Deal-Close Forecast: payment-verified ML forecast of which deals actually close
Know which deals will actually pay, measured against money received, not CRM optimism.
What was getting in the way.
CRM close dates are aspirational: deals marked "closing this week" for a month, and won dates edited after the fact. Leadership couldn't trust the pipeline view for cash planning, and the handoff from sales to onboarding depended on someone noticing a deal was about to land.
Context
The marketing team’s own sales pipeline (prospective local-business clients).
How the work runs.
- 01
Trigger
scheduled runs (forecast reads on demand; watcher on a 2-hour cycle; monthly retrain).
- 02
Ground truth is the client's
Ground truth is the client's first successful payment, not the CRM close date. Payment history syncs nightly and joins to deals.
- 03
trained model scores every open
A trained model scores every open deal: probability of first payment within 14 and 30 days, plus a predicted close week.
- 04
Output includes a "lie factor"
Output includes a "lie factor": how CRM close dates historically compare to actual payment dates, and zombie flags for deals long past their stated close.
- 05
watcher escalates high-probability deals to
The watcher escalates high-probability deals to leadership for onboarding prep, with cooldowns and dedupe so it never nags.
- 06
Outcome
a calibrated, payment-verified pipeline view. Honest status note: the escalation watcher is currently PAUSED at leadership's request (2026-09-18); the forecast itself remains available on demand.
Evidence from the workflow.
Each system has a role.
Intake
CRM (deals, stage history)
Record of truth (ground truth)
Payments platform
Action (scoring)
ML model (local)
Action (escalations: paused)
Team chat
Why this is Operator.
Owns a workflow end to end, running the process inside the authority you set.
- 01
Watcher escalates internally only, with per-deal dedupe and cooldowns; currently paused per leadership instruction (paused ≠ deleted; resumes on request). Internal test deals are filtered from any client-facing readout. Retrain is calibrated against holdout data before replacing the model.
Impact / Outcomes
Model trained on 1,468 pipeline deals (121 won / 1,325 lost), with 84% of won deals matched to a verified first payment.
Calibration holds where it matters: deals scored ≥50% closed at a 75% actual rate on holdout (14-day head, AUC 0.696).
Quantified the CRM honesty gap: 17.4% of won deals actually paid later than their recorded close date; leadership now sees both numbers side by side.
Find where a workflow like this fits.
Start with the systems, work, constraints, and authority already present in your operation.