You will be able to forecast with weighted pipeline, forecast categories and historical run rate.
Forecast the month three separate ways (weighted pipeline, forecast categories and run rate), then compare the three numbers before you call one. Each method works from different information. When they agree, you can be more confident in the number. When they disagree, the gap tells you where to look.
Picture the usual approach. Your manager asks for the team's monthly number by Thursday. You add up the deals your reps say will close, cut the total a bit for rep optimism, and send it. At month end the forecast is out by a third, and you cannot tell whether the pipeline, the reps or your adjustment caused the miss. A forecast built from one method plus a gut adjustment cannot be checked, because there is nothing to compare it against.
The worked example follows Wei Ming, a sales manager, and his team (an invented example; every figure below is invented too). His team uses the four stages from module 4: meeting agreed, problem confirmed, proposal seen and terms agreed.
A weighted pipeline forecast takes each deal's value, multiplies it by the probability that deals at its stage go on to close, and adds up the results. Those stage probabilities have to come from your own history. Don't use the default percentages in your CRM or a figure from a book, because they describe someone else's sales process.
To work out your rates, take the last year of deals from the CRM. For each stage, count how many deals reached it and how many of those were eventually won. Then divide won by reached. Wei Ming pulls the last twelve months. Of the 40 deals that reached problem confirmed, 10 were won, so 10 / 40 = 25 percent. Of the 30 deals that reached proposal seen, 12 were won, which is 40 percent. Of the 15 deals that reached terms agreed, 12 were won, which is 80 percent. He doesn't need a rate for meeting agreed.
Next, multiply each deal, or each stage total, that is expected to close this period by its stage probability, and add the results. Wei Ming's team has S$50,000 at problem confirmed, S$60,000 at proposal seen and S$30,000 at terms agreed, all expected to close this month.
S$50,000 x 25 percent = S$12,500 S$60,000 x 40 percent = S$24,000 S$30,000 x 80 percent = S$24,000
His weighted forecast is S$60,500.
This method 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 entirely on stages meaning the same thing for every rep. If one rep marks a deal proposal seen after a casual chat and another waits for a formal walkthrough, the rates mean nothing. That is the reason module 4 came first.
In category forecasting, each rep places every deal expected this period into one of three groups, using definitions the whole team has agreed:
Commit: the rep is confident it will close this period and would be surprised if it did not. Best case: it could close this period if things go well. Pipeline: it is active but unlikely to close this period.
The agreed definitions are what make this method work. Without them, one rep's commit is another rep's best case. The commit definition should include concrete tests. On Wei Ming's team, a deal can be commit only if it is at terms agreed, or at proposal seen with a named decision maker, an agreed decision date and a next meeting in the diary. Anything that fails those tests can be best case at most.
Once the reps have sorted their deals, you report the commit total, and commit plus best case as the upper figure. Wei Ming's reps mark S$45,000 as commit. That is the S$30,000 at terms agreed plus one S$15,000 deal at proposal seen that met every test. They mark S$20,000 as best case. His category forecast is S$45,000 commit, rising to S$65,000 if the best case deals land.
Categories use what each rep knows about each deal, which the weighted method ignores. The 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 reps' category calls.
A run rate forecast uses only past results. You take what the team closed in recent periods, average it, and use the average as the next period's forecast. Wei Ming's team closed S$52,000, S$61,000, S$48,000, S$70,000, S$58,000 and S$63,000 over the last six months. The average is about S$58,700 a month, so his run rate forecast is about S$58,700.
Run rate ignores the current pipeline entirely, which is both its strength and its weakness. An optimistic rep can't talk it up. But it assumes the future looks like the past. It works best when deal flow is steady and the team, product and market haven't changed much. It works poorly for a new team, after a price change, or in a business that closes a few large deals a year.
Put the three numbers side by side. Wei Ming has a weighted forecast of S$60,500, a category forecast of S$45,000 commit rising to S$65,000 with best case, and a run rate of about S$58,700. The weighted forecast and the run rate are within S$2,000 of each other, and both fall inside the range from commit to best case. Since the three methods are fairly close, he could reasonably call around S$58,000 to S$60,000. He can also explain why: three methods built from different information land in the same place.
When the methods are far apart, the gap is the most useful part of the exercise, and the direction of the gap tells you what to check. If the weighted pipeline is well above the run rate, the pipeline may hold deals at later stages that don't really belong there, or one large deal may be lifting the total. If commit is well above the weighted figure, your reps may be more confident than history supports. If the run rate is well above everything else, the pipeline may be thin, which warns you about next month as much as this one. In each case, go through the particular deals or assumptions the gap points to before you call a number.
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. If you don't, use Wei Ming's example rates and note that they are borrowed.
Take a sample pipeline and calculate a weighted forecast, using stage probabilities from your own history or the sample history.
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