Rep Forecast Submission Template: What to Ask Every Seller Every Week

What should a forecast submission actually collect?

Only information a system cannot generate on its own. CRM already knows deal amounts, stages, and close dates. Asking a rep to retype those wastes the one thing the submission has going for it, which is the rep's judgment about deals the data cannot yet see.

That narrows the form to four things: the number the rep commits to, the deals behind it, what changed since last week, and the specific event that would break the number. Everything else is decoration.

What fields belong on the form?

Eleven, split between a summary block and a per-deal block. The last field earns its place because a moved close date is the strongest slippage signal ORM sees in a deal record. A deal that slips into the next period is less likely to close even when it stays in commit, so a rep who moved a date and left the deal in commit has some explaining to do.

Which questions force a real answer instead of a number?

Questions about the buyer, phrased so a vague reply is obviously vague. Three work better than any confidence slider:

- What has the buyer done in the last seven days that they had not done before? - What is the next thing they will do, and on what date? - If this deal misses, what will the reason turn out to be?

The third question is the useful one. Reps who cannot name a failure mode usually have not tested the deal. Reps who name procurement timing or a signature authority they have never met are telling you exactly where to spend the coaching hour.

The earliest warning sign shows up in the answers to the first question. ORM counts meaningful activity as a change in stage, close date, or amount, and the earliest signal of a dying deal is the absence of any signal at all. No returned email, no scheduled call, no data changing. A submission full of "still working it" across six deals is a forecast problem, not a communication problem.

How do you score submissions after the period closes?

Compare each rep's submitted commit at week one against their actual result, then hold that comparison across several periods to separate noise from bias. One period tells you nothing. Four periods tell you whether a rep has a direction. A rep who lands above commit every period is not lucky, they are managing their own risk by understating. That is correctable once it is visible, and it is invisible until someone keeps the history.

Week-one accuracy is the number worth improving. Getting the forecast right in the final week of a period does not help anybody, because by then the period has already happened. The value sits in knowing the shape of the period on day one, early enough to change it.

How does the submission connect to the rolled-up number?

The rep submission is one input to the forecast, not the forecast itself. Managers who simply add up rep commits inherit every rep's bias without correcting for any of it. The roll-up should adjust each rep's number using their measured history, which is what the scoring table above is for.

This is also where a model earns its keep. Manual sales forecasting processes can reach roughly 90% accuracy on new and expansion business, but they consume significant analyst time and they do not respond when conditions change. ORM targets 95% without manual adjustment, and holds it from day one through day ninety of the quarter, updating as the period progresses. A model trained on your own historical performance takes four to six weeks to stand up.

Keep the rep submission anyway. The model handles the aggregate pattern. The rep submission tells you what the model cannot know yet, such as the buyer who went silent on Tuesday. Read the two together, then use the gap between them as your coaching list. That combination does more for forecast accuracy than any single-source process, and it fits the wider set of forecasting practices that hold up under pressure.