# When the growth model assumes unlimited capacity > Projected volume grows while sales, support or infrastructure effort stays fixed. Diagnose the cause, choose a bounded correction and verify forecast scenarios that include binding operating constraints. Source: https://saas-marketing.net/guides/growth-forecast-assumes-unlimited-capacity/ Topic: SaaS Growth Marketing Type: guide Published: 2026-09-17 Last updated: 2026-09-17 Publisher: SaaS Marketing (saas-marketing.net) License: CC BY 4.0. Quote or republish with attribution and a link to https://saas-marketing.net/guides/growth-forecast-assumes-unlimited-capacity/ ## Short answer Projected volume grows while sales, support or infrastructure effort stays fixed. Start with this check: List the operating resources required at each projected volume level. The corrective action is to add capacity thresholds and explicit expansion costs to the scenario model. ## Key takeaways - List the operating resources required at each projected volume level. - Add capacity thresholds and explicit expansion costs to the scenario model. - Do not present a planning scenario as a prediction or guaranteed trajectory. - Review forecast scenarios that include binding operating constraints. --- Projected volume grows while sales, support or infrastructure effort stays fixed. The useful response is a diagnosis that changes a decision, not another report describing the symptom. Use this play with the experiment owner and the analyst responsible for design integrity. The working evidence should include hypothesis, assignment rules, metric definition and decision record, with private or sensitive details removed from any shared example. ## Confirm the problem in the actual workflow List the operating resources required at each projected volume level. Start with one representative case and follow it from the original action to the reported outcome. Identify where the observed behavior first differs from the intended process. A screenshot of a final dashboard can be useful, but it may hide the source record, a delayed update or a decision made elsewhere. Keep the unit of analysis explicit: the prespecified eligible user or account cohort. The same label can conceal different populations or stages. Before comparing two results, check that they describe the same kind of work and have had a comparable chance to complete it. ## Separate the visible symptom from the cause Check the design before interpreting a result. Assignment, exclusions, outcome timing and stopping rules can change the meaning of an apparently precise statistic. Separate practical effect from statistical evidence and keep guardrails beside the primary outcome. The symptom in this case is specific: projected volume grows while sales, support or infrastructure effort stays fixed. Ask which piece of evidence would distinguish an operating failure from a measurement failure or a mismatch in the original plan. If the evidence is unavailable, record the missing source and its owner instead of treating the preferred explanation as established fact. ## A situation to work through A model can become unrealistic when every new customer requires assisted onboarding but the implementation team never expands. This is an illustrative situation, not a reported client case. Record the equivalent evidence and assumptions for your own workflow. ## Choose the smallest useful correction Add capacity thresholds and explicit expansion costs to the scenario model. Keep the change narrow enough that the responsible people can implement and inspect it. If a correction changes several things at once, describe it as a combined operating change; do not later claim that one small element caused the whole result. Assign the correction to the experiment owner and the analyst responsible for design integrity. Agree which artifact will show that the work is complete. An owner without an observable acceptance condition can close a task while leaving the original problem unresolved. A detailed checklist without an owner creates the opposite problem: the evidence requirement exists, but nobody is accountable for producing it. ## Preserve the important limitation Do not present a planning scenario as a prediction or guaranteed trajectory. This condition belongs beside the recommendation because it can change the decision. It should not disappear when the plan becomes a short presentation or a status update. A higher signup rate is not automatically a better activation path if the removed step helped users reach a useful workflow. Review the complete sequence and the relevant customer outcome. A bundled product change can be evaluated as a bundle without claiming to isolate every component. ## Verification worksheet | Review item | What to record for this issue | Owner | Evidence | | --- | --- | --- | --- | | Observed symptom | Projected volume grows while sales, support or infrastructure effort stays fixed. | | | | Diagnostic test | List the operating resources required at each projected volume level. | | | | Proposed correction | Add capacity thresholds and explicit expansion costs to the scenario model. | | | | Guardrail | Do not present a planning scenario as a prediction or guaranteed trajectory. | | | | Review measure | Forecast scenarios that include binding operating constraints | | | Download a working copy and follow the [worksheet instructions](/resources/#using-worksheets). Keep unknown facts visible rather than filling gaps with guesses. ## Decide whether to keep, revise or stop the change Review forecast scenarios that include binding operating constraints after the agreed observation period. Keep the correction when the intended behavior is verified and the guardrail remains acceptable. Revise it when the diagnosis was useful but the intervention did not resolve the cause. Stop and reassess when new evidence shows that the original problem was framed incorrectly. Record what changed in hypothesis, assignment rules, metric definition and decision record. This gives the next review a stable starting point and prevents a definition change from being mistaken for a performance improvement. ## Related methods and next steps - [B2B SaaS Growth: The Levers That Move the Model](/guides/b2b-saas-growth-growth/) - [SaaS Growth Model: Build the Equation Before Tactics](/guides/saas-growth-model/) - [Growth experiment velocity: measure learning capacity](/research/experiment-velocity-benchmarks/) - [SaaS growth model worksheet](/templates/growth-model-spreadsheet/) Return to the [saas growth topic guide](/saas-growth/), browse its [complete resource collection](/topics/saas-growth/), or use the [working resource library](/resources/). The [primary reference](https://docs.statsig.com/) provides relevant platform or methodological context; the diagnosis and example here are original editorial guidance. ## Frequently asked questions ### What is the first diagnostic check? List the operating resources required at each projected volume level. Inspect the actual working record or customer path rather than relying only on a summary report. ### What should change after the diagnosis? Add capacity thresholds and explicit expansion costs to the scenario model. Record the owner and the evidence needed to verify the correction. ### What limit should the team keep visible? Do not present a planning scenario as a prediction or guaranteed trajectory. A local improvement does not establish a universal benchmark or guarantee a commercial result. ### How should the correction be evaluated? Review forecast scenarios that include binding operating constraints using a consistent unit and observation window. Keep the original evidence and record any measurement changes.