You will be able to run a mock pipeline review and submit a forecast with a written explanation.
Open a spreadsheet and list at least 15 deals your team expects to close next month, with a value and a stage for each. Use your team's real pipeline if you can. If you are not yet a manager, or the data is sensitive, build a sample of at least 15 deals spread across the stages instead.
You need this list for a request that comes up often. On the last Monday of the month, your manager messages you and asks for your number and the main risks by Wednesday. Earlier lessons covered the parts separately: a review focused on what the buyer did, three forecasting methods, and the warning signs of a slipping deal. This exercise joins them into one job, and you will do that job every week or every month.
Besides the deals, pull two kinds of history. You need past win rates by stage for the weighted method, and past results by period for the run rate.
Wei Ming is a sales manager forecasting next month. He has 15 deals expected to close next month, across four stages (every figure in his forecast is an example figure):
Meeting agreed: 4 deals of S$8,000, S$10,000, S$6,000 and S$6,000, totalling S$30,000 Problem confirmed: 5 deals of S$12,000, S$10,000, S$14,000, S$9,000 and S$15,000, totalling S$60,000 Proposal seen: 4 deals of S$10,000, S$30,000, S$8,000 and S$12,000, totalling S$60,000 Terms agreed: 2 deals of S$14,000 and S$16,000, totalling S$30,000
His stage probabilities come from the past year's CRM history. Meeting agreed is 10 percent, because 6 of the 60 deals that reached that stage were won. Problem confirmed is 25 percent, proposal seen is 40 percent and terms agreed is 80 percent.
Run the review deal by deal, using the agenda you wrote in lesson 5.1. For each deal, check the stage against its exit criteria and ask what the buyer has done since last week. Then agree a next action with an owner and a date, and confirm the forecast category. Apply the warning signs from lesson 5.3, "Spotting deals that will slip", to every deal the rep puts in commit.
If you are using sample data, play both roles, manager and rep. For each deal, write a short line on what the buyer did and what happens next, as if a rep had said it to you.
Record the review in a simple table with these columns: deal, stage, buyer's last action, next action, owner, date and category.
Commit means deals that pass every test today. Before his review, Wei Ming's commit was S$52,000: both terms agreed deals (S$14,000 + S$16,000) and two proposal seen deals (S$12,000 + S$10,000).
The review changed two things. The S$16,000 terms agreed deal, which Daniel owns, is the deal from lesson 5.3 whose close date has moved twice. A deal whose close date has moved twice drops from commit to best case, so Wei Ming moved it there. The S$30,000 proposal seen deal, the largest in the pipeline, belongs to Raj. Raj wanted it in commit, but it has no agreed decision date, and a deal with no agreed decision date stays in best case even when the rep wants it in commit.
Commit after the review is S$36,000 (S$52,000 minus the S$16,000 deal), across three deals. Best case is commit plus the deals that could close but still have unresolved doubts, such as no decision date or a close date that has moved. For Wei Ming that is S$82,000: the S$36,000 commit, plus the S$16,000 deal, plus the S$30,000 deal.
Use at least two of the three methods from lesson 5.2, "Three ways to forecast, and why to use more than one". You can use all three if you have the data, as Wei Ming does, and you should show the arithmetic for each method you use.
For a weighted pipeline, multiply each stage's total value by that stage's historical win rate, then add the results. Wei Ming gets S$3,000 (S$30,000 x 10 percent), S$15,000 (S$60,000 x 25 percent), S$24,000 (S$60,000 x 40 percent) and S$24,000 (S$30,000 x 80 percent), which add up to a weighted total of S$66,000.
The categories method gives him the two figures from the review: commit of S$36,000 and best case of S$82,000.
The run rate is the average of past results by period. Wei Ming takes the last six months, including the one just closing, and averages S$61,000, S$48,000, S$70,000, S$58,000, S$63,000 and S$60,000 to get a run rate 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. Then explain the gaps by finding the deals that drive each figure.
Run rate and weighted pipeline both assume that some best case and earlier-stage deals will close, as they usually do, so the forecasting question is how many of them will. Wei Ming's weighted figure is S$6,000 above his run rate. The S$30,000 proposal seen deal alone contributes S$12,000 of the weighted figure, at 40 percent. A large single deal can inflate the weighted figure like this, and if that deal slips, the weighted figure overstates the month.
Wei Ming calls S$60,000. To reach it, he needs S$24,000 from best case and earlier deals on top of the S$36,000 commit. Write a few sentences like these for your own numbers, saying which deals explain each gap and why you chose your call.
The forecast note is one paragraph. It gives the number, the main risks and the upside, with what is being done about each. Wei Ming's note to his manager reads something like this:
"My forecast for next month is S$60,000. Commit is S$36,000, across three deals at terms agreed or late proposal. The first risk is the S$30,000 deal, which has no decision date yet. Raj is meeting the finance director next week to agree one. The second risk is the S$16,000 deal whose close date has moved twice, and Daniel is checking it with the operations director. If both land, the upside is about S$82,000. The weighted pipeline is S$66,000 and the six-month average is S$60,000, so I am comfortable with the call."
The review table, the forecasts, the gap explanation and the note together make up 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 forecasts with two methods, and write a one-paragraph forecast note.
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