Services
Sales
Forecast Accuracy Audit
Analyze your forecasting process and data to identify why projections miss and how to make them reliable.
Intelligence
Forecast misses tend to produce the same conversation: the number was wrong, someone explains why, leadership says it can't happen again, and then it happens again. The reason it keeps happening is that forecast accuracy is treated as a discipline problem when it's usually a data problem. Either the stage definitions don't reflect real buyer progress, or the model doesn't account for rep-level accuracy patterns, or both. The audit tells you which it is and builds the forecasting model that actually holds up.
Forecast accuracy problems have two sources, and they require different fixes. The first is data quality: if deal stages don't reflect real buyer progress, if close dates are aspirational rather than grounded, and if pipeline gets inflated by deals that should have been qualified out, no forecasting methodology will save you. The second is methodology: even clean data produces bad forecasts when the model weights are wrong, when it doesn't account for rep-level patterns, or when it uses average close rates applied uniformly across a non-uniform pipeline.
Most companies try to fix forecast accuracy through management pressure, asking reps to be more careful and realistic. That addresses neither source.
What we do
We start with the data. We audit deal stage accuracy, close date movement patterns, and rep-level forecast submission behavior to establish how much of the forecast error is coming from input quality versus methodology. Then we build a forecast model calibrated to your actual historical data, with rep-level accuracy weights, stage probability adjustments, and deal health signals that flag at-risk pipeline before it shows up as a miss.
For context on what this type of analysis typically surfaces, read what accurate forecasting looks like when built on real CRM data.
Deliverable
A forecast accuracy audit report covering data quality findings, methodology assessment, rep-level accuracy analysis, and a rebuilt forecast model with documentation. Includes a monitoring framework for measuring forecast accuracy going forward so improvement is visible.
Outcome
Forecasts that are closer to right more often. Leadership that trusts the number going into the board meeting. Earlier visibility into quarters that are going to miss, which is when intervention is still possible.
How Stratum Group Cut Reporting Time by 50% and Detected Churn 60 Days Earlier — cut decision time by 50% and started detecting churn 60 days earlier.
See how it worked in practice: Apex Advisory increased proposal win rate by 31%.
Best Fit
If your forecast is off by more than 15% in either direction on a regular basis, or if you've had quarters where leadership was surprised by the miss, this is the engagement to run before the next planning cycle. Also appropriate before implementing or changing CRM forecasting tools, since the problem is almost never the tool.