Impact estimate
Estimate how much pipeline sits inside one region, persona, form, or after-submit layer before dashboards localize the leak.
Rare failures can still be expensive. On high-exposure sales-led paths, a few weeks of partial degradation can put six or seven figures of pipeline at risk.
Based on editable scenario inputs. Pipeline at risk is not lost revenue or recovered revenue.
Pick a starting scenario, then put in your own numbers. Every output comes from your inputs and the formulas below.
A total collapse moves every chart. A partial leak does not. That is the expensive case.
One region, persona, or form barely moves the weekly total. The affected buyers are real, the dip is not visible.
The symptom shows up weeks later as no-shows, a Booked to Held drop, or a campaign that underperforms.
A dip does not say which path, region, persona, form, or after-submit layer degraded. Localizing it is the fire drill.
The detection window is where the exposure accrues: every week of lag multiplies the affected requests in the estimate above.
External buyer-side checks per Path and approved buyer context, with kept evidence. The estimate above shrinks where it hurts: the detection window and the reconstruction work.
When the Path last demonstrably worked for an outside buyer, with evidence attached.
How long the leak may have been open, reconstructed from run history.
Which region, persona, or form degraded. Not a vague dip in a chart.
Replay, screenshots, and submit state from a real outside buyer run.
Confirmation, invite, follow-up, or silence in covered channels, judged against expected follow-through.
A rerun that shows the Path working again, instead of waiting for the metric to recover.
Engineering can script one happy path. That is not the hard part.
Four multiplications. Nothing hidden, nothing weighted behind the scenes.
affected requests = requests per week × share of requests affected × weeks before noticed × paths affected
opportunities at risk = affected requests × request-to-opportunity conversion
pipeline at risk = opportunities at risk × average deal size
revenue exposure (weighted) = pipeline at risk × win rate
Opportunity value and conversion defaults lean on public B2B SaaS benchmarks: SaaS Capital and KeyBanc surveys for ACV, Ebsta and Pavilion GTM benchmarks for win rates, and published SQL-to-opportunity ranges. Volume and detection delay are not credibly benchmarked anywhere, so they are scenario choices you should replace with your own numbers. Research on lead response time, from the Harvard Business Review study onward, supports one direction: delayed or missing follow-up sharply degrades qualification odds.
What this estimate does not claim. It is a scenario model of exposure, built from your inputs. It does not claim lost revenue, recovered revenue, attribution truth, CRM truth, or a conversion lift from using BookedDemo. Use it to prioritize which Paths deserve coverage.
Start with the path that carries the most exposure in your estimate. One audit shows what an outside buyer reached and what arrived after submit.
