PERSONAL SELLING:
FORECASTING
market potential
an estimate of the maximum possible sales of a good or service for
an entire industry during a stated time period
sales potential
refers to the maximum market share that a particular firm can achieve
under ideal conditions
sales forecast
an estimate of the dollar or unit sales for a specific future period
under a proposed marketing plan or program for an individual firm
NOTE: a forecast is what is realistically expected, not what is hoped
or desired
How might you predict demand for:

wind generators in 2025

Ford Escorts next year

size 3a rivets made by Acme Mfg.

refrigerators in two years

Wendy's menu strips made by VFSign Co.
FACTORS UNDERLYING FORECASTING PREMISES:
Controllable Factors
those that are under control of the firm

pricing

distribution

promotion

product characteristics

product mix

account policies

choice of customers

etc.
Uncontrollable Factors
environmental elements over which the firm has little, if any, direct
control

economy, interest rates, inflation

public policy, government regulation

political conditions

market factors, changing demographics

competitors, competitor actions

supplies, supplier actions

industry trends

etc.
FORECASTING
Three Basic Approaches:

Judgmental / Qualitative

Relational

Analytical / Quantitative
Judgmental / Qualitative Techniques

subjective; based on a hunch, intuition

assume that somebody knows the answer and ask them

experience based

subjective: might result in bias
Judgmental / Qualitative Techniques

Jury of Executive Opinion

Sales Force Composite

User's Expectation

Delphi Techniques

Scenario Method
Judgmental / Qualitative Techniques
Useful for:
long range forecasting

e.g., where technological, political, etc. factors play a significant role
when data is limited or nonexistent
Relational Techniques

assume cause and effect, and cause can be used to predict sales

if you know one variable, you can forecast the other
Relational Techniques

leading indicators

e.g., housing starts suggest refrigerator sales

e.g., births suggest college enrollments

regression techniques

assume a straight line; cannot account nonlinear sales

in some cases, assumes a causal relationship between time and sales (don't
repeat this one on a stats exam!)

use a ruler for "eyeball regression"
Analytical / Quantitative Techniques
time series approaches

assume that historical data can be used to predict future demand

all we look at is historical data over time used to reduce the element
of subjectivity
trend
used to describe a time series that is not flat
stationarity
used to describe a time series as flat
Analytical / Quantitative Techniques
Four Approaches:

naive

cumulative mean

moving average

exponential smoothing
NOTE: cumulative mean is mentioned to develop insights into these methods
and is generally not a method that is used in practice.
Idea behind what we will be doing:

we want to smooth the data

we want to find the pattern in the noise
NAIVE APPROACH
S_{t+1} = S_{t}

cumulative mean looks at all data

naive approach looks at no data past the present

forecast for the next period is the same for the last period

works best when data follows a "random "walk" or is very noisy

best in the short run, not so good in the long run

does not work with data that is trended or has a clear pattern

assumes high volatility
CUMULATIVE MEAN
S_{1} + S_{2 }+ . . . + S_{t
}S_{t+1} = 
t

assumes that all data are equally relevant

never throw anything out

not frequently used
EXAMPLE: CUMULATIVE MEAN
period sales forecast
1 16,250 
2 17,000 16,250
3 20,000 16,625
4 16,000 17,750
5 15,000 17,312
6 17,250 16,850
7 18,000 16,917
8 20,000 17,071
9  17,438
MOVING AVERAGE
S_{t} + S_{t1} + S_{t2} + . . . + S_{t(N+1)
}S_{t+1} = 
N
idea

we want to try to "average out" the forecast to cancel out noise

looks for some sort of trend up or down; attempts to smooth out the trend,
but always lags behind the trend
small N
the forecast will quickly respond to changes, but we lose the "averaging
out" effect which cancels out noise
large N
we get good averaging out of noise, but poor response; sluggish
N is usually chosen by trial and error. Whatever has worked the
best in predicting past data is presumed to be the best for predicting
the next period.
EXAMPLE: 3PERIOD MOVING AVERAGE
Note: a "period" is some amount of time. It could be a year, a month,
a week, an hour, or a millisecond.
sales
for
period sales period forecast
1 16,250 
2 17,000 
3 20,000 53,250
4 16,000 53,000 17,750 (53,250/3)
5 15,000 51,000 17,667 .
6 17,250 48,250 17,000 .
7 18,000 50,250 16,080 .
8 20,000 55,250 16,750 .
9  18,417 (55,250/3)
EXPONENTIAL SMOOTHING
S^_{t+1} = aS_{t} + (1a)S^_{t}
where
a is a smoothing constant
naive forecast acts as though only the most recent observation has
any forecasting value; all prior observations are treated as worthless
cumulative mean procedure ignores the age of the observation;
all observations are treated as equally relevant, no matter how old the
observation
moving average acts as though the last N periods of data
are equally useful but that all prior observations are worthless
It might seem reasonable that historical observations gradually lose
their value rather than so abruptly as in the moving average.
This idea leads to the concept of weighted moving averages.
EXPONENTIAL SMOOTHING

assumes that the most recent data is the most valuable

assumes that data gradually loses its value over time

similar to moving average except:

most recent sales are weighted more heavily

older sales weighted less
S^_{t+1} = aS_{t }+ (1a)S^_{t}
where
a is a smoothing constant
large a

fast smoothing

heavy emphasis on new data

highly responsive but "nervous" to noise
small a

slow smoothing

heavier reliance on older data

sluggish response but calm to noise
The value of the smoothing constant is usually chosen by trial and error.
Whatever has worked the best in predicting past data is presumed to be
the best for predicting the next period.
EXAMPLE: EXONENTIAL SMOOTHING
using
a = .8
period sales forecast
1 16,250
2 17,000 16,250 (use naive to seed)
3 20,000 16,850 (.8)(17,000) + (.2)(16,250)
4 16,000 19,370 (.8)(20,000) + (.2)(16,850)
5 15,000 16,774 (.8)(16,000) + (.2)(19,300)
6 17,250 15,335 .
7 18,000 16,867 .
8 20,000 17,773 .
9  19,555 (.8)(20,000) + (.2)(17,773)
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