A bottom-up forecast model is the most defensible way to produce a revenue projection because every number traces back to a real deal with a named account. When leadership asks why the forecast is what it is, you can answer at the opportunity level. Building one correctly means getting the stage weights right, segmenting the model by the right dimensions, and being honest about what the model cannot predict.
One adjustment a pure bottom-up model will miss: seasonality. Q2 and Q4 are usually stronger than Q1 and Q3, and within any quarter the third month is usually stronger than the first two. A stage-weighted model built on blended history spreads expected bookings evenly, so it reads month one as a shortfall and month three as a surge, every time. Layer a seasonality adjustment on top of the stage weights so the shape of the quarter is expected, not alarming.
Step 1: Define Your Stage Win Rates
The backbone of any weighted pipeline model is the historical win rate at each opportunity stage. This is not the same as your overall win rate. You need a conversion rate from each stage to closed won.
Pull every opportunity from the past four to eight quarters that reached at least one defined stage, and calculate what percentage closed.
| Stage | Definition | Historical Close Rate |
|---|---|---|
| Qualified | Confirmed budget, authority, need, timeline | Your actual data |
| Discovery complete | Discovery call done, next step confirmed | Your actual data |
| Technical evaluation | Active proof of concept or pilot underway | Your actual data |
| Proposal sent | Written proposal submitted | Your actual data |
| Verbal commitment | Buyer has indicated intent to proceed | Your actual data |
If your volume of historical closed deals is too low to produce statistically stable rates, use a larger lookback window or segment less granularly. A less precise rate grounded in real data is better than a precise rate based on intuition.
Step 2: Segment the Model by the Right Dimensions
A single rate applied across your entire pipeline is more accurate than no model, but it misses important variation. Segment your stage win rates and average deal sizes by at least two dimensions:
Segment or market. Enterprise deals convert differently than mid-market deals. If you blend them, you will systematically overstate or understate depending on your current pipeline mix. Opportunity type. New logo deals typically have lower win rates and longer cycles than expansion or renewal opportunities. Model them separately.If your volume supports it, segment further by deal source (inbound versus outbound), product line, or sales rep tenure. Add dimensions only when you have enough closed deals in each bucket to produce a stable rate.
Step 3: Build the Rep-Level Rollup
For each open opportunity, calculate the weighted value:
Weighted value = Deal amount x Stage close rateIf a deal is worth a certain amount and carries a close rate reflecting its current stage, the weighted value is that amount multiplied by the rate. Sum all weighted values at the rep level, then roll up to the team level.
A rep-level view lets you identify two problems at once: reps whose pipeline volume is insufficient relative to their quota, and reps whose pipeline is concentrated in a single large deal that will dominate the forecast whether it closes or not.
Step 4: Separate Committed Deals from Weighted Pipeline
Not all deals belong in the weighted calculation. Deals in late stages, particularly those with a verbal commitment or signed LOI, should be tracked separately as commit-category deals.
The commit category represents what the rep is willing to state publicly they expect to close. The weighted model represents what the math says should close. Both numbers matter:
- Weighted pipeline: Statistically expected revenue based on stage mix. - Committed forecast: Rep and manager judgment about which deals are actually going to land.
The gap between the two is a signal. If the weighted model is significantly higher than the committed forecast, either the model is generous or the reps are sandbagging. If the committed forecast is significantly above the weighted model, the reps are being optimistic. Investigate the gap rather than choosing one number over the other.
Step 5: Apply Cycle Length to Determine Period Eligibility
A deal's stage close rate answers the question of whether it will close at all. Cycle length answers whether it will close this period.
For each opportunity, calculate how long it has been open. Compare that to the median total cycle length for deals at that stage. Deals that are very early in a long cycle, regardless of their stage, are poor candidates for this quarter's forecast even if their stage probability looks good.
| Cycle position | Forecast eligibility |
|---|---|
| Early (under half of median cycle) | Remove from near-term forecast unless late stage |
| Mid-cycle | Include in weighted model with stage probability |
| Late (at or past median cycle) | Flag for close-date review; likely slipping |
Step 6: Present the Model With Its Assumptions Visible
The output of the model is only useful if the people reading it understand the inputs. For every forecast presentation, show:
- The stage win rates applied. - The segment split used. - The pipeline total by stage before and after weighting. - The committed number alongside the weighted number. - Any large single deals that are disproportionately affecting the total (and whether they are included or excluded from the base case).
A model presented as a black box erodes trust. Showing the inputs produces real conversations about the assumptions, which is where forecast accuracy actually improves.
For related frameworks, see revenue forecasting models and weighted sales forecast. For how manager and rep commits roll up into a team number, see roll-up forecast.
The Full SaaS Revenue Model: What a Bottom-Up Build Leaves Out
A stage-weighted rollup forecasts the pipeline you can see. A full SaaS revenue model needs more lines than that, and it should be built as a monthly ARR waterfall, where each month begins with the prior month's ending ARR:
| Line | How to forecast it | Common mistake |
|---|---|---|
| Carry-over new business | Open deals with in-quarter close dates, valued at closed-won, weighted by stage and close curve | Using CRM amounts. Only about 20% of day-one in-quarter pipeline typically closes in the quarter across ORM customers |
| In-quarter new business | Deals created and closed inside the quarter, from the same quarter in past years | Leaving it out entirely |
| Pull-forward | Deals from later periods that close early, usually discounted | Counting it as upside with no cost to next quarter |
| Expansion | Seats, products and price changes on existing accounts | Blending it with new business |
| Renewals | Contract value plus any price lift, timed to the renewal date, typically within 30 days either side | Applying one flat retention rate |
| Churn and contraction | Account-level risk tiers and product-level downgrades | Waiting for the cancellation notice |
1. Weights by motion. One set of stage weights for new business, expansion, renewals, enterprise and commercial deals averages away the differences that matter. Stage weights also only work when stages have strict entry and exit criteria. 2. Weekly seasonality. Revenue does not arrive evenly. In one example ORM curve, 34% of the quarter has closed by the end of week six, against 46% on a straight line. 3. Stale deals out. ORM treats a deal with no change in stage, close date or amount for 12 months as stale, and more than 10% of pipeline typically fails that test.
For the worked waterfall, see the ARR waterfall that reconciles, and for the three sources of new business, how to forecast revenue.
Common Mistakes
Using a single win rate across all pipeline. Stage-level rates exist for a reason. Blending them produces a number that is accurate for no specific deal. Not segmenting by opportunity type. New logo and expansion deals behave differently. A model that treats them identically will mislead you about where to focus. Ignoring cycle length. A deal at a high-probability stage that opened two weeks ago is not a realistic close this quarter. Stage probability and timing are separate inputs; the model needs both. Updating rates with insufficient data. Recalibrating win rates on small sample sizes creates noise. Set a minimum closed-deal volume before changing the rates. Treating the model as the answer. The model organizes evidence. Rep-level deal inspection is still required.Frequently Asked Questions
What is a bottom-up revenue forecast model?
A bottom-up forecast model builds the revenue projection from individual opportunity data rather than from a top-line growth assumption. Each deal is assessed by its stage, expected value, and probability of closing in the period. The aggregate of these deal-level projections becomes the forecast. This approach is more grounded than top-down models because it connects to named accounts and actual pipeline activity.What inputs does a stage-weighted forecast model require?
The model needs four inputs per opportunity: current stage, estimated deal value, expected close date, and the historical win rate for that stage in your business. The stage win rate is the key calibration input. Without it, you are weighting deals by assumption rather than by evidence.How do you handle the difference between the rep's forecast and the model's output?
Treat the model output and the rep's call as two separate inputs. Where they diverge significantly, investigate the individual deal. Sometimes the rep has context the model cannot see, such as a verbal commitment or a change of executive sponsor. Sometimes the model is catching a deal the rep overvalues out of familiarity. Neither is automatically right. The manager's job is to resolve the gap with evidence.Frequently Asked Questions
What goes into a SaaS revenue forecast model?
New business, expansion, renewals, and churn and contraction. New business itself has three sources: deals already in pipeline, deals created and closed in the quarter, and deals pulled forward. Build it all on a monthly ARR waterfall.
Should the model use CRM deal values?
No. Most deals close for less than their pipeline value. In one example, the average open deal was $80,000 and the average closed-won deal $40,000. Value pipeline at what it actually closes for.
What is a bottom-up revenue forecast model?
A model built up from single deals rather than a top-line growth rate. Each deal gets a stage, a value and odds of closing in the period, and the total becomes the forecast. It ties the number to real accounts and real pipeline.
What inputs does a stage-weighted forecast model require?
Four per deal: its stage, its value, its expected close date, and your past win rate for that stage. The win rate is the key one. Without it, you weight deals by guesswork rather than evidence.
How do you handle the difference between the rep's forecast and the model's output?
Treat them as two inputs. Where they differ a lot, look at the deal. Sometimes the rep knows something the model cannot see, like a verbal yes. Sometimes the model catches a deal the rep overrates. Settle it with evidence.
See how ORM turns these insights into action
ORM builds custom revenue forecast models for B2B SaaS companies. Not dashboards. Prescriptive analytics that tell you what to do next.
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