 1998).

Each B2B good should have its own tailored model in order to

accurately predict the revenues associated with a given

marketing mix decision.


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Appendix 1: Model estimation

Demand

The demand for auto component sales can be represented by

the following equation:

Qd ¼ð2:5Þ0:652

ð5:1Þ5:11Pri 2

ð6:2Þ4:91Prj þ ð5:76Þ

4:13Ns þð2:45Þ

0:52Ad

þð2:31Þ

0:39Ad21 þð2:11Þ

0:19SP21 2ð2:41Þ0:51Pi þ

ð0:81Þ0:13Pj þ ð3:30Þ

2:2Yd

þð3:05Þ1:8Ca;

where Qd is company demand for auto component sales; Pri

is an overall rating of product attributes assigned to

Everware’s own product by consumers; Prj is an overall

rating of product attributes assigned to the competitors’

product by consumers; Ns is the constant dollar outlay on

direct selling; Ad is the constant dollar outlay on

advertising; SP is the constant dollar outlay on sales

promotion; Pi is Everware’s own price; Pj is the competitors’

average price; Yd is the real US disposable income; and Ca

is the real US consumption of autos. In addition,

R2 ¼ 0:872, SE ¼ 1:14, DW ¼ 1:61, and F ¼ 18:4.

Supply

Qst ¼ð4:7Þ

1:1Qst21 2ð3:2Þ

0:31ðInv=RÞt ;

where Qs is the supply of auto component sales and Inv/R is

the company inventory-to-sales ratio. In addition, R2 ¼ 0:92,SE ¼ 1:05, DW ¼ 1:74, and F ¼ 22:6.Based on the Durbin-Watson test, either no serial

correlation was present or was (in the majority of cases)

indeterminate, i.e. for K number of independent variables, all

DW fell between du and 4, indicating acceptance of the null

hypothesis that no autocorrelation is present (Durbin and

Watson, 1951). Multicollinearity was not a problem (all

partial correlation coefficients below 0.1).

Appendix 2: Stationarity

A stationary time series remains in statistical equilibrium withunchanging mean, variance and autocorrelations. Theprobabilistic approach to time series forecasting requiresstationarity. Since these data are heteroscedastic, the Box-Coxpower transform (BCT) shown below was employed on thesedata to obtain stationarity (Hamilton, 1994):

YtðlÞ ¼ Ylt21=l:

The appropriate auto-covariance function was formulated.The autocorrelation function (ACF) was derived bynormalizing the autocovariance function (i.e. dividing eachterm of the autocovariance function by the variance). Theeffect of the transform was to change the relationship of thevariation of a positive-valued time series to its local level. Theexpected impact of the stationary adjustment was achieved;the original series ACF “died slowly”, whereas thetransformed series ACF “died fast” with increasing lags. Asexpected, regression estimates based on the transformed tostationary data produced an improvement in statisticalsignificance compared with estimates previously performedon non-stationary data (Lackman, 1995). In addition, 13more years of data were added to the sample. All estimationsare made using two-stage least squares (2SLS) regressionanalysis (Pindyck and Rubinfeld, 1976). This method oftenproduces estimates that have larger variances than estimatesderived from ordinary least squares (OLS) regression(Johnston, 1992). Since the major objective of this paper isto present a consistent and unbiased model, a smaller meansquare error (RMSE) achieved during the dynamic OLSsimulation of the product sales is sacrificed in favor of the2SLS approach.

Appendix 3: Validation

Regression estimates on both a lag and no-lag basis from aholdout sample based on the period 1960.1 to 1969.4 wereconsistent with the estimates in the model’s test period. Theregression coefficients for each independent variable in themarketing mix for lagged and non-lagged error term estimatesare shown in Table AI. These coefficients are quite consistentwith those of the model.

Table AI Estimated regression coefficient: no lag versus lagged errorterm

Parameter No lag Lagged

a 0.65 0.73

Pri 5.11 5.31

Prj 24.91 25.09

Ns 4.13 3.98

ADt 0.52 0.60

ADt 21 0.39 1.14

SPt 21 0.94 1.03

Pi/Pj 20.51 20.62

Yd 2.20 2.12

Ca 1.80 1.71

Qt 21 1.1 1.24

Iw/R 20.31 20.46


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234

Corresponding author

Conway L. Lackman can be contacted at: [email protected]

Executive summary and implications formanagers and executives

This summary has been provided to allow managers and executivesa rapid appreciation of the content of the article. Those with aparticular interest in the topic covered may then read the article in

toto to take advantage of the more comprehensive description of theresearch undertaken and its results to get the full benefit of thematerial present.

Despite the rising importance of business to businessproducts and the need to improve the ability of B2B

marketing managers to more accurately forecast their sales,not enough attention has been paid to forecasting methods

applied to specific products.Previous studies have focused largely on overall company

sales or product line sales. Early approaches focused onforecasting total company sales using a few simple

independent marketing variables, such as price andadvertising. Subsequent efforts have concentrated on logistic

models of specific products with high dependency on laggedindependent variables such as promotion and sales indexes; or

on a series of variables focused on buying characteristics ofconsumers, i.e. number of users, customer concentration,

attitudes towards product.On the dependent variable side, recent efforts have focused

on disaggregating from total company sales to product linesand specific products or brands The focus for independent

variables shifted to a broader range of independent marketingvariables such as research and development expenditures,

product- related variables, dealer allocation, and place-relatedvariables as well as pricing and promotion. Promotion usually

included advertising and first-level sales staffing, such as themanufacturer’s sales force, but not the sales force of eachchannel member in the channel network.The simulation model (based on auto component products)

addressed by Conway L. Lackman in this paper addresses

deficiencies on both the dependent and independent variablesides and provides a more accurate sales forecasting model

(8.5 percent error) than the average error (30 percent) of 168B2B companies forecasting B2B products.By providing a balance between simplicity and

comprehensiveness, and depending on modest data

demands, managers will find it practical for use. Forinstance, the inventory-to-sales ratio has become standard at

the vast majority of firms with the rapid development oflogistics systems. Sales expenses are also routinely collected as

firms need to track and control the rising cost of a sales call.And the product rating data set has become a standard MIS

item as competitive pressures force firms to track and react tocustomer satisfaction levels.Consequently, simulation models like the one developed

here are manageable, even for the data-challenged firm.

Problems cited by previous research as needing eitherstrong or moderate remedy are addressed by the model. Thefirst (identifying outcomes before forecasts) is addressed bythe model’s ability to enable managers to close sales variances.Management in B2B components markets may find this

model useful for forecasting the revenue results of itsmarketing policies and developing a market plan in whichthose results are made consistent with marketing goals. This isa critical and essential function of the firm, because the mainobjectives are reaching or exceeding sales and profits targets.These revenue forecasts point out the variance between thevalues of the marketing mix variables currently in effect andthose values that achieve target revenue.The model provides a method to specify a marketing plan

that is likely to result in revenue that achieves or exceeds thetarget revenue. Therefore management learns what marketingmix levels would move its present sales to meet or exceedtarget.The second problem (use of different data types) is

addressed by successfully combining heterogeneous data(ratings for product, budget outlays for promotion) in theindependent variable set. The third problem (test situationmatching the problem type) is addressed by developing themodel to fit the product type (components). The fourthproblem (comparison of different forecast methods) isaddressed by comparing the model’s forecastingperformance with the average performance over a largesample of B2B forecasts.The model arms the B2B marketing manager with a better

management planning tool, comparable to that developed bythe author for managers responsible for choosing themarketing mix for consumer specialty goods. B2Bcomponents market management may rely on the model toforecast the revenue results of its marketing policies and todevelop a market plan in which those results are madeconsistent with marketing goals.This is a critical and essential function of the firm, because

the main target is reaching sales and profits targets. Theserevenue forecasts point out the variance between the values ofthe marketing mix variables currently in effect and those thatproduce the target revenue. Therefore management learnswhat changes would most likely move its present sales resultscloser to target. Its task then is to decide if it is advantageousto execute these changes.Competitive business pressures force firms to collect data

needed for simulation modeling. Although these data are noterror free, incorrect information is the bane of organizations.However, studies show that incorrect information results inno less accurate forecasts than no information at all. Anysuccessful application of a marketing model must be based onthree components: the phenomena in the marketplace, thetechnical skills of the modeler, and the options open to themanager.

(A precis of the article “Forecasting sales for a B2B productcategory: case of auto component product”. Supplied by MarketingConsultants for Emerald.)


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