Quantify Your
Revenue Distortion Exposure.
No pitch. Run the model first. Two lenses on the same problem: what your forecast variance exposes each quarter, and the revenue you recover when it is fixed.
Benchmarks from 20+ years of enterprise RevOps across Microsoft, HP/HPE, and Philips. Used in $3B+ revenue environments.
See what forecast error is costing you now.
Set your ARR and forecast error rate. The model estimates revenue at risk each quarter, what you could recover each year, and how fast the install pays back.
Forecast error rate measures how far actual results land from the forecast. Higher variance means larger planning gaps. The model assumes the Controls Install cuts the error rate by 50%. Illustrative assumption, not an observed client outcome.
The Scorecard traces this exposure to the specific control gaps producing it, against your actual CRM and forecast data.
See what cleaner pipeline controls could add.
Enter your current pipeline numbers. The model applies MxM's improvement assumptions to show the added revenue, the drivers behind it, and how quickly the full engagement could pay back.
These gains assume the controls behind your pipeline data hold. The Scorecard verifies whether they do.
If your numbers surfaced a gap worth closing, the Scorecard is where we start.
All outputs are illustrative planning models. Actual results depend on pipeline composition, sales process maturity, and CRM data quality. Benchmarks drawn from founder in-house operating track record and PE RevOps research.