What a run rate assumes
Multiplying one month by 12 or one quarter by 4 embeds a set of assumptions in a single number: that the period was typical, that nothing in the market shifts, that the revenue which arrived will arrive again, and that selling capacity stays constant. Every one of those fails routinely at a growing B2B SaaS company.
Seasonality breaks it immediately
ORM's customer data shows Q2 and Q4 running stronger than Q1 and Q3, and the third month of a quarter running stronger than the first two. A run rate taken off the end of Q4 annualizes the best period of the year. A run rate taken off January annualizes the worst. Neither calculation is wrong. Both answers are.
Conditions change and a run rate cannot see it
The most common reason a SaaS forecast misses is that something in the business or the market changed while the model was still built on old assumptions. A competitor enters and compresses average deal size. Buyers cut costs under pressure and win rates fall. Uncertainty stretches the time from qualified to closed. A territory redesign distracts the field for a quarter while coverage still looks fine on paper.
A run rate has no mechanism for any of that. It carries last period's deal size, win rate, and cycle length forward by construction. Forecast accuracy is a property of how fast a model registers those shifts, which is the one thing arithmetic on a closed period cannot provide.
Where run rate earns its place
Run rate works as a sanity check and as a scale descriptor. When a bottom-up forecast lands 60% above the current run rate, the gap needs an explanation grounded in pipeline, headcount, or pricing. It is also the right tool when there is not enough history for anything better, or when describing the size of a new product line in its first quarters.
What it cannot do is describe the shape of a quarter before the quarter happens. That requires decomposing revenue into its real sources:
- Carry-over deals already in pipeline on day one and expected to close this period - In-quarter deals that do not exist yet and will be created, qualified, and closed inside the period - Pull-forward deals from later quarters that close early, usually at a discount
Build the sales forecast that way and run rate becomes a reference point rather than a plan. The decomposition is walked through in how to forecast revenue.
Frequently Asked Questions
Is a run rate a forecast?
No. A run rate describes a period that already closed and projects it forward on the assumption that nothing changes. A forecast starts from pipeline, capacity, and retention, and it updates as those inputs move.
When is a run rate accurate enough to use?
When there is too little history for anything better, at a business with a handful of contracts, or when you need a scale descriptor for a new product line. It also works as a sanity check against a bottom-up forecast.
Why do run rate and forecast disagree?
Seasonality and changing conditions. ORM's customer data shows Q2 and Q4 running stronger than Q1 and Q3, so the period you annualize decides the answer. A forecast built on current pipeline and win rates picks up shifts that a run rate carries forward unchanged.
What should you use instead of a run rate?
Decompose the period into carry-over pipeline expected to close, business that has to be created and closed inside the period, and deals pulled forward from later quarters. That decomposition tells you the shape of the quarter early enough to change it.
Put these metrics to work
ORM builds custom revenue forecast models that turn concepts like run rate vs forecast into prescriptive action for your team.
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