What does the leak finder calculate?

It calculates stage-by-stage conversion and drop-off using your counts for MQL, SQL, opportunity, and won. It then identifies the stage with the highest drop-off rate as the largest leak.

Does this tool infer causes automatically?

No. It does not infer hidden causes. It only quantifies where leakage is highest so your team can investigate root causes with qualitative context.

Why do I get an input error for funnel counts?

Counts must be monotonic: MQL must be greater than or equal to SQL, SQL greater than or equal to opportunity, and opportunity greater than or equal to won. This ensures rates and drop-offs are mathematically valid.

Free tools

Free MQL to SQL Funnel Leak Finder

Use stage counts to identify the highest-leak funnel step and prioritize fixes.

Copy-paste outputs

Win high-intent buyers from ChatGPT, Gemini, Claude, Perplexity, and AI Overviews before your competitors do.

One operating layer for monitoring, measurement, content action, and technical cleanup.

Start now See features

AI Visibility TrackingCompetitive RankingSentiment by ModelSource CitationsAI Overviews TrackingPrompt MonitoringAI Visibility TrackingCompetitive RankingSentiment by ModelSource CitationsAI Overviews TrackingPrompt Monitoring

Content GapsAI InsightsAdvanced AnalyticsData CopilotBlog GenerationUGC CampaignsLLM CouncilContent GapsAI InsightsAdvanced AnalyticsData CopilotBlog GenerationUGC CampaignsLLM Council

Shopping IntelligenceCrawler MonitoringGEO OptimizationMulti-Brand ManagementShopping IntelligenceCrawler MonitoringGEO OptimizationMulti-Brand Management

Tool 01

MQL to SQL Funnel Leak Finder

Identify the biggest drop-off stage between MQL, SQL, opportunity, and won.

Funnel Counts

Enter observed funnel counts. Tool only uses deterministic calculations.

MQL Count *

SQL Count *

Opportunity Count *

Won Count *

Leak Analysis

Find the largest conversion leak and its priority.

Enter MQL count and downstream stage counts to analyze leaks.

How it works

MQL to SQL Funnel Leak Finder: methodology and worked example

How this tool computes its result

Takes four raw funnel-stage counts (MQL, SQL, Opportunity, Won) and computes three sequential stage transitions — MQL→SQL, SQL→Opportunity, Opportunity→Won — each with `conversionRate = to/from*100`, `dropOffCount = max(from-to, 0)`, and `dropOffRate = dropOff/from*100`, all rounded to 2 decimals. Overall `mqlToWinRate = won/mql*100`. The stage with the highest `dropOffRate` is selected via `reduce` as the "biggest leak," and a priority label is derived purely from that drop-off rate: Critical (≥50%), High (≥30%), Medium (≥20%), otherwise Low. Inputs are hard-gated as monotonic — the calculation returns `null` (and an inline error shows) unless MQL ≥ SQL ≥ Opportunity ≥ Won.

Worked example

MQL=1000, SQL=350, Opportunity=120, Won=35: MQL→SQL conversion 35.00%, drop-off 650 (65.00%); SQL→Opportunity conversion 34.29%, drop-off 230 (65.71%); Opportunity→Won conversion 29.17%, drop-off 85 (70.83%). Overall MQL→Won rate = 3.50%. Biggest leak = Opportunity to Won at 70.83% drop-off → Priority "Critical" (≥50%).

When not to use this tool

Only models a fixed 4-stage MQL→SQL→Opportunity→Won pipeline — funnels with different or additional named stages (a separate "demo booked" or "trial" step, for instance) have to be awkwardly mapped onto these four buckets or cannot be represented at all.

Common mistakes

- Entering counts that are not monotonically decreasing (e.g. more Opportunities than SQLs) — the tool does not attempt a partial calculation; it blocks the entire analysis with "Funnel counts must be monotonic" until every stage is ≤ the stage before it.
- Leaving a field blank and expecting it to be treated as "unknown" rather than zero — `parseInt(value, 10) || 0` coerces empty or invalid input straight to 0, which usually fails the monotonic check if a later stage is non-zero.
- Reading "biggest leak" as the stage that lost the most people in absolute terms — it is actually chosen by drop-off rate (percentage), so a low-volume stage with a high percentage loss can outrank a high-volume stage with more total records lost.

Ready to dominate AI search visibility?

Track where your brand shows up in AI answers, close the content gaps that cost conversions, and stay visible across ChatGPT, Claude, Gemini, Perplexity, and Grok.

Start now