Download Sales forecasting

Part I
THE BIG PICTURE
Sales Management Resources:
Estimating Potentials and
Forecasting Sales
WHY FORECAST?
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One of the keys to success in sales is knowing where
customers are located and being able to predict how
much they will buy. Firms have found that sales
potential data are indispensable to developing a sales
program, particularly in setting up territories,
assigning quotas, developing budgets, and comparing
sales performance of individual salespeople. Sales
forecasting is so important that more than 50 percent
of firms include this topic in their sales manager
training programs.
Inaccurate demand predictions can have disastrous
effects on profitability.
QUALITATIVE SALES
FORECASTING
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Sales forecasting is concerned with predicting future
levels of demand. These projections are vital for
budgeting and planning purposes. For new products,
a few simple routines can be employed. The absence
of past sales means that you have to be more
creative in coming up with predictions of the future.
Sales forecasts for new products are often based on
executive judgments, sales force projections,
surveys, and market tests.
We will begin our discussion of forecasting
techniques by focusing on subjective methods that
are based on interpretations of business conditions
by executives and salespeople.
Sales Force Composite
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A favorite forecasting technique for new and
existing products is the sales force composite
method. With this procedure, salespeople
project volume for customers in their own
territory, and the estimates are aggregated
and reviewed at higher management levels.
The territory estimate is often derived based
on demand estimates for each of the largest
customers in the territory, the remainder of
the customers as a group, and then for new
prospects.
Jury of Executive Opinion
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This technique involves soliciting the
judgment of a group of experienced
managers to give sales estimates for
proposed and current products. The main
advantages of this method are that it is fast
and it allows the inclusion of many subjective
factors such as competition, economic
climate, weather, and union activity.
Leading Indicators
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Where sales are influenced by basic changes in the
economy, leading indicators can be a useful guide in
preparing sales forecasts. The idea is to find a factor
series that is closely related to company sales, yet for
which statistics are available several months in
advance. Changes in the factor can then be used to
predict sales directly, or the factor can be combined
with other variables in a forecasting model.
Some of the more useful leading indicators include
prices of common stocks, new orders for durable
goods, new building permits .
Leading indicators are sensitive to changes in the
business environment and they often signal turns in
the economy months before they actually occur.
When Should Qualitative
Forecasting Methods Be Used?
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Qualitative methods are often used when you have
little numerical data to incorporate into your
forecasts.
New products are a classic example of limited
information, and qualitative methods are frequently
employed to predict sales revenues for these items.
Qualitative methods are also recommended for those
situations where managers or the sales force are
particularly adept at predicting sales revenues.
In addition, qualitative forecasting methods are often
utilized when markets have been disrupted by strikes,
wars, natural disasters, recessions, or inflation.
When Should Qualitative
Forecasting Methods Be Used?
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Under these conditions, historical data
are useless, and judgmental procedures
that account for the factors causing
market shocks are usually more
accurate. Managers should calculate
and record the forecasting errors
produced by the qualitative techniques
they employ so that they will know
when these methods are best
employed.
Naive Forecasts
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Time series forecasts rely on past data to provide a
basis for making projections about the future. The
naive forecast is the simplest numerical forecasting
technique and is often used as a standard for
comparison with other procedures.
This method assumes that nothing is going to change
and that the best estimate for the future is the
current level of sales. For example, actual sales of 49
units observed in quarter 1 in Table SMR2-4 can be
used to predict sales in quarter 2. Naive forecasts for
the last three quarters of year 1 would be
Naive Forecasts
Moving Averages
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With the moving average method, the
average revenue achieved in several
recent periods is used as a prediction of
sales in the next period.
The formula takes the form :
CALCULATING A
MOVING AVERAGE FORECAST
Ft 1
St  St 1  ...  St  n 1

n
where
Ft+1
St
n
= forecast for the next period
= sales in the current period
= number of periods in the moving average
MOVING AVERAGE
FORECASTING EXAMPLE
Quarter
Actual sales
Two-period
moving average
1
2
3
4
49
77
90
79
63
83.5
FORECASTING WITH MOVING AVERAGES
Actual sales
Seasonally adjusted sales
Two-period moving average forecast
seasonally corrected
Three-period moving average
forecast seasonally corrected
Two-period moving average forecast
F3 = ( S1 + S2 ) x I3
2
= ( 67 + 68 ) x 1.16
2
= 78.3
1
2
Time Periods
3
49
67
77
68
90
78
79
81
57
78
98
87
78.3
70.1
58.0
89.8
68.9
55.2
Three-period moving average
forecast
89.3
4
5
6
F4 = ( S1 + S2 + S3 ) x I4
3
= ( 67 + 68 + 78 ) x 0.97
3
= 68.9
Exponential Smoothing
An important feature of exponential
smoothing is its ability to emphasize
recent information and systematically
discount old information. A simple
exponentially smoothed forecast can be
derived using the formula:
CALCULATING AN EXPONENTIAL
SMOOTHING FORECAST
S t  S t 1  (1   ) S t 1
where
S t = smoothed sales forecast for period t and the forecast for period t + 1
α = the smoothing constant
St = actual sales in period t
S t -1 = smoothed forecast for period t – 1
EXPONENTIAL SMOOTHING
FORECASTING EXAMPLE
Quarter
Actual sales
1
2
3
4
49
77
90
79
60.2
72.1
Smoothed forecast
When Should Quantitative
Forecasting Methods Be Used?
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Quantitative forecasting techniques are best
employed when you have access to historical data.
It is also helpful if the time series you are trying to
forecast are stable and do not frequently change
direction.
Quantitative methods have distinct advantages in
situations where you must make frequent forecasts
for hundreds or thousands of products.
Because of the large number of calculations required
by quantitative forecasting procedures, analysts need
access to computers and appropriate forecasting
software..