You will run a mock pipeline review and submit a forecast with a written explanation.
Your own manager's message arrives on the last Monday of the month: "Need your number and the main risks by Wednesday." Until now you have learned the parts separately: a review that focuses on what the buyer did, three forecasting methods, and the warning signs of a slipping deal. This exercise puts them together into the job you will actually do every week or every month.
You will run a mock pipeline review on at least 15 deals, forecast with two methods, explain the gap between them, and write a short forecast note. We will follow Wei Ming through his forecast for next month, and every figure in it is an example.
Use your team's real pipeline if you can. If you cannot, because you are not yet a manager or the data is sensitive, build a sample of at least 15 deals across your stages, with a value and a stage for each.
Wei Ming's 15 deals were all expected to close next month. By stage:
Meeting agreed: four deals worth S$8,000, S$10,000, S$6,000 and S$6,000, a total of S$30,000. Problem confirmed: five deals worth S$12,000, S$10,000, S$14,000, S$9,000 and S$15,000, a total of S$60,000. Proposal seen: four deals worth S$10,000, S$30,000, S$8,000 and S$12,000, a total of S$60,000. Terms agreed: two deals worth S$14,000 and S$16,000, a total of S$30,000.
Also pull the history you need: past win rates by stage for the weighted method, and past results by period for the run rate.
Take the agenda you wrote in lesson 5.1 and work through it deal by deal. For each one, check the stage against its exit criteria, ask what the buyer has done since last week, agree a next action with an owner and a date, and confirm the forecast category. Use the warning signs from lesson 5.3, Spotting deals that will slip, on every deal the rep puts in commit.
If you are doing this with sample data, play both parts. Write a short line for what the buyer did and what happens next, as if a rep had told you.
Record everything in a simple table: deal, stage, buyer's last action, next action, owner, date, category.
Wei Ming's review changed two things. The S$16,000 deal at terms agreed was the one from lesson 5.3 whose close date had moved twice, so it dropped from commit to best case. And the S$30,000 proposal seen deal, the largest in the pipeline, had no decision date agreed, so it stayed in best case even though Raj wanted it in commit.
Now produce two forecasts. Pick two of the three methods from lesson 5.2, Three ways to forecast, and why to use more than one. Wei Ming used all three, which you can do too if you have the data.
Weighted pipeline. His stage probabilities, from the past year's CRM history, were 10 percent for meeting agreed, because 6 of the 60 deals that reached it were won, then 25 percent for problem confirmed, 40 percent for proposal seen and 80 percent for terms agreed. The weighted figures by stage were S$3,000, S$15,000, S$24,000 and S$24,000, for a total of S$66,000.
Categories. Before the review, the reps had put S$52,000 in commit: both terms agreed deals and two proposal seen deals of S$12,000 and S$10,000. After the review moved the S$16,000 deal out, commit stood at S$36,000. Best case, adding the S$16,000 deal and the S$30,000 deal, came to S$82,000.
Run rate. In the last six months, including the one just closing, the team closed S$61,000, S$48,000, S$70,000, S$58,000, S$63,000 and S$60,000, an average of S$60,000.
Put the numbers side by side: commit S$36,000, run rate S$60,000, weighted S$66,000, best case S$82,000.
Weighted pipeline is S$6,000 above run rate. Look at where the weighted figure comes from. The single S$30,000 proposal seen deal contributes S$12,000 of it, at 40 percent. That deal has no agreed decision date, which is why the rep's own view puts it in best case. If it slips, the weighted figure overstates the month.
The gap between commit and the other methods is larger, and it is also explainable. Commit includes only deals that pass every test today. Run rate and weighted pipeline both assume that some best case and earlier-stage deals will close, as they usually do. The question for the forecast is how many.
Wei Ming called S$60,000. It matches the run rate, sits below the weighted figure because of the doubt over the S$30,000 deal, and needs S$24,000 to come from best case and earlier deals on top of the commit.
The note to your manager is one paragraph. It gives the number, the main risks and the upside, and says what you are doing about each.
Wei Ming's note read: "Next month's forecast is S$60,000. Commit is S$36,000 across three deals at terms agreed or late proposal. Main risks are a S$30,000 deal with no decision date yet, where Raj is meeting the finance director next week to agree one, and a S$16,000 deal whose date has moved twice, which Daniel is checking with the operations director. If both land, we reach about S$82,000. Weighted pipeline gives S$66,000 and our six-month average is S$60,000, so I am comfortable with the call."
You should end with the review table for at least 15 deals with next actions and dates, forecasts from two or more methods with the arithmetic shown, a few sentences explaining the gap, and the forecast note. Together, those are the module 5 capstone.
In the activity below you will run the full exercise on your own or sample data. Check every figure in a spreadsheet before you write the note, because the note is the part your manager will quote back to you.
Run a mock pipeline review on 15 deals, produce two forecasts and write a one-paragraph forecast note.
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