Free Strategic Revenue Planning & Forecasting Cheat Sheet

What's the difference between a bottom-up capacity model and a top-down revenue target, and why does this cheat sheet treat them as separate inputs?

A top-down target is what the CEO and CFO want — a number driven by investor expectations or growth ambition. A bottom-up capacity model is what your actual headcount, ramp curves, and pipeline coverage can realistically produce. The cheat sheet treats them separately because the gap between the two is where most revenue plans break down, and closing that gap requires a structured reconciliation session with CRO, CMO, and FP&A before targets get locked.

What data do I need to have clean before this cheat sheet is actually useful to me?

At minimum, you need accurate bookings by rep and segment from your CRM, documented ramp curves (modeled vs. actual), pipeline coverage data by quarter, and retention metrics like GRR and NRR. If your stage-to-stage conversion rates or win rates are unreliable, your capacity model will produce garbage outputs regardless of how well you follow the workflow. The cheat sheet flags this explicitly — one source of truth across CRM, BI, and Finance is a prerequisite, not a nice-to-have.

Which of the three forecasting methods — weighted, bottom-up, or AI — should I start with if we don't have a formal forecast process yet?

Start with a weighted forecast. It's low complexity, gets your team thinking in probabilities by stage, and exposes the most obvious data hygiene problems fast. The cheat sheet is direct about this: build the bottom-up cadence only after you have one team running a simple process consistently, and treat AI forecasting as a supplemental data point rather than a replacement for the bottom-up roll-up.

How do I know if our pipeline coverage number is actually healthy, or just looks healthy on paper?

The cheat sheet benchmarks coverage at 3×–4× quota, but that ratio is only meaningful if your stage exit criteria are enforced and your win rates are accurate. A bloated pipeline with poor opportunity hygiene will show 4× coverage while masking significant close risk — the cheat sheet calls this out directly as a pitfall. Cross-check your coverage ratio against actual stage conversion rates and pipeline waterfall data (adds, slips, pulls) to see if the volume is real.

How often should I be revisiting the capacity model once the annual plan is set?

Ramp actuals versus modeled ramp should be reviewed quarterly — the cheat sheet specifically flags ignoring ramp actuals as one of the most common and damaging planning pitfalls. Attrition rates and manager-to-rep ratios also need a quarterly check, since a 20% annual attrition buffer can go stale fast if hiring or turnover patterns shift mid-year. Treat the capacity model as a living document, not a one-time planning artifact.

Do I need a dedicated BI tool like Tableau or Looker to use the dashboards section, or can this work out of a CRM like Salesforce?

The core dashboards — pipeline coverage, pipeline waterfall, forecast vs. actuals, stage conversion — can be built natively in Salesforce or HubSpot if your data hygiene is solid. Where a BI tool earns its keep is in the integrated GTM report, where you're merging sales, marketing, and CS data into a single view and running cohort or correlation analysis that CRM reporting can't handle cleanly. Start with what you have and add a BI layer when the cross-functional reporting requirements outgrow your CRM's native capabilities.