Quantify the Cost of
Inaction.
No pitch. Run the model first. Two lenses on the same problem: what your forecast variance costs you each quarter, and the revenue you recover when it's 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. MxM's Controls Install typically reduces variance by 40-60%, modeled here at 50%.
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.
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. MxM engagement data, Q1 2026. PE RevOps research, Q1 2026.