Can RevOps Fix Forecasting?
More than half of forecast deals never close. CSO Insights found the average win rate of forecast deals was just 45.8%. That means for every ten deals in your commit forecast, five of them are going to fall out. And yet most organizations respond to this by layering on more adjustments — rep-level haircuts, manager overrides, weighted averages — instead of asking why the pipeline data was wrong in the first place.
The problem is not the math. The problem is what goes into the math.
RevOps sits at the center of the forecasting problem
Revenue operations teams are responsible for the systems and processes that feed every forecast. They configure Salesforce. They build the reports. They design the stage definitions, field requirements, and approval workflows that determine what data gets captured and when.
This puts RevOps in a unique position: they see every gap, every workaround, every field that reps leave blank. They know where the data breaks down, and they know what leadership is trying to build on top of it.
The questions RevOps is always asking:
- Does this process work?
- If not, why?
- How do we fix it?
- How do we prevent it from breaking again?
These are the right questions. But the tools available to answer them — spreadsheets, standard Salesforce reports, bolt-on point solutions — often can’t keep up with the complexity of the pipeline data they’re trying to manage.
Garbage in, garbage out
This phrase gets repeated so often in sales operations that it has lost its punch. But the underlying truth hasn’t changed: if your opportunity data is incomplete, outdated, or one-dimensional, no forecasting model built on top of it will produce accurate results.
Most forecasting tools rely on whatever is already in your CRM. They apply algorithms to your existing data. They calculate weighted pipeline, trending averages, AI-generated predictions. Some of it is useful. But none of it compensates for the foundational problem: the deal data itself is often wrong.
Close dates default to the last day of the quarter. Amounts get set during qualification and never updated. Stages advance based on sales activity rather than buyer behavior. Next steps say “follow up” for six weeks straight.
RevOps teams know this. They have been fighting this battle for years — adding validation rules, required fields, automation, dashboards that highlight stale deals. But enforcement alone doesn’t fix the incentive structure. Reps will always take the path of least friction unless the system makes accurate data entry the easier path, not just the required one.
The handicapping trap
When the data going in is unreliable, organizations develop workarounds. Sales managers start mentally adjusting every forecast based on who submitted it:
- Rep A is always optimistic, so discount her commit by 20%.
- Rep B is a sandbagger who consistently undercommits, so bump his number up.
- Rep C doesn’t have enough deal detail, so assume the worst.
This handicapping is everywhere. It’s treated as the “art” of forecasting — the experienced judgment that a good sales leader brings. And there’s real skill in it. But it’s also a symptom. When you have to mentally adjust every number because you don’t trust the underlying data, your forecasting process is compensating for a pipeline management problem.
The question is whether you’re actually getting more accurate, or just getting better at predicting that you’ll be wrong.
What actually moves the needle
Improving forecast accuracy doesn’t start with better algorithms. It starts with better pipeline data — which means better processes for how deals are tracked, reviewed, and validated inside Salesforce.
Structured pipeline reviews. A weekly cadence where managers and reps walk through deals together — not in a spreadsheet, but in a visual format that surfaces risk patterns like stalled deals, close-date stacking, and concentration risk. When reviews are consistent and focused on deal quality rather than just totals, pipeline hygiene improves because reps are held accountable for the details.
Stage definitions that mean something. If your stages map to sales activities (“demo completed,” “proposal sent”) instead of buyer milestones (“stakeholders identified,” “business case approved”), your pipeline view is a record of what reps did, not where deals actually stand. RevOps owns these definitions, and getting them right changes everything downstream.
Visibility into deal movement. Static snapshots of pipeline — even weekly ones — miss the story. What moved forward? What slipped? What changed since last Tuesday? The ability to see pipeline movement over time is what turns a forecast review from a guessing game into an analysis.
Forecasting inside Salesforce, not outside it. Every time pipeline data gets exported to a spreadsheet for the forecast call, it becomes a snapshot frozen in time. The deals keep moving, but the spreadsheet doesn’t. Building the forecast directly in Salesforce — where the data lives and updates in real time — eliminates the gap between what’s in the CRM and what’s on the forecast slide.
RevOps can fix forecasting — with the right foundation
RevOps can’t fix forecasting by adding another layer of adjustment on top of unreliable data. But they can fix it by changing the foundation: improving the systems, processes, and visibility that determine what goes into the pipeline in the first place.
That means moving beyond enforcement (required fields, validation rules) toward enablement — giving reps and managers tools that make accurate pipeline management easier than the alternatives. Visual pipeline reviews. In-Salesforce forecasting. Deal movement tracking. Stage progression analysis.
When the data going in is accurate, the forecast coming out takes care of itself.
Akoonu Forecasting and Pipeline Reviews are built 100% native in Salesforce — giving RevOps teams the visibility and process structure to improve pipeline data at the source. See how it works.
