Impact estimate
Model the exposure inside one affected slice: region, device, buyer profile, form, scheduler, or after-submit layer. Use it to decide which Path deserves proof first, while dashboards still show only a vague symptom.
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.
Built from editable scenario inputs. A prioritization model, not attribution.
Use numbers your team already knows. The affected slice can be one region, device, buyer profile, scheduler, form variant, or follow-up layer.
A total collapse moves every chart. A partial leak does not. That is the expensive case.
APAC expected 15 meetings, 12 happen. The weekly total still looks normal, so the affected buyers stay invisible.
No-shows, a Booked-to-Held drop, or campaign underperformance appears days or weeks later, far from the original break.
Dashboards show recorded symptoms. They do not prove whether the buyer saw slots, received an invite, or got a follow-up.
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, with kept evidence. The estimate above shrinks where it hurts: the detection window and the reconstruction work.
See whether one region, device, buyer profile, form, scheduler, or follow-up layer is the problem.
Catch the break closer to when it starts, not weeks later.
Keep evidence of what an outside buyer reached and what arrived after submit.
See when the Path last worked and when the first affected run appeared.
Rerun the same Path and context to prove recovery.
Cover the similar Paths and contexts that could share the same break.
A script can click one happy path. It does not maintain buyer identity, configured regional context, follow-through windows, evidence history, affected-window reconstruction, and fix verification.
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
Conversion and deal-size defaults lean on public B2B SaaS benchmarks. Volume and detection delay are not credibly benchmarked, so replace them with your own numbers. Research on lead response time is consistent on one point: delayed or missing follow-up sharply degrades qualification odds.
This is a prioritization model, not attribution. It sizes exposure from your inputs to help you decide which Paths deserve coverage first. It does not claim lost revenue, recovered revenue, CRM truth, or a conversion lift from using BookedDemo.
Use the estimate to choose the Path, region, device, buyer profile, or follow-through layer worth covering first. One audit shows what an outside buyer reached and what arrived after submit.
