You will be able to produce a forecast using weighted pipeline, forecast categories and historical run rate.
Your own manager asks for the team's number for the month by Thursday. You look at the pipeline, add up the deals the reps say will close, knock off a bit because they are always optimistic, and send it. At the end of the month you are out by a third, and you cannot say whether the pipeline was wrong, the reps were wrong or your knocking-off was wrong.
A forecast built from one method and a gut adjustment cannot be checked. This lesson gives you three methods that work from different information. When they agree, you can be more confident. When they disagree, the gap tells you where to look.
We will use Wei Ming's team and the stages from module 4: meeting agreed, problem confirmed, proposal seen and terms agreed. All figures here are examples.
A weighted pipeline forecast multiplies each deal's value by the probability that deals at its stage go on to close, then adds up the results.
The probabilities must come from your own history. Do not use percentages a CRM suggests by default or a figure from a book. Look at the deals that reached each stage over the past year and count how many were eventually won.
Wei Ming pulled the last twelve months from the CRM. Of 40 deals that reached problem confirmed, 10 were won, so the probability for that stage is 10 divided by 40, which is 25 percent. Of 30 that reached proposal seen, 12 were won: 40 percent. Of 15 that reached terms agreed, 12 were won: 80 percent.
His pipeline of deals expected to close this month had S$50,000 at problem confirmed, S$60,000 at proposal seen and S$30,000 at terms agreed. The weighted forecast is S$50,000 times 25 percent, which is S$12,500, plus S$60,000 times 40 percent, which is S$24,000, plus S$30,000 times 80 percent, which is S$24,000. That comes to S$60,500.
Weighted pipeline is objective and easy to calculate. Its weakness is that it treats every deal at a stage as average. A large deal with a missing decision maker gets the same 40 percent as a small deal with a signed purchase order on the way. It also depends completely on the stages meaning the same thing for every rep, which is why module 4 came first.
Category forecasting asks each rep to place every deal expected this period into one of three groups, using definitions the team has agreed. Commit means the rep is confident it will close this period and would be surprised if it did not. Best case means it could close this period if things go well. Pipeline means it is active but unlikely to close this period.
The definitions are what make this work. Without them, one rep's commit is another's best case. Wei Ming's team agreed that a commit deal must be at terms agreed, or at proposal seen with a named decision maker, an agreed decision date and a next meeting in the diary. Anything else can be best case at most.
Applied to this month, the reps marked S$45,000 as commit: the S$30,000 at terms agreed and one S$15,000 deal at proposal seen that met every test. Another S$20,000 went in best case. So the category forecast was S$45,000 commit, rising to S$65,000 if the best case deals land.
Categories use what the rep knows about each deal, which the weighted method ignores. Their weakness is human: reps under pressure push deals into commit to look good, or hold them back to look safe. Lesson 5.3, Spotting deals that will slip, covers how to test their calls.
A run rate forecast looks only at past results. You take what the team closed in recent periods and project the same for the next one.
Wei Ming's team closed S$52,000, S$61,000, S$48,000, S$70,000, S$58,000 and S$63,000 in the last six months. The average is about S$58,700 a month, so the run rate forecast is about S$58,700.
Run rate ignores the current pipeline entirely, which is both its strength and its weakness. It cannot be talked up by an optimistic rep. But it assumes the future looks like the past, so it works best when deal flow is steady and the team, the product and the market have not changed much. It works poorly for a new team, after a price change, or in a business with a few large deals a year.
Put the three numbers side by side: weighted pipeline S$60,500, commit S$45,000 rising to S$65,000 with best case, run rate about S$58,700.
These are fairly close, which is reassuring. The weighted figure and the run rate are within S$2,000 of each other, and both fall inside the range between commit and best case. Wei Ming could reasonably call around S$58,000 to S$60,000 and say why.
When the methods are far apart, the gap is the most useful part of the exercise. If weighted pipeline is well above run rate, the pipeline may be full of deals at later stages that do not really belong there, or one large deal may be lifting the total. If commit is well above weighted, the reps may be more confident than the history supports. If run rate is well above everything, the pipeline may be thin, which is a warning about next month as much as this one. Each gap points to particular deals or assumptions you need to check.
In the activity below you will calculate a weighted pipeline forecast for a sample pipeline. Use stage probabilities from your own team's history if you have them, and if not, use Wei Ming's example rates and note that they are borrowed.
Calculate a weighted pipeline forecast for a sample pipeline using stage probabilities from your own or sample history.
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