Seeking Feedback on Advanced Annual Planning Prototype Using Timing and Probability Models
Hey RevGenius community 👋 I’m at a real build-or-pivot inflection point on a side project and could use some candid feedback from folks handling annual planning and forecasting. The Problem: Most annual planning builds forecasts using stage-to-stage (S2S) conversion (the left side of the bowtie) or sales velocity models. But these have two massive blind spots:
- 1.
Timing: A deal created in Q1 has up to 12 months to close; a deal created in Q4 has at most 90 days. Both models treat them as identical.
- 2.
Outcome Variance: Both approaches produce deterministic point estimates that mask the distribution of possible outcomes, forcing us to rely on hand-picked high/mid/low scenarios.
The Experiment: I built an un-gated web prototype that automates continuous timing analysis (using survival curves) and distributions (via Monte Carlo simulations). It lets you test speculative changes in key inputs and see the dynamic effect on your odds of meeting a revenue goal. Sample output below. The Question for You: The prototype math works great—currently on synthetic data. But before I sink time into building native CRM data ingestion, I need an honest reality check: Do RevOps leaders actually care about this level of timing and probability rigor, or are traditional bowtie and sales velocity models "good enough" in practice—despite their limitations? If you have a few minutes to poke around and see it in action, the prototype is un-gated here: funnelcast.com/bookings-planner Really appreciate any candid feedback!
