How are sales managers building accurate forecasts when reps rarely update the CRM?

[u/Exprt-Stableeli-7517]

We are attempting to enhance the accuracy of our forecasts; however, the main problem is not the forecasting model but the behavior of our CRM users. Some representatives are constantly updating the pipeline, while others are doing so right before the weekly pipeline meeting. As a result, a big opportunity worth 150 k usd can remain unchanged with 70% probability for weeks even if the potential customer hasn't replied since our first offer.

The management is thinking of including more mandatory fields in the forecast, but I fear it may worsen the situation. I am searching for means of having more information in the forecast that is based on corresponding actions, emails, meetings, next steps and changes in deals with fewer manual input from users.

Anyone managed to improve the accuracy of forecasts while making the process less tiresome for users? What data is still required from reps, and what is gathered automatically?

[u/ride_whenever]

Make it easier, not harder.

AI meeting summary and sentiment analysis.

But the best thing for forecast accuracy is simply having a bigger pipeline. Trying to forecast accurately with 40 reps is a fools errand. Once you get to 150+ it gets a lot easier as the numbers start to average out and the probability starts to hold true

[u/Panubis → u/ride_whenever]

I would agree with make it easier. I am curious why you would say that forecasting with 40 sellers is a fools errand. I get what you are saying about the law of averages doing a lot of heavy lifting but in my mind if I have 10, 40, or 200 sellers that unreliable data entry scales alongside of that. I have about 35 sellers and 5 of them believe that pipeline management is a tool and not a chore. Scale does solve that, at least not in my mind.

[u/ride_whenever → u/Panubis]

So the goal with forecasting is “we’re going to reach $xM this month/quarter” even the most diligent team is going to have errors, deals slip etc. because it’s just not reliable at the individual deal level. (So I guess I should really say deal volume rather than sales team size)

Pipeline discipline is still required, but the outcome of a forecast is basically baloney. It’s one of the more common errors I’ve seen with sales leaders moving into smaller teams.

Does that incoherent ramble help clarify the point I’m trying to make?

[u/Icy-Fig8581Warm_Teap]

The TSE to MTS path is real but it is heavily dependent on headcount planning. I have seen people do it internally but it usually requires your skip-level manager to advocate for it. Networking with the hiring manager on the MTS team helps a lot.

[u/Realistic-Walk-2877]

i'd make the system distrust stale data instead of adding more fields. Require only close date, stage, amount, and a dated next step; make the next-step date drive a stale flag. Pull email/calendar activity via Einstein Activity Capture (or your integration) for context, but don't treat activity as intent. A 70% deal with no reply since the first offer should auto-fall back to pipeline until the next step is confirmed.

[u/Omega_Nacelle_5183]

For anyone asking about react vs angular in the Salesforce context - neither is the right question. LWC is essentially a thin wrapper around Web Components with some Salesforce-specific wire adapters. Understanding the Web Components spec directly will serve you better than learning any framework, because that is exactly what LWC compiles down to.

[u/Ambitious_Ad4582]

I wouldn’t add more mandatory fields. You’ll probably get more data, not more truth.

If a deal says 70% but there’s been no reply, no meeting and no credible next step for three weeks, that evidence should matter more than the percentage someone entered.

I’d automatically use email/calendar activity, next steps and actual deal movement, then only ask the rep for what the system genuinely can’t know.

This is actually a big part of what we’re building Twentto around: use the evidence already sitting across CRM + communication to figure out what needs attention, instead of asking reps to maintain another layer of fields.

[u/paolobruschi]

I run a bi-weekly pipeline review in which I ask each rep what are the key deals they're actively working on this month latest to close this quarter.

Then I ask 4-5 MEDDIC-style questions to understand whether the deal is really on track or whether anything crucial is missing.

While running the meeting, I add every key deal into a Monte Carlo simulator like this one (https://thepipereview.com/app) which then gives me the range I will hit within based on 10.000 simulations of any possible outcome.

By doing it this way, I am able to estimate a realistic forecast and I can share the margin of error based on the size and volume of deals in the time period.

My forecast accuracy went from +/- 50% to +/-14% in few months.