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4/3/19, 8)45 PMControlling Scoring Guide
Page 1 of
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FP/BUS-
FP3011/190100/Scoring_Guides/a04_scoring_guide.html
Controlling Scoring Guide
CRITERIA NON-PERFORMANCE BASIC PROFICIENT
DISTINGUISHED
Create a strategically
aligned purpose
statement for a
human resources
functional area.
Does not create a
purpose statement
for a human
resources
functional area.
Creates a purpose
statement for a
human resources
functional area but
it is not
strategically
aligned.

Creates a
strategically
aligned purpose
statement for a
human resources
functional area.
Creates a strategically aligned
purpose statement for a human
resources functional area and
explains how the purpose
statement will help produce
effective results.
Identify strategically
aligned performance
standards for a
human resources
functional area.
Does not identify
performance
standards for a
human resources
functional area.
Identifies
performance
standards for a
human resources
functional area but
their strategic
alignment is
unclear.
Identifies

strategically
aligned
performance
standards for a
human resources
functional area.
Identifies strategically aligned
performance standards for a
human resources functional area
and explains how managers can
support the team through
measuring performance and
taking corrective action.
Create a strategically
aligned purpose
statement for a
declared functional
area.
Does not create a
purpose statement
for a declared
functional area.
Creates a purpose
statement for a
declared functional
area but it is not
strategically
aligned.
Creates a
strategically
aligned purpose

statement for a
declared functional
area.
Creates a strategically aligned
purpose statement for a declared
functional area and explains how
the purpose statement will help
produce effective results.
Identify strategically
aligned performance
standards for a
declared functional
area.
Does not identify
performance
standards for a
declared functional
area.
Identifies
performance
standards for a
declared functional
area but their
strategic alignment
is unclear.
Identifies
strategically
aligned
performance
standards for a
declared functional

area.
Identifies strategically aligned
performance standards for a
declared functional area and
explains how managers can
support the team through
measuring performance and
taking corrective action.
Explain how the
purpose statement
for the human
resources functional
area supports people
leadership.
Does not identify
how the purpose
statement for the
human resources
functional area
supports people
leadership.
Identifies but does
not explain how
the purpose
statement for the
human resources
functional area
supports people
leadership.
Explains how the
purpose statement

for the human
resources
functional area
supports people
leadership.
Explains how the purpose
statement for the human
resources functional area
supports people leadership and
assesses potential management
challenges associated with the
purpose statement.
Explain how the
purpose statement
for a declared
functional area
supports people
leadership.
Does not identify
how the purpose
statement for a
declared functional
area supports
people leadership.
Identifies but does
not explain how
the purpose
statement for a
declared functional
area supports
people leadership.

Explains how the
purpose statement
for a declared
functional area
supports people
leadership.
Explains how the purpose
statement for a declared
functional area supports people
leadership and assesses
potential management
challenges associated with the
purpose statement.
Explain how the
performance
standards for the
Does not explain
how performance
standards for the
Identifies
performance
standards for the
Explains how the
performance
standards for the
Explains how the performance
standards for the human
resources functional area reflect

4/3/19, 8)45 PMControlling Scoring Guide
Page 2 of
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FP/BUS-
FP3011/190100/Scoring_Guides/a04_scoring_guide.html
human resources
functional area
reflect strategic
alignment with the
organization.
human resources
functional area
reflect strategic
alignment with the
organization.
human resources
functional area, but
does not explain
how they relate to
the organization.
human resources
functional area
reflect strategic
alignment with the
organization.
strategic alignment with the
organization through support of
goals, activities, a purpose
statement, and an organizational

structure.
Explain how the
performance
standards for a
declared functional
area reflect strategic
alignment with the
organization.
Does not explain
how performance
standards for a
declared functional
area reflect
strategic alignment
with the
organization.
Identifies
performance
standards for a
declared functional
area, but does not
explain how they
relate to the
organization.
Explains how the
performance
standards for a
declared functional
area reflect
strategic alignment
with the
organization.

Explains how the performance
standards for a declared
functional area reflect strategic
alignment with the organization
through support of goals,
activities, a purpose statement,
and an organizational structure.
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https://content.ashford.edu/print/AUBUS620.12.1?sections=ch0
4,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,
ch04_eoc&content=… 1/20
Chapter 4
Evaluating Market Demand and Forecasting
Belinda Images/SuperStock
Learning Outcomes
By the end of this chapter, you should:
Recognize the differences between market poten�al, available
market, and sales poten�al.
Explain the significance of the product usage gap and brand
usage gap.
Know the uses of short-range, medium-range, and long-range
sales forecasts, as well as the limita�ons of
related forecas�ng methodologies.
Know the differences between qualita�ve and quan�ta�ve

forecas�ng techniques and be able to describe the
strengths and weaknesses of each.
4/1/2019 Print
Ch. 4 Introduction
This chapter examines the role of forecas�ng in
evalua�ng market demand. The first half inves�gates the
es�ma�on of market demand and market poten�al,
referred to collec�vely as demand measurement. This is
concerned with the determina�on of current levels of
market demand and market poten�al. The focus is
exclusively on the analysis of exis�ng markets in total
without regard to market segmenta�on or brand-specific
measures of demand. The second half of the
chapter inves�gates sales forecas�ng. The methodologies
discussed in this sec�on focus on es�ma�ng future sales
at the product and brand levels of analysis.
These forecasts are focused on predic�ng what will
happen to markets and brand sales in future periods.
Accurate forecasts of product sales and market poten�al
are essen�al to survival when confron�ng uncertain
environments. In the same way that other forms of
market research provide the means for organiza�ons to
reduce risk in the face of dynamic markets, forecas�ng is
a strategy for mi�ga�ng market-related hazards by
providing �mely informa�on on the changes taking place.

Although forecas�ng is primarily understood as a strategy
for threat reduc�on, it can also provide insights
on the poten�al of new market opportuni�es.
***
When we hear the word forecas�ng, many of us first
think of weather forecasts on our local television sta�ons.
Our second thought is usually about those all-too-
frequent occasions when the meteorologists got it
completely wrong and our family picnic was rained out.
Well, sales forecas�ng is also pre�y far from being an
exact science. Maybe you have heard the story about the
two market forecasters who went deer hun�ng
together. When a deer appeared 120 feet from their stand,
the first forecaster carefully squeezed off a shot that
missed by 4 feet to the le�. His hun�ng partner
hurriedly fired a�er him, but missed by 4 feet to the
right . . . at which point the first forecaster began
jumping up and down, shou�ng: "We hit it! We hit it!"
Yep . . . it's just like Yogi Berra said: It's tough to
make predic�ons, especially about the future.
4/1/2019 Print
Apple markets products well and accurately forecasts

market
poten�al and sales. This has consistently resulted in lines
of eager
customers at store openings and product debuts.
Demo�x/Corbis
4.1 Demand Measurement
More accurate informa�on will inevitably help marke�ng
managers improve the design and performance of
marke�ng plans. Historical data on pa�erns in the
market (e.g., market shares, aggregate growth trends) and
brand-specific informa�on (e.g., sales, customer reten�on
rates) can provide significant insight into
previous marke�ng decisions. To effec�vely plan for the
future, however, managers need access to general market
and brand-specific informa�on for both the
present and future. Es�mates of future demand can be
created based on short-range, medium-range, or long-range
forecas�ng horizons, as will be discussed later
in the chapter.
The founda�on required to create reliable forecasts,
without regard to the �me period in ques�on, is a
thorough and accurate understanding of the prevailing
current or near-term market condi�ons. This requires a
rigorous study of the individual components that make up
the cumula�ve or aggregate level of total market
demand available to the firm.
Assessing Market Demand
Whether evalua�ng the profit poten�al associated with a
new market opportunity or considering revisions to an
exis�ng marke�ng program, marke�ng managers

need to understand the basic parameters that define the
markets in which they compete. Market demand is the total
volume or amount of a specific product
purchased by consumers over the complete range of prices
at which it is sold. Market demand is usually expressed
in terms of both the specific market and the
�me period. Consider, for example, the market for tablet
personal computers in 2011. The level of market demand
can be expressed in either total unit sales
volume (e.g., 22 million) or dollar sales volume ($15.4
billion). The dollar volume metric is usually expressed in
retail sales.
Market demand provides a snapshot in �me of the
market's overall size. More detailed informa�on about
current pa�erns can be disaggregated from the whole by
examining how segments within the market behave in
response to different price levels. Market research can
provide insights on how other segment descriptors
relate to sales (e.g., age, income, family size). Markets
can also be analyzed geographically if those characteris�cs
are relevant. For all marke�ng managers, it is
essen�al to understand elements of current market demand,
although some factors will be more important than others.
Market Demand Measures
The ini�al assessment of market demand provides the
pla�orm for subsequent financial analysis and projec�ons.
Whether based on the extrapola�on of trends
over �me or rooted only in current period data, all
forecasts rely on measures of current market condi�ons as
a jumping off point. In situa�ons where market
demand is not par�cularly vola�le, however, current
period data are some�mes used in lieu of any forecasts.

The dimensions highlighted in an ini�al evalua�on of
market demand depend on the intended use of
the informa�on. Entrepreneurs seeking investment funding
from venture capitalists will need to
demonstrate the financial poten�al of a market. Produc�on
and opera�ons managers need
informa�on about poten�al unit sales so that they can
align manufacturing capacity with demand.
Human resource managers for retailers will need to derive
the demand for part-�me hiring in the
current period as a func�on of prevailing sales levels.
The most common and rou�ne use of market demand
metrics is as a scorecard to evaluate the
effec�veness of the firm's marke�ng efforts. Determining
which elements of the marke�ng mix
contribute to the current period's sales, however, is not
always a straigh�orward task. Some elements
of the marke�ng mix may have an immediate impact on
sales (e.g., sales promo�ons), others have a
lagged effect (e.g., image-oriented adver�sing), and all of
them have the poten�al to interact with
each other. For example, inten�onal interac�ons o�en
occur between changes in pricing strategy and
adver�sing over �me as managers gradually shi� the
posi�oning of a brand over several repor�ng
periods. Consequently, current levels of market demand
may reflect the results of one-�me
marke�ng events, the cumula�ve impact of marke�ng
efforts over �me, or the interac�on of one
organiza�on's marke�ng ini�a�ves with those of
compe�tors. Invariably, all three types of effects are
usually present in the sales results from the current
period.

Accurate market demand measures set the stage for
financial projec�ons including the assessment of market
poten�al and sales forecas�ng. The realis�c evalua�on
of market poten�al includes both the appraisal of the
available market for a product category and the sales
poten�al that is specific to a given brand.
Measuring Market Potential
Market poten�al is a measure of the maximum total
possible sales of a given product over a fixed interval of
�me. It is the mathema�cal product of the total
number of poten�al buyers in a market mul�plied by the
average quan�ty purchased per buyer. This yields an
assessment of market poten�al in product units.
Total dollar sales poten�al can be determined by
mul�plying this measure by the average price of one unit.
Total market poten�al may be stated in dollars or
expressed in unit sales.
The available market for any product is made up of those
buyers who possess both the interest and means to
purchase the product under current market
condi�ons. The target market for each brand is, of
course, a subset of this group. Sales poten�al is a term
that is o�en confused with market poten�al. However,
sales poten�al is a brand-specific metric that provides an
es�mate of the por�on of total market poten�al that a
specific brand can reasonably expect to reach. In
4/1/2019 Print

Interna�onal markets provide poten�al growth
opportuni�es for
companies like Burger King. What must marketers consider
when
evalua�ng an interna�onal marketplace?
Associated Press
short, it is an es�mate of the size of the brand's target
market. When expressed in percentage terms, sales
poten�al indicates the size of the brand's target market
rela�ve to the available market.
The basic conceptual formula for calcula�ng market
poten�al remains the same in every
circumstance. However, the applica�on of the model must
be uniquely tailored to suit the situa�on.
Some�mes secondary data in the form of census
informa�on, other government sta�s�cs, or readily
available data from other published sources (e.g., trade
associa�ons) will be sufficient to meet the
needs of the analyst. In other instances, market research
studies are needed to develop primary data
specifically collected for this purpose. Internal company
data is a third source of sta�s�cal
informa�on that is some�mes overlooked if marke�ng
managers fail to consider how some types of
historical data may relate to the evalua�on of current
opportuni�es. For example, the firm's market-
specific sales databases may be applied to the study of
product development opportuni�es. Similarly,
the assessment of the market poten�al for a market
development opportunity may be related to

historical pa�erns in product-specific sales data.
Geographic market boundaries are largely irrelevant to the
market poten�al for many products that
are sold online. However, geographic concep�ons of
market poten�al s�ll hold significance for many
brick-and-mortar retailers, agribusiness firms, and service
companies. The delinea�on of an
organiza�on's geographic trade area will be defined by the
convergence of several factors such as the
size and number of nearby compe�tors, the uniqueness of
the product or service being offered, and
the mobility of prospec�ve buyers within the region.
The challenges related to geographic considera�ons
increase substan�ally when companies begin to evaluate
the market poten�al of foreign countries. Although
the complexity of the related tasks is amplified, the
fundamental conceptualiza�on of market poten�al does not
change.
Measures of Potential in International Markets
Given the simplicity of the concept, the challenges
encountered in developing metrics and models to es�mate
market poten�al can be surprisingly complex. For
example, one of the first steps that marke�ng managers
must take when evalua�ng new interna�onal markets for
their products is determining the financial
poten�al represented by compe�ng opportuni�es.
Typically, analysts are charged with assessing the market
poten�al across na�ons with widely disparate
economies and cultures. Rather than simply comparing
apples to oranges, it becomes a ma�er of comparing
rambutan to jabo�caba. The challenge is par�cularly
daun�ng when trying to iden�fy measures that accurately

reflect the true market poten�al of developing or
emerging na�ons.
Figure 4.1: Interna�onal Market Poten�al Index for 2010
4/1/2019 Print
Based on the informa�on in the indexing study above,
which countries seem to have the most and the least
market
poten�al and why?
This figure shows the dimensions that help influence
market poten�al. No�ce that some dimensions hold more
weight
than others. Why do you think this is the case?
h�p://globaledge.msu.edu/resourcedesk/mpi/
(h�p://globaledge.msu.edu/resourcedesk/mpi/)
To address this problem, the Michigan State University
Center for Interna�onal Business and Economic Research
(MSU-CIBER) annually publishes its Market
Poten�al Index for emerging markets. As illustrated in
Figure 4.1, this index value is calculated for the fastest-
growing emerging global markets using the eight
dimensions of market poten�al iden�fied in Figure 4.2.

Figure 4.2: Dimensions and measures of market poten�al for
2010
h�p://globaledge.msu.edu/resourcedesk/mpi/
(h�p://globaledge.msu.edu/resourcedesk/mpi/)
The use of mul�ple factors reflects different facets of
market poten�al. Each factor is determined by mul�ple
measures, and each is weighted rela�ve to its
importance to the overall poten�al of any given market.
Some measures such as market size and economic growth
rate make generally posi�ve contribu�ons to
overall poten�al. Factors such as limited economic
freedom and country risk (e.g., high poli�cal instability)
reduce the Market Poten�al Index value.
Though this is far from being a perfect representa�on of
market poten�al across all countries and products, it does
provide analysts with a preliminary assessment.
This is clearly a task worth pursuing, insofar as these
countries with underdeveloped economies account for more
than half of the world's popula�on, and
represent a huge sales poten�al for many types of
products.
Think About It
http://globaledge.msu.edu/resourcedesk/mpi/
http://globaledge.msu.edu/resourcedesk/mpi/
4/1/2019 Print

Assessing market poten�al within an interna�onal context
can be very difficult. However, the challenges confron�ng
small-business owners and entrepreneurs
are no less demanding. Assume that you are the owner of
a small retail pet store in Columbia, South Carolina.
What types of market poten�al measures would interest
you?
What inexpensive methods might you use to es�mate the
market poten�al?
4/1/2019 Print
Arizona Iced Tea products, once only popular in a small
consumer
niche, have increased in popularity and now appeal to
soda and tea
drinkers alike.
Associated Press
4.2 Comparing Performance to Potential: Gap Analysis
As noted earlier, the most common use of market demand
metrics is as a scorecard to evaluate the effec�veness of

the firm's marke�ng efforts. Measures of
market poten�al provide important baselines or benchmarks
against which an organiza�on can assess its performance.
One of the most useful techniques for
comparing sales poten�al to actual results is gap analysis.
Gap analysis is a term used to describe a range of
contrasts of interest to marke�ng managers. Each of these
comparisons examines actual performance rela�ve to
poten�al performance. Different types of gaps reveal
different areas of poten�al improvement or growth for the
organiza�on. Marke�ng managers are par�cularly
concerned with two market-related gaps that reflect a
difference between current sales and sales poten�al:
product usage gap and brand usage gap.
Product Usage Gap
The product usage gap is defined as the difference between
total market poten�al and actual or current product usage
by all customers within a given market. This
contrast is based on two measures of industry-wide volume
and is not directly related to the sales of any given
brand. Significant gaps between market poten�al
and actual usage indicates that there is an opportunity to
s�mulate primary demand for sales.
Product Usage Gap = Total Market Poten�al – Current
Product Usage
Consider the market for prepared iced tea. Not very long
ago it was regarded as a niche market in
the so� drink industry, limited to consumers who already
enjoyed brewed iced tea but who might

enjoy the convenience of bo�les and cans. Marketers'
percep�ons in this regard limited es�mates of
market poten�al. Current usage was almost exclusively a
func�on of product development—
introducing a new varia�on of the product to current
consumers. The introduc�on of brands like
Snapple and Arizona Iced Tea increased the size of the
total poten�al market through market
development. That is, new users and higher rates of
consump�on were added to the market as new
varie�es were promoted to a wider audience.
In most instances, closing the usage gap is a higher
priority for market leaders than companies that
hold small market shares. The firms that have already
achieved industry leadership are the most
likely to benefit from expanding the size of the market as
a whole. Market leaders some�mes opt to
acquire the innova�ve emerging brands that pioneer new
market segments as a cost-effec�ve path to
defending overall market share and rounding out the
company's por�olio of brands within a category.
This was the case when the Dr. Pepper beverages group
acquired Snapple in 2008.
A concept that is closely related to the product usage gap
is the market penetra�on index. This
measure is simply the ra�o of current market demand to
poten�al demand. Low market poten�al
index values indicate substan�al growth poten�al for all
the firms. High market penetra�on index
values suggest the costs of bringing those rela�vely few
new prospects into the market will be
rela�vely high.

Brand Usage Gap
Most companies do not sell brands into every segment of
a product market. This may be the inten�onal consequence
of strategic planning or the result of limited
resources. In either circumstance, product-level analysis of
the usage gap between total market poten�al and current
usage by all customers within a given market
will yield a distorted picture of the firm's performance. If
segments of the market where a company does not
ac�vely compete are included in the baseline
measure of total market poten�al, the actual effec�veness
of the marke�ng efforts on behalf of the exis�ng brands
will be underes�mated. That is, an assessment
of the compe��ve impact of brand-specific marke�ng
programs will be diluted by contras�ng current brand
sales to a level of poten�al market sales that includes
segments where the brand is not intended to compete. The
calcula�on of a brand-specific measure called the brand
usage gap is intended to compensate for that
poten�al error.
Brand Usage Gap = Target Market Poten�al – Current
Brand Usage
Brand usage gap is defined as the difference between
poten�al sales to the target market and the current level
of brand usage by customers within the targeted
segment of the market. The gap provides an indica�on of
how much poten�al growth is possible from taking market
share away from compe�tors. In this sense, it
relates most closely to market penetra�on strategies.
In a compe��ve market, it is highly unlikely that one
brand can drive the brand usage gap to zero by
elimina�ng the compe��on. Further, it is important to

note
that the presence of a gap does not necessarily indicate a
problem or marke�ng failure of any kind. The value in
calcula�ng brand usage gap values over �me is to
4/1/2019 Print
help managers remain aware of the outstanding or residual
financial value remaining in the market. The implica�ons
differ from simply tracking market share
stability over �me, especially in growing industries.
Consider the case of a leading brand in the young adult
mul�vitamin and mineral supplements market. If the
company's brand held a steady market share of 27
percent for five years, it might suggest that the market is
stable and offers few opportuni�es for growth. However,
if the number of young adults has increased by
45 percent over that interval, the brand usage gap as
measured in dollars will have grown substan�ally.
Consequently, brand managers may wish to pursue a more
aggressive market penetra�on strategy since the total
dollar volume and corresponding profits have grown so
quickly. The cost of acquiring each addi�onal
percentage point of market share may have remained
virtually unchanged, but the value of each incremental
point has increased considerably.
An alterna�ve measure that is closely related to the brand
usage gap is the firm's share penetra�on index. This

measure is simply the ra�o of the brand's current
sales to its poten�al sales. If the index values for this
measure are low, the company may have opportuni�es to
greatly expand its market share. Marke�ng mix
strategies to build brand awareness and preference could
yield substan�al gains at rela�vely low cost. Much like
the market penetra�on index, however, high share
penetra�on index values suggest the incremental cost of
acquiring addi�onal share points will be rela�vely high.
Each of the gaps iden�fied in this sec�on suggests
opportuni�es for growth that stem from a disparity
between what consumers demand and the characteris�cs of
the products offered by compe�ng companies. It is
important to remember, however, that not every gap can
be bridged collec�vely by the sum of the alterna�ve
brands. Some sets of demands are simply beyond the
market's scope or ability to meet. Similarly, each brand
cannot realis�cally expect to meet the demands of
every consumer in its defined target market.
One of the many valuable insights that can be gleaned
from gap analysis is a be�er understanding of how the
sales volume of a brand compares to its poten�al
performance. These types of contrasts provide marke�ng
managers with the opportunity to evaluate areas of
possible improvement. Sales forecasts, however, serve
a dis�nctly different purpose. These predic�ons about
future sales are primarily intended to help marke�ng
managers improve the match of company resources to
an�cipated brand-specific demand and improve the
efficiency of marke�ng programs.

4/1/2019 Print
The accuracy of forecas�ng affects every aspect of the
sales planning
process. Perhaps the most cri�cal part of forecas�ng is
the sales
forecast. How does forecas�ng affect the marke�ng of a
product?
4.3 Sales Forecasting
Sales forecas�ng is generally regarded as a specialized
subfield within the domain of market research that
provides predic�ons about markets, customers, and
compe�tors. Almost all forms of marke�ng-related
forecasts relate either directly or indirectly to predic�ng
future sales. Predic�ons are most o�en based on
historical data, observed trends, and expert opinion.
Accurate forecasts are essen�al to the efficient
management of current opera�ons, assessing new market
opportuni�es, and evalua�ng the effec�veness of ongoing
marke�ng programs. Reliable predic�ons of how brand
sales will respond to future changes in
marketplace dynamics are essen�al to reducing uncertainty,
and consequently risk, to the lowest levels possible.
Forecasts can span different �me frames
depending on the purpose of the forecast and availability
of informa�on.
Sales Forecas�ng

Short-Range Forecasting
Short-range forecas�ng refers to making predic�ons less
than one year into the future. These are fairly rou�ne
forecasts that o�en reach managers' desks in the
form of monthly or quarterly projec�ons of sales. They
provide some of the most cri�cal data that are used in
managing the opera�ons of the firm. Short-range
forecasts are essen�al to making scheduling and alloca�on
decisions about near-term produc�on capacity. An�cipa�ng
sales levels also permits manufacturers to
keep the costs of carrying large inventories of raw
materials and unfinished goods to a minimum. Accurate
forecasts are par�cularly cri�cal in labor-intensive
manufacturing industries where human resource costs
represent a large percentage of unit costs. Matching
capacity to demand not only improves efficiency but
also reduces the likelihood of stock-outs and shortages
that result in dissa�sfied customers.
Medium-Range Forecasting
Medium-range forecas�ng refers to annual forecasts or
predic�ons. These projec�ons are typically used as
primary inputs to the annual budget planning process.
This is the level of data most o�en used in se�ng or
revising the marke�ng plan for each brand and SBU
within the organiza�on's por�olio. Alloca�ons and
adjustments to the marke�ng variables are o�en made in
direct response to data from these forecasts.
The promo�onal mix elements (e.g., adver�sing, personal
selling) are o�en the features of the marke�ng plan most
directly impacted by sales projec�ons. Although
sales forecasts play a pivotal role in the establishment of
sales budgets, they are not the same thing. Forecasts

predict future sales, and budgets specify the
planned alloca�on resources to accomplish the
organiza�on's goals. When used correctly, there is a
cause-and-effect rela�onship between the two. The sales,
adver�sing, and marke�ng budgets for a brand should be
based on the goals for the brand as well as the
corresponding level of forecast sales.
Long-Range Forecasting
Long-range forecas�ng refers to making predic�ons for
periods greater than one year into the future. The most
common use for these long-term projec�ons is
strategic planning. Since these predic�ons extend farther
into the future, they are generally more difficult to
construct and tend to be less accurate. However, they
provide essen�al inputs to the process of shaping the
strategic direc�on of the organiza�on over the long run.
Both the providers and users of long-range marke�ng
forecasts recognize the constraints and limita�ons
associated with predic�ons about events more than a year
in the future. These projec�ons are necessarily more
specula�ve in some markets than others. However, the
discipline of having to specifically quan�fy the
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ch04_eoc&content… 10/20
Small companies, such as this local bakery, as well as
large

companies use sales forecasts to help them make decisions
that
will lead to improvement and success.
Ambient Images Inc./SuperStock
Outsourcing manufacturing can reduce produc�on costs and
improve the firm's ability to compete on price in global
markets.
Imaginechina/Associated Press
expecta�ons associated with various op�ons and scenarios
is essen�al to good strategic planning. It improves the
rigor and strengthens the commitment that
par�cipants bring to the process. However, even under the
best of circumstances, the accuracy of short-range,
medium-range, and long-range forecasts is
necessarily limited by a range of complica�ng factors.
Think About It
As with other decision making aids we have looked at in
previous chapters, marke�ng managers cannot afford to let
forecasts do their thinking and decision
making for them. There are risks associated with becoming
too dependent on long-range predic�ons, just as there are
nega�ve consequences associated with
ignoring short-term sales forecasts.
What can managers do to avoid these kinds of problems?
Limitations of Sales Forecasting

Sales forecasts are used extensively as cri�cal inputs to
decision making throughout both small and
large companies. Accurate forecasts of sales are essen�al
to making good decisions in areas related
to opera�ons management, human resource planning, and
marke�ng. In marke�ng, these predic�ons
are used to evaluate the poten�al profitability of new
ini�a�ves, evaluate the effec�veness of exis�ng
programs, and establish future direc�ons for the firm. In
light of the importance of sales forecasts, it
is necessary to recognize the limita�ons and poten�al
problems that are built in to the process of
crea�ng them.
Sales forecasts can only take into account a limited
number of market-related condi�ons. Most forms
of market forecas�ng rely on very limited sta�s�cal
models of the environment. By defini�on, models
are incomplete, abstract representa�ons of the environment
they represent. The real world is far
messier, and it is very difficult to an�cipate or assess all
of the factors that will impact brand sales.
A closely related problem is that most forecas�ng models
assume that the basic condi�ons and
events observed in past periods will con�nue into the
future. However, compe��ve environments are
dynamic and subject to change. New entrants to the
market, the introduc�on of subs�tute goods or
new technologies, significant changes in the marke�ng mix
decisions made by compe�tors, and one-
�me events or shocks to the market or economy cannot
be an�cipated by forecas�ng models.
The accuracy and reliability of sales forecasts is also
limited by the quality of available data. For

example, some forecas�ng models require informa�on on
the marke�ng expenditures and alloca�on
decisions made by compe�tors. Few firms have any
incen�ve to make this type of informa�on
publicly available. In B2B contexts, prices and other terms
of trade may change significantly from one
customer to the next. Consequently, the data used to
construct profiles of compe�tor behavior o�en
need to be es�mated before being subsequently used to
construct forecasts. This added layer of
poten�al error can render medium- and long-range
forecasts unreliable in many industries.
Finally, market-related variables are always interac�ng in
fairly complex ways that are difficult to
capture in sales forecasts. Consider the case of a major
appliance manufacturer from China that
wishes to improve its U.S. market rank from fi�h to third
for convec�on ovens. In the span of 12
months, the company may opt to reduce retail prices,
decrease adver�sing, and increase sales
promo�ons to wholesale distributors. Assume that this is
happening against the backdrop of a
slowing na�onal economy. The impact of any one of
those changes is dependent on the others to a
greater or lesser extent. That is, the net impact is almost
certainly different from the sum of the
individual changes. The interac�on of these changes with
the compe��ve response from other
brands adds a second order or �er of complexity.
Think About It
Consider the case of the toy store owner who developed
her sales forecasts based on economic indicators within
her rapidly growing community and previous

years' sales trends. She was an�cipa�ng 15 to 18 percent
growth in the coming year, but actually lost nearly half
her sales over the prior period.
Assuming that her forecas�ng model was specified
correctly, what internal and external factors could possibly
account for the huge discrepancy?
Though never perfectly precise in its predic�ons, the
complex task of sales forecas�ng provides informa�on
that is essen�al to the firm. Short-, medium-, and long-
range projec�ons are used in many se�ngs and
applica�ons. As with many other facets of the
organiza�on, being very good at forecas�ng future events
provides a
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dis�nc�ve competence or capability that can be translated
into a compe��ve advantage for the company. The
sec�on that follows examines the range of
quan�ta�ve and qualita�ve forecas�ng techniques
available to market analysts.
Why Good Forecasts Go Bad
Sales forecasts some�mes seem much more objec�ve and
unbiased than they are because they produce such precise
quan�ta�ve projec�ons. However, it
would be naive to believe that the processes and outcomes

related to the sales forecas�ng are immune from personal
bias. "I have never seen (nor heard of) a
sales forecast that was accurate, except by accident. The
reason is simple: the poli�cs of self-interest makes
accurate forecasts virtually impossible" (Geoffrey,
2011).
It's human nature. Whether inten�onal or uninten�onal,
sales forecasts are necessarily biased by the experience,
priori�es, and goals of the people involved in
their crea�on. Consequently, the people most in�mately
involved with the prepara�on of sales forecasts o�en have
much more faith in their accuracy than the
sales force that is o�en most directly affected by them.
So, how do bad forecasts emerge from the offices of
professionally qualified managers and experienced
execu�ves? The inaccuracy of many sales forecasts can
be a�ributed to the unrealis�c expecta�ons of execu�ves
and their failure to acquire accurate input from the
salespeople in the field (Warner, 2012). James
Geoffrey (2011), the author of the world's most-visited
sales blog, humorously describes the chao�c process of
crea�ng forecasts as a nine-step process:
Step #1: Top management needs a story to tell investors,
and thus asks the sales manager for a forecast.
Step #2: The sales manager asks the sales reps what they
think they'll sell, based upon their current gut feeling.
Step #3: The sales reps make a wild guess at what they
might actually sell, and then subtract 10 percent as a
fudge.
Step #4: The sales manager compares that forecast to the

sales quota and adjusts both to match.
Step #5: The marke�ng group issues its own forecast,
based upon sta�s�cs from a "market research" firm.
Step #6: The manufacturing group ignores all of the above
and forecasts what it plans to manufacture.
Step #7: Top management looks at the three forecasts,
throws them out, and then makes up numbers that will
look good.
Step #8: The accountants juggle the books so that,
regardless of what was sold, the quarterly report resembles
the CEO's story.
Step #9: If, for some reason—like the laws of
mathema�cs—Step #8 isn't good enough, the CEO fires
the sales manager.
Though somewhat exaggerated, this nine-step model clearly
illustrates how accuracy and forecas�ng discipline can be
sacrificed to the serve the self-interest of
mul�ple par�cipants in the process. Accurate sales
forecas�ng begins with a commitment to making effec�ve
use of tangible, objec�ve sources of data. The
ini�al forecast can be created from mathema�cal models
that rely on trend analysis based on previous years' sales,
seasonal buying pa�erns, recent marke�ng
expenditures, and general economic factors. Once fine-
tuned and tested against historical data, this type of model
can provide a baseline to build from.
The refinement of mathema�cally driven sales projec�ons
by subjec�ve judgment is both unavoidable and essen�al
in many instances. To minimize the impact

of self-interest bias at this stage of the process, Geoffrey
suggests two basic strategies.
First, decouple forecasts from quotas. Sales quotas are
valid tools in managing and mo�va�ng the sales force.
However, they should not have a role in driving
the development of forecasts. The de-coupling of sales
quotas and forecasts, though difficult, should remove
pressure from the process and enhance the
accuracy of the informa�on provided by the sales
representa�ves in the field.
Second, eliminate unnecessary influencers from the process.
If everyone within the organiza�on gets involved in the
forecas�ng process, it will necessarily
reflect the sum of everyone's personal and professional
biases. Star�ng with an independent, mathema�cally driven
ini�al sales forecast, only marke�ng,
manufacturing, and top management should be involved in
the considera�on of subsequent revisions. Any substan�al
changes necessarily require the evalua�on
of suppor�ng data so that informed revisions can be made
rather than changes driven by self-interest (Geoffrey,
2011).
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This line graph shows an increase in sales over �me.

Using a graph like this allows marketers to understand
their
products' trends and to predict their products' future
success.
4.4 Forecasting Techniques
A range of approaches and methodologies can be applied
to the predic�on of future sales growth. Some are
dependent on modeling sta�s�cal rela�onships
between data, while others rely on the expert opinion of
business professionals. The choice of a sales forecas�ng
technique in any given situa�on depends on
several factors, including the required level of predic�ve
accuracy, the availability of reliable data, and how far
into the future projec�ons need to be made. None
of the exis�ng techniques for forecas�ng sales is superior
under all condi�ons. Both quan�ta�ve and qualita�ve
methodologies have specific strengths and
weaknesses. However, the appropriate applica�on of
mul�ple measures and techniques to any given problem
will invariably serve to refine and fine-tune the
accuracy of the final forecast.
Quantitative Forecasting Methods
Quan�ta�ve forecas�ng methods primarily rely on the
analysis of historical data to predict future sales. The
sta�s�cal techniques and data sets used in crea�ng
quan�ta�ve forecasts make the process rela�vely
objec�ve in contrast to methods that depend on the expert
opinion and judgments of people. In general, most
marke�ng managers are more confident in quan�ta�ve
approaches when reliable data are available, the market
environment is rela�vely stable, and their previous
experience with specific forecas�ng models has
demonstrated that the past can be a good predictor of the

future events.
Simple Trend Extension
The simplest method for forecas�ng future sales from past
sales is called naive forecas�ng. It assumes no vola�lity
in sales whatsoever and simply predicts that
sales for the next period will be the same as sales for
the previous period. Though simple, the assump�on of no
change is seldom realis�c.
St+1 = St where S = sales and t = �me period
In rela�vely stable markets, there are situa�ons in which
the overall trend in sales growth is clear, but not flat.
For example, year-to-year sales growth for pens and
mechanical pencils typically mirrors the rate of change in
popula�on. Consequently, the rate of growth is slow, but
posi�ve and steady. If sales data over �me
exhibit a generally linear trend, the sta�s�cal method of
simple linear regression analysis may be used to determine
a trend line sales predic�on for the future
period's forecast. (The general principles of regression
analysis are discussed in the sec�on on mul�variate
regression analysis.)
Figure 4.3: Simple trend extension
In simplest terms, this approach to forecas�ng the next
period's sales is to graph the pa�ern of sales over �me
and simply extend the observed trend line for one
addi�onal period as shown in Figure 4.3.
Time Series

Quan�ta�ve methods of forecas�ng are based on an
analysis of historical data specific to one or more
intervals called a �me series. A �me series of data is a
set
of quan�ta�ve observa�ons collected at successive points
in �me over a specified period. For example, the monthly
sales of a given brand over a two-year interval
would make up a set of �me series data with 24
observa�ons.
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This sca�er plot diagram shows the components of
forecast demand. With a diagram like this, a marketer can
decipher the trend from the fluctua�ons and random noise
of the market sales.
If the historical �me series data set is used exclusively
to predict future values of the same variable (e.g., 2007–
2011 unit sales data are used to forecast 2012 unit
sales), then the process is called a �me series study.
However, if the historical �me series data are combined
with other �me series data sets related to the
variable that we wish to forecast, the analysis is called a
causal study. For example, if you combine the 2007–2011
unit sales data with a comparable data set on
product pricing over the same interval to predict 2012
unit sales, it would be considered a causal analysis.
The purpose of both �me series methods is to

sta�s�cally isolate the underlying trends in the data from
random noise and fluctua�ons. Once the trend is
iden�fied, the historical data provide the baseline
measures from which predic�ons about future levels can
be projected. Whether building a rela�vely simple �me
series study or a highly sophis�cated causal model,
forecasts derived from this approach typically feature four
major elements: trend, seasonal, cyclical, and
irregular components.
The trend component accounts for the gradual shi�ing of
the �me series data over a long period of �me. This can
be thought of as the core element of the
forecast that provides the general projec�on of growth or
decline for the product.
The seasonal component of a �me series accounts for
regular, recurring pa�erns of variability at specific �mes
of the year. Many products exhibit some seasonality
in sales as func�on of when they are used (e.g.,
snowblowers), specific purchase occasions (e.g., Easter
baskets), or annual events (e.g., children's back-to-school
lunch boxes). Figure 4.4 illustrates the linear trend and
seasonal components of forecast demand.
Figure 4.4: Components for forecast demand
The cyclical component incorporates any regular wavelike
pa�ern in the sequences of values. When graphed, these
cyclical influences o�en appear as arcs rising
above and dipping below the core trend line. These cycles
are independent of any seasonal effects and can be very
difficult to detect. Analysts need to understand
the source of the pa�ern to determine whether these
cyclical components can be expected to reliably recur in

future periods.
The irregular component of a �me series study is caused
by short-term, unan�cipated, and non-recurring factors that
impact the observed values in the data set.
The impact of these random, unpredictable events (e.g., a
natural disaster, labor dispute) on the forecast values of
the model cannot be predicted in advance.
The �me series study approach to forecas�ng assumes
that future demand can be predicted from the analysis of
historical data. In this way, fairly objec�ve
methods are applied to mi�gate the risks posed by future
uncertainty. Time series studies of all kinds, however, can
some�mes be fine-tuned to make them more
accurate by transforming the data used as inputs to the
sta�s�cal analysis process. Exponen�al smoothing, for
example, is a process for weigh�ng the observa�ons
from previous sales periods to place greater emphasis on
the most recent data. This reflects the pa�ern most o�en
observed in sales data: that historical
observa�ons gradually lose their value as �me passes and
market condi�ons change.
Mul�variate Regression Analysis
Mul�variate regression analysis is the most common
technique used to carry out causal �me series analyses. It
is a sta�s�cal forecas�ng tool that iden�fies
rela�onships between sales (the dependent variable) and
one or more influencing factors (independent variables). If
only one independent variable is used to
forecast sales (e.g., household income), the model is called
a simple linear regression, and the results can be
illustrated by a line graph.

This mathema�cal rela�onship can be used to predict
future values of sales based on changes in the independent
variable. When more than one independent
variable is considered, however, it is called a mul�variate
regression.
The mul�variate regression approach to forecas�ng permits
analysts to iden�fy mul�ple variables that have a causal
influence on future sales. Consider, for
example, how many factors might contribute to the retail
sales of word processing so�ware. Among the variables
that would have the most direct influence is
computer sales. However, a simple model using historical
sales data on word processing so�ware and retail
computer sales could be made more accurate by
including other factors that are not as directly related to
so�ware sales. Growth rates in college enrollment or even
the popula�on of high school students might
4/1/2019 Print
Microso� Windows dominates the word processing
so�ware
market. To be this profitable, Microso� examines the
variables that
affect its success in the market, such as college
enrollment and
business pa�erns.

Associated Press
have predic�ve validity. For forecasts within a specific
geographic market, popula�on growth,
employment levels, and pa�erns in home business startups
might have explanatory power as well.
The power of mul�variate regression analysis to
inves�gate these rela�onships is based on the
iden�fica�on of variables that are significantly correlated
with retail so�ware sales.
Forecasters can incorporate a wide range of possibly
useful variables in ini�al model trials to test
their explanatory power against historical data. The
sta�s�cal models in the form of mathema�cal
formulas will indicate which variables have significant
correla�ons with the dependent variable. Some
will have a posi�ve rela�onship with sales (e.g.,
compe�tors' prices), while others may have an
inverse rela�onship (e.g., own brand prices). By running
prac�ce tests against different subsets of
�me series data, some correla�ons between the two will
emerge as truly causal in nature. The final
model that emerges from this itera�ve trial-and-error
process will include the set of mathema�cal
rela�onships between variables that are the most effec�ve
in predic�ng future sales.
Mul�variate regression analysis is a poten�ally powerful
tool. It can incorporate a wide range of
variables and evaluate their reliability as sales predictors.
Among the marke�ng mix variables most
commonly included in these causal models are changes in
adver�sing expenditures, sales promo�ons,
and pricing at various points in the channel of product
distribu�on. Causal approaches to forecas�ng

tend to be complex and provide the means to recognize a
wide range of poten�al influences on product sales,
including interac�ons between independent
variables.
Qualitative Forecasting Methods
Quan�ta�ve forecas�ng methods rely exclusively on
inferences from the extrapola�on of trends in historical
data to predict future sales. By contrast, qualita�ve
forecas�ng techniques u�lize the judgment and expert
opinion of knowledgeable professionals to generate
forecasts. A primary advantage to these methods is that
they can be applied in situa�ons where historical data are
not available. However, even in contexts where historical
sales data are readily available, qualita�ve
forecas�ng methods can be used to qualify or fine-tune
sta�s�cal projec�ons.
Expert opinion is a subjec�ve approach to forecas�ng that
relies exclusively on the judgment of individuals who
possess specialized knowledge of buyers,
compe�tors, and products. Companies o�en rely on
experts from a variety of sources to provide their unique
insights on the market. These sources may include
customers, suppliers, channel intermediaries, trade
associa�ons, or consultants. The opinions provided by
these experts may be influenced by quan�ta�ve forecasts,
but their forecast is typically an independent extension of
judgment beyond projec�ons grounded in historical data.
The complexity of compe��ve business environments
poses a significant challenge to sta�s�cal forecas�ng. It
simply cannot account for all of the factors and
interac�ons between factors that influence market-related
behaviors at any given �me. The advantage of expert

opinion and judgment over sta�s�cal forecas�ng
methods is the ability of the human mind to synthesize
informa�on from a wide range of qualita�ve and
quan�ta�ve sources.
The Delphi method is a qualita�ve method that relies on
the interac�on of mul�ple expert sources to develop
forecasts through group consensus. Although the
specific steps in the methodology may differ from one
organiza�on to the next, the process is rela�vely simple.
Ini�ally, the company that wants to develop a
forecast contacts a group of experts from different
backgrounds to answer the same set of structured
ques�ons about the future of the product or brand of
interest. Each of the experts completes his or her
responses independently and returns them to a coordinator.
Subsequently, each par�cipant in the process
receives feedback about the anonymous responses of the
other forecasters. In this second itera�on, the team
members return their revised responses based on the
input from round one.
This Delphi process is an itera�ve one that con�nues
un�l there is a rela�vely high degree of agreement and
certainty among the forecasters' responses. Although
there are many varia�ons on the specific procedures used,
in every instance the objec�ve of this endeavor is to
build a consensus view of future events.
Expert panel methods are similar to the Delphi method
insofar as they rely on the opinions and judgment of
many professionals. However, expert panels are
assembled and brought together to directly share and
compare views. Individuals' forecasts are each given due
considera�on before the par�cipants begin working
to converge on one common forecast for the future.

In most instances, the expert panel is drawn from diverse
professional backgrounds (e.g., academics, corporate
execu�ves, consultants). However, there are �mes
when the forecas�ng task requires a gathering of
individuals with a common background. Some companies
rou�nely gather their sales representa�ves to provide
insights on the market and sales forecasts for the next
period. Since members of the sales force are in immediate
contact with both exis�ng customers and
prospec�ve buyers on a daily basis, they can be a
valuable and reliable source of marke�ng intelligence.
The gathering of the sales force described above is
some�mes combined with simple quan�ta�ve forecas�ng.
In this version, called the sales force composite
method, each salesperson prepares forecasts for his or her
own territory. Prior to gathering together as an expert
sales panel, the forecasts are consolidated across
products, brands, and geographic regions and distributed
for subsequent discussion by the whole group.
Another varia�on on the basic model of expert panels is
the jury of execu�ve opinion. In this version, par�cipa�on
is limited to the top execu�ves within a
company. Though farther removed from the field than
salespeople, execu�ves possess a perspec�ve on the larger
trends and market developments that is essen�al
to making good decisions on new products and emerging
technologies.
4/1/2019 Print

Accurate forecas�ng requires both the analysis of
historical data
and subjec�ve judgments about the future.
Photodisc/Thinkstock
Simula�on is a final varia�on on the use of expert
opinion that can be combined with any of the
procedures described above. A simula�on consists of
providing experts with several wri�en,
conceptual scenarios of the future based on clearly defined
assump�ons. Par�cipants are asked to
respond to the scenarios by indica�ng which they believe
is most likely to occur. In many instances,
the discussions about the validity and reliability of the
assump�ons built into each of the scenarios
provide valuable insight by itself. However, the final
product of this procedure is the same as with
other forms of qualita�ve forecas�ng: to build a
consensus predic�on of future events.
Market methods of sales forecas�ng are qualita�ve
techniques that do not depend on the expert
judgment of business professionals, but rather rely on the
opinion of current customers and
prospec�ve buyers. However, it is worth no�ng that
market surveys are not typically designed and
executed only for the sake of forecas�ng, since many of
the same insights can be gleaned from a
combina�on of the company's internal data and external
published sources.

Similarly, market tests are used to gauge consumer
responses and assess the viability of a wider
product introduc�on or market rollout. The results of
these field tests for new product concepts are
intended to assess the interest of poten�al customers in
buying the product. The actual sales
forecasts and other measures of a product's poten�al are
completed prior to field tes�ng a new product concept.
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Ch. 4 Conclusion
Quan�ta�ve forecas�ng methods provide predic�ons of
future sales based on the analysis of historical sales data
and an�cipated future market condi�ons.
Qualita�ve approaches allow marke�ng managers to
incorporate the judgment and opinions of experts within
the field. Forecasts enable companies to evaluate
current performance and prepare for future sales levels.
The predic�on of slowing or declining sales provides an
opportunity for firms to re-evaluate their
marke�ng plans and explore alterna�ve opportuni�es for
growth.
Accurate predic�ons of future demand growth enable
organiza�ons to compete and func�on more effec�vely by
making their opera�ons more efficient. Reliable
sales forecasts can facilitate more efficient produc�on
planning, improve customer service, and generally improve

the alloca�on of the firm's resources to match
consumer demand. In this way, the compe��ve advantage
provided by be�er sales forecas�ng can result in lower
costs, higher margins, and improved levels of
customer sa�sfac�on and brand loyalty.
4/1/2019 Print
Ch. 4 Learning Resources
Key Ideas
Key Terms
Click on each key term to see the defini�on.
available market
(h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�o
ns/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/b
ooks/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.
12
Those buyers who possess both the interest and means to
purchase a specific product under current market
condi�ons.
brand usage gap

12
The difference between poten�al sales to the target market
and the current level of brand usage by customers within
the targeted segment of the market.
causal study
12
An analysis in which historical �me series data (e.g.,
sales) are combined with other �me series data (e.g.,
adver�sing expenditures) related to the variable being
forecasted (e.g., future sales).
cyclical component
12
The element in a �me series study that incorporates any
regular wavelike pa�ern in the sequences of values.
Delphi method
12
A qualita�ve forecas�ng method that relies on the
interac�on of mul�ple expert sources to develop forecasts
through group consensus.

expert opinion
12
A subjec�ve approach to forecas�ng that relies
exclusively on the judgment of individuals who possess
specialized knowledge of buyers, compe�tors, and
products.
expert panel methods
12
A qualita�ve forecas�ng method that relies on the
opinions and judgment of many professionals. Unlike the
Delphi method, however, expert panels are assembled
and brought together to directly share and compare views.
exponen�al smoothing
12
A process for weigh�ng the observa�ons in �me series
data to place greater emphasis on the most recent
observa�ons.
gap analysis

12
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4/1/2019 Print
A variety of comparisons that examine the difference
between actual performance and poten�al performance on
measures of interest to marke�ng managers.
irregular component
12
The element of a �me series study that is caused by
short-term, unan�cipated, and non-recurring factors that
impact the observed values in the data set.
jury of execu�ve opinion

12
The top execu�ves within a company serve as an expert
panel to consider long-range trends that may impact the
organiza�on.
long-range forecas�ng
12
Making predic�ons for periods greater than one year into
the future.
market demand
12
The total volume or amount of a specific product
purchased by consumers over the complete range of prices
at which it is sold.
market methods
12
Qualita�ve sales forecas�ng techniques that rely on
tapping the opinion of current customers and prospec�ve

buyers.
market penetra�on index
12
The ra�o of current market demand to poten�al demand
level.
market poten�al
12
A measure of the maximum total possible sales of a given
product over a fixed interval of �me.
Market Poten�al Index
12
A mul�-factor index of market poten�al for emerging
global markets created by the Michigan State University
Center for Interna�onal Business and Economic
Research.
medium-range forecas�ng
12

Making annual forecasts or predic�ons.
mul�variate regression analysis
12
The most common technique used to carry out causal �me
series analyses. This sta�s�cal forecas�ng tool iden�fies
rela�onships between sales (the dependent
variable) and one or more influencing factors (independent
variables).
naive forecas�ng
12
The predic�on that sales for the next period will be the
same as sales for the previous period.
product usage gap
12
The difference between total market poten�al and current
product usage by all customers within a given market.
qualita�ve forecas�ng techniques

12
Using the judgment and expert opinion of knowledgeable
professionals to generate forecasts.
quan�ta�ve forecas�ng methods
12
Using the analysis of historical data to predict future
sales.

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