A bottom-up forecast starts with what is actually in front of your sellers and works upward. Done properly it produces a number you can defend line by line, and it tells you which specific deals or which specific reps carry the quarter. Done partially, which is the common case, it becomes a weighted pipeline report with a more impressive name. The difference is whether you model the revenue that does not exist yet.
What is a bottom-up sales forecast?
A forecast assembled from individual production units rather than divided down from a target.The units are open opportunities and selling capacity. You calculate what each unit is likely to produce, sum them into segment and team totals, then roll those into a company number. Top-down works the other direction, starting from a market size or a growth rate and allocating the result downward.
Bottom-up wins on defensibility. Every dollar in the total traces to a deal, a rep, or a documented rate. It loses on speed, since building it requires opportunity-level data that many teams have not cleaned. Most revenue organizations run both and treat the difference as the real planning conversation.
What layers does the build need?
Four. Skipping the third is why most bottom-up forecasts miss.| Layer | Source | What it answers |
|---|---|---|
| Carry-over pipeline | Open deals on day one with in-period close dates | What should close from what we can see |
| In-quarter creation | Historical create-and-close-same-period volume | What will be created and closed inside the period |
| Pull-forward | Deals with future-period close dates | What might land early, and at what discount |
| Capacity ceiling | Ramped rep count times productive quota | Whether the team can physically deliver the total |
How do you build the carry-over layer?
Take day-one open pipeline with in-period close dates, apply conversion rates from your own history, then cut the deals that are not really alive.Start by pulling every open opportunity with a close date inside the forecast period, as of day one. Segment it, because enterprise and SMB conversion behavior share nothing but a column header.
Apply stage conversion rates calculated from twelve months of your own closed opportunities, not CRM defaults. Then make two corrections that most builds skip.
First, remove stale deals. Any opportunity with no change to stage, amount, or close date in twelve months should be excluded regardless of what stage it sits in. More than 10 percent of open pipeline across our customer base meets that description.
Second, correct for value. Deals close for less than their recorded amount. A pipeline averaging $80,000 per deal against $40,000 in average closed-won value overstates every period by half. Calculate your own realization ratio and apply it.
The reality check on this layer is blunt. Of the pipeline carrying in-quarter close dates on day one of the quarter, roughly 20 percent closes in that quarter. The other 80 percent of that value does not land in the period it was promised to. Build the carry-over layer expecting that shape rather than assuming stage probabilities will carry you.
How do you model the in-quarter creation layer?
Count how much revenue you have historically created and closed inside the same period, then project it forward against current demand generation.Pull the last six to eight quarters of closed-won deals and filter to those whose created date and closed date fall inside the same quarter. That volume is your in-quarter motion. Express it as a share of total closed revenue for the period and as an absolute dollar figure.
Segment this too. Short-cycle SMB business often produces a large share of quarterly revenue from deals that did not exist on day one. Enterprise business rarely produces any, since the cycle is longer than the period. If your enterprise segment shows meaningful in-quarter creation, check whether reps are creating opportunities late to skip stage hygiene.
Then adjust for what is different now. If marketing spend dropped, if a competitor entered and created pricing pressure, or if buying decisions slowed across the market, the historical in-quarter rate will overstate what happens this period. Deal cycles stretch when buyers get uncertain, and stretched cycles kill the in-quarter layer first.
This layer is the reason pipeline coverage alone answers nothing. A team at 2.5x coverage with a strong in-quarter motion can beat a team at 4x that closes nothing it did not start with.
How do you check the capacity ceiling?
Multiply ramped-equivalent headcount by realistic per-rep production and compare against the sum of layers one through three.Count reps by ramp status rather than by headcount. A rep in month two of a six-month ramp is not a full unit of capacity. Weight them by expected productivity for their tenure.
Multiply by per-rep production derived from actuals, not from quota. Quota is a target, and if the team attained 78 percent of quota last year, per-rep production is 78 percent of quota. Using the quota number here builds the miss into the model.
When the summed layers exceed the capacity ceiling, the forecast is wrong no matter how good the deal-level math is. That result usually means the pipeline layer is carrying deals that will not get the attention they need. Sales velocity analysis will show you where the constraint sits, whether it is deal count, deal size, win rate, or cycle length.
How do you reconcile bottom-up against top-down?
Subtract, then convert the gap into pipeline creation and headcount requirements with dates attached.The gap is the output, not a problem to negotiate away. If bottom-up lands at $8.2M against a $10M plan, the $1.8M has to come from somewhere specific: more pipeline created by a date that allows it to close, higher conversion on existing pipeline, or capacity added early enough to ramp.
Work the gap backward through your average cycle length. If mid-market deals take 90 days from creation to close, pipeline that has to close in this quarter needed to exist last quarter. That constraint kills most late-quarter recovery plans before they start, and naming it in week two rather than week ten is the whole point of building bottom-up.
Coverage math will not surface this. The 3x pipeline coverage rule is wrong precisely because it treats pipeline as one undifferentiated pool rather than four layers with different timing and different conversion behavior.
Frequently Asked Questions
What is a bottom-up sales forecast?
A bottom-up sales forecast builds the revenue number from individual units of production, usually open opportunities and rep capacity, then sums upward to a company total. The alternative is top-down, which starts from a market or growth target and divides it among teams. Bottom-up produces a number you can defend deal by deal.
What data do you need for a bottom-up forecast?
Open pipeline with amounts and close dates, stage conversion rates calculated from your own closed-won history, average sales cycle length by segment, rep headcount with ramp status, and historical in-quarter creation-to-close volume. The last input is the one most teams do not track and the one that decides whether the forecast holds.
How is bottom-up different from weighted pipeline?
Weighted pipeline is one layer inside a bottom-up forecast. It covers revenue from deals that already exist in CRM. A complete bottom-up build also models deals that will be created and closed inside the same period, deals that may be pulled forward from future periods, and the capacity constraint that limits all of it.
How long does a bottom-up forecast take to build?
The first build takes a few days if your conversion rates are already calculated and a few weeks if they are not. Deriving real stage conversion rates and cycle lengths by segment from historical opportunity data is the bulk of the work. Once the rate library exists, refreshing the forecast is a weekly exercise.
Should a bottom-up forecast match the top-down plan?
No, and forcing a match destroys the value of doing both. The gap between the two is the planning output. A bottom-up number below the top-down plan tells you how much pipeline creation or capacity has to change, and when.
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