Forecasting sales for a B2B product category - case of auto component product
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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
The current issue and full text archive of this journal is available at
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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
Journal of Business & Industrial Marketing
Volume 22 · Number 4 · 2007 · 228–235
229
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
Forecasting sales for a B2B product category
Conway L. Lackman
Journal of Business & Industrial Marketing
Volume 22 · Number 4 · 2007 · 228–235
230
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
Forecasting sales for a B2B product category
Conway L. Lackman
Journal of Business & Industrial Marketing
Volume 22 · Number 4 · 2007 · 228–235
231
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.
Forecasting sales for a B2B product category
Conway L. Lackman
Journal of Business & Industrial Marketing
Volume 22 · Number 4 · 2007 · 228–235
232
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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
Forecasting sales for a B2B product category
Conway L. Lackman
Journal of Business & Industrial Marketing
Volume 22 · Number 4 · 2007 · 228–235
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.)
Forecasting sales for a B2B product category
Conway L. Lackman
Journal of Business & Industrial Marketing
Volume 22 · Number 4 · 2007 · 228–235
235
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