Learn which B2B sales forecasting methods are the most effective.

1. A test of B2B sales forecasting methods
A Test of B2B Sales Forecasting Methods
July 2012
©2012 Nimble Apps Limited
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2. A test of B2B sales forecasting methods
Table of contents
1 Introduction .................................................................................................................................... 1
2 Scope and methodology ................................................................................................. 2
2.1. Scope ........................................................................................................................................................... 2
2.2. Methodology ............................................................................................................................................. 2
3 Analysis of sample ................................................................................................................. 2
3.1. Closing dates are always optimistic .............................................................................................. 2
3.2. Losing takes longer than winning ................................................................................................... 3
4 Forecasting methods ........................................................................................................... 3
4.1. Description ............................................................................................................................................... 3
4.2. Results ........................................................................................................................................................ 4
5 Conclusion ........................................................................................................................................ 6
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3. A test of B2B sales forecasting methods
1 Introduction
Sales forecasting is a major issue for B2B companies. On one hand, B2B companies often lack the
thousands of data points that statistical forecasting techniques require. But on the other hand, recent
research by Aberdeen Group shows a clear link between forecasting best practices and sales
performance.
The implication is obvious: robust, B2B-specific forecasting methods would change the life of sales
managers.
This white paper describes the test of common and not-so-common B2B forecasting techniques we
recently performed on a sample of SalesClic client data. Our research yields a number of confirmations and
a few surprises.
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4. A test of B2B sales forecasting methods
2 Scope and methodology
2.1. Scope
Our sample data - representative of B2B compa- Measure of forecasting error
nies selling complex products and services -
was made up as follows: We measured forecasting errors over the test pe-
riods using their root mean square, normalized by
• 12 sales teams
the average amount of opportunities in the sample.
• The teams were located in the US, UK and Asia
• he teams operated in the software, electronic
T
equipment and financial services industries 3 Analysis of sample
• The teams managed “structured” sales pipelines
Before discussing the accuracy of the forecasting
(i.e. following stage-by-stage sales processes) techniques included in the test, it is worth noting
2 interesting patterns in the sample data.
• ver the research period, we totaled 144,817
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closed opportunities
3.1. Closing dates are always optimistic
• The sales cycles were from 75 to 250 days
Initial closing dates are optimistic for 10 teams out
of 12 in our sample. On average, it takes 22%
2.2. Methodology
longer than initially expected to win an oppor-
tunity for the sample teams.
Training and test periods
That is worrying in a B2B context, where sales
We divided the historical data of these 12 teams
forecasts are very sensitive to closing dates. For
into training and test periods for the selected
sales managers and sales operations managers,
algorithms. The training period is always twice
monitoring closing dates is a clear priority.
as long as the test period, with a minimum of 1.5
years, an average of 4.5 years and a maximum of
9 years. Training periods contained at least 500
sales opportunities.
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5. A test of B2B sales forecasting methods
4 Forecasting methods
120% Closing date error
4.1. Description
100%
The following describes the 8 forecasting
80%
techniques we tested.
60%
• eighted pipeline #1 is a simple weighted pipe-
W
40%
line using declared opportunity amounts, closing
Sales cycle length
20%
(days) dates and closing probabilities
0%
50 100 150 200 250 300
• Weighted pipeline #2 uses declared opportunity
amounts and closing dates but historical closing
-20%
Figure 1 - Closing date error x Sales cycle length probabilities
• Weighted pipeline #3 uses declared opportunity
The bias increases with the length of the sales amounts, historical opportunity time-to-wins1 and
cycle, as illustrated by figure 1 above. In our historical closing probabilities
sample, we find a closing date error of 16% for the • eighted pipeline #4 is a variation of weighted
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team with the shortest sales cycle, and of 109% pipeline #3, using declared opportunity amounts,
for the team with the longest sales cycle. historical stage durations2,3 and historical closing
probabilities
3.2. Losing takes longer than winning
• We also tested most combinations of the 4
In our sample, two-thirds of closed-lost oppor- weighted pipelines methods mixing declared
tunities are lost after the closing date initially and historical inputs
expected, and losing an opportunity takes an • he “linear predictors” assume a linear rela-
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average 1.7 times longer than winning one. tionship between stage amounts on day d and
closed-won amount on day d+n
Stagnation in the pipeline does not bode well for
• he “decision tree predictors” are sophisticated
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pending opportunities, and B2B companies have
algorithms using the decision tree technique. Our
much to gain from detecting “stuck” opportu-
trees are grown and pruned on stage amounts
nities as early as possible.
• he “daily closing rate” method assumes that,
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until the end of the forecast period, a team will
close the same daily amount as during the last n
days (see example p. 5)
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1. The “time to win” of an opportunity is the average time required to win opportunities that have reached the corresponding
pipeline stage.
2. The “duration” of a pipeline stage is the average time that opportunities spend in that stage.
3.
Time to win for pipeline stage n and the sum of stage durations for stages n to i usually differ
because of early losses, stage jumps and back-and-forth opportunity movements.

6. A test of B2B sales forecasting methods
We classified these techniques according to 2 criteria that are very relevant to CRM software users:
• hether they use declared or calculated closing dates and closing probabilities
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• How computer intensive they are
Closing date Closing probability Computer intensive
Weighted pipeline #1 Declared Declared Low
Weighted pipeline #2 Declared Calculated Medium
Weighted pipeline #3 Calculated Calculated Medium
Weighted pipeline #4 Calculated Calculated Medium
WP combinations Both Both Medium
Linear predictors Both None High
Decision tree predictors None None High
Daily closing rate None None Low
Figure 2 - Classification of forecasting methods
4.2. Results
Judgmental forecasts are not reliable This means that sales teams are sitting on a
huge amount of forecasting information they
The simple weighted pipeline forecasting tech- could be using to inform their judgments.
nique (declared amounts, declared closing dates
and declared closing probabilities) is the second Our research shows that replacing sales rep
worst performing in the sample. and manager judgment on closing dates and
closing probabilities with historical averages
This research thus confirms what most sales ma- increases forecast accuracy.
nagers already know: simple weighted pipelines
• Weighted pipeline #3 is 7% more accurate than
cannot be trusted. weighted pipeline #1
• eighted pipeline #4 is 35% more accurate than
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Leveraging historical data helps
weighted pipeline #1
Traditional CRM software is unable to leverage
properly the historical data of sales teams for
optimization purposes.
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7. A test of B2B sales forecasting methods
Sophistication pays... up to a point And the winner is...
Averaging time-to-wins, durations and closing As shown by figure 3 (below left), the best fore-
probabilities is a straightforward way to leverage casting technique on our sample data is a simple
historical data. How do the more sophisticated but nimble one: the “daily closing rate”. Here is an
techniques tested here perform? example of how it works:
• inear predictors don’t work very well.
L • uppose your team has closed €300K
S
In particular, they are outperformed by weighted over the past rolling 3 months
pipeline #4. This is disappointing but not
• That is a daily average of €3.3K
surprising since sales pipelines can have widely
• Suppose that you are 30 days
different shapes, and linear equations are ill
into the current quarter (1/3 of the quarter)...
equipped to deal with such irregularities.
• ...and that you have closed €50K so far
• ecision trees perform well. Compared to the
D
simple weighted pipeline, they increase forecast • Your “historical daily closings” forecast for the
accuracy by an average 46%. However, they are quarter is: €50K + €3.3K x 60 days = €250K
quite hard to implement.
This method improves simple weighted pipeline
Accuracy improvement forecasts by 53%. Forecast accuracy also in-
creases by 20% compared to weighted pipeline #4.
Weighted pipeline #1 Reference point
Daily closing rate 53%
Decision tree
46%
predictors4
Weighted pipeline #4 42%
WP combinations 20%
Linear predictors5 11%
Weighted pipeline #3 7%
Weighted pipeline #2 -8%
Figure 3 - Performance of forecasting methods
vs. simple weighted pipeline
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4. This is the average of 3 decision tree predictors – all 3 tightly grouped around this average.
5. This is the average of 6 linear predictors. The best one improves forecast accuracy by 21% over weighted pipeline #1.

8. A test of B2B sales forecasting methods
5 Conclusion
This research suggests immediate ways for B2B companies to improve sales forecast accuracy.
• easure “pipeline dynamics” (opportunity time-to-wins, pipeline stage durations,
M
closing probabilities by pipeline stage) and use that information for forecasting purposes.
• Calculate your “daily closing rate” and use that information for forecasting purposes.
Implementing forecasts based on decision trees is also a good idea, although potentially complicated.
For additional progress, we believe that moving from the analysis of a pipeline’s “macro structure” (pipeline
stages essentially) to a pipeline’s “micro structure” (the behavior of individual opportunities) is required.
Nimble Apps will continue to study and share insights on these topics.
About Nimble Apps Limited
Nimble Apps is the publisher of SalesClic, a simple and powerful solution for visualizing,
analyzing and forecasting your sales pipeline.
SalesClic integrates with Google Apps, Highrise and Salesforce.
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