RevOps
Find the stage where your pipeline is bleeding the most deals.
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- Claude, via MCP
How do you find where your sales pipeline is leaking?
Multiply leads through each stage rate in turn: leads × lead-to-MQL × MQL-to-SQL × SQL-to-opportunity × win rate. The product is your lead-to-win rate. Then compare each stage rate with your own history and with benchmarks for that stage, because a 28% win rate and a 28% MQL rate mean very different things.
Your weakest link is Opp → Win at 28%. Lifting the lowest stage a few points compounds through every stage below it, fix the leak, don’t just pour in more leads.
Overall lead-to-win is the product of every stage, so a single weak conversion caps the whole funnel regardless of top-of-funnel volume.
About this calculator
Total lead-to-win rate hides where a funnel actually breaks. A pipeline can look fine in aggregate while one stage, often MQL→SQL or SQL→Opportunity, is quietly capping everything downstream. This calculator walks a lead volume through four stage conversion rates and flags the single weakest link, because fixing that one stage compounds through every stage below it.
How to use it
- Enter monthly leads at the top of funnel, and the four stage conversion rates: lead→MQL, MQL→SQL, SQL→opportunity, opportunity→win.
- Read the volume surviving at each stage, MQLs, SQLs, opportunities, and won deals.
- Check the verdict for your weakest stage, the lowest conversion rate among the four you entered.
- Prioritize fixing that stage before adding more top-of-funnel leads, since a weak stage caps everything that flows through it regardless of volume.
Methodology
Each stage multiplies the surviving volume from the stage above by that stage's conversion rate: MQLs = leads × lead→MQL rate, SQLs = MQLs × MQL→SQL rate, and so on through opportunities and wins.
Overall lead-to-win rate is the product of all four stage rates, not their average. A single weak stage caps the whole chain: a funnel with three 50% stages and one 10% stage converts at roughly the same overall rate as a funnel where every stage is 10%.
The "weakest link" flagged in the verdict is simply the lowest of the four rates entered. It's a diagnostic pointer, not a full root-cause analysis, the actual cause (routing delay, poor MQL definition, weak SDR follow-up, pricing friction) needs to be investigated separately.
This model assumes stage rates are independent of volume. In practice, pushing more raw leads into a weak filter (like MQL scoring) can change the rate itself, so re-check rates periodically rather than treating them as fixed constants.
Stage-by-stage funnel and overall conversion
MQLs = Leads × Lead→MQL rate SQLs = MQLs × MQL→SQL rate Opportunities = SQLs × SQL→Opportunity rate Won deals = Opportunities × Win rate Lead-to-win rate = Lead→MQL × MQL→SQL × SQL→Opp × Win rate
- Lead
- Any new contact entering the funnel in the month.
- MQL
- Marketing qualified lead: meets your fit and engagement rules.
- SQL
- Sales qualified lead: accepted by sales after a first conversation.
- Opportunity
- A deal with a defined need, buyer and timeline, open in the CRM.
- Win rate
- Share of opportunities that close as won.
The tool flags the lowest of the four rates as the weakest link. Treat that as a starting point, not a verdict: later stages naturally convert less than earlier ones, so the lowest number is often the win rate even when it is healthy. A better test is the gap between each stage and its own norm. Improving any one stage by the same relative amount raises won deals by the same amount, so fix the stage that is cheapest to improve, not only the smallest one.
Worked example: 4,000 leads a month through four stages
A SaaS company gets 4,000 leads a month. Its CRM shows 35% become MQLs, 45% of MQLs become SQLs, 55% of SQLs become opportunities and 28% of opportunities are won.
- MQLs = 4,000 × 35% = 1,400.
- SQLs = 1,400 × 45% = 630.
- Opportunities = 630 × 55% = 346.5.
- Won deals = 346.5 × 28% = 97.
- Lead-to-win rate = 0.35 × 0.45 × 0.55 × 0.28 = 2.43%.
- Lifting MQL→SQL from 45% to 55% (a 22% relative gain) raises won deals to 118.6, the same as lifting win rate from 28% to 34.2%.
The tool will flag the 28% win rate as the weakest stage, but the question is which improvement is cheapest. Tightening MQL rules and sales follow-up often costs less than changing win rate, which depends on product, price and competition. Price the gain with the pipeline velocity calculator, and if the gap is between marketing and sales, use the MQL to sales disconnect analyser.
MQL to SQL conversion rate benchmarks
| Industry | MQL to SQL rate |
|---|---|
| B2B SaaS | 13% |
| IT and managed services | 13% |
| Financial services | 13% |
What is a good pipeline conversion rate?
Judge each stage against its own norm rather than one overall number:
- Lead to MQL depends mostly on how strict your MQL rule is. A rising rate after loosening the rule is not an improvement.
- MQL to SQL is where marketing and sales disagree. A low rate usually means the MQL definition and the sales acceptance criteria do not match.
- SQL to opportunity tests discovery. If it is low, look at the first call, not the leads.
- Win rate depends on product, price and competition, and changes slowly.
The overall lead-to-win rate is useful for planning lead volume from a revenue target; the annual growth goal back-calculator does exactly that.
Pipeline leaks in India vs the US
Indian B2B funnels often have a large leak between lead and first conversation, because many leads come by phone or WhatsApp and are never logged as CRM records, or are logged without a source. Before fixing conversion rates, make sure every enquiry creates a record with a source, so the top of the funnel is complete. Price negotiation late in the deal is common too, which shows up as a lower win rate rather than as a stage leak.
US SaaS funnels more often split work between SDRs and AEs, so leaks cluster at the handover: SQLs accepted by an SDR but never turned into an opportunity by an AE. Report the SQL-to-opportunity rate by SDR and AE pair. The AE and SDR hand-off friction calculator sizes that loss.
Common pipeline leak mistakes
- Measuring stages in the same month for leads that move through the funnel over several months. Use cohorts by lead creation month.
- Changing the MQL definition mid-year and comparing rates across the change.
- Fixing the stage with the lowest rate when another stage is further below its own norm.
- Averaging stage rates instead of multiplying them to get the overall rate.
- Counting reopened or duplicate opportunities, which inflates the later stages.
Sources
- MQL to SQL conversion rate by industry, First Page Sage. Accessed 2026-10-19.
Go deeper
FAQ
Because overall lead-to-win is a multiplied chain, improving your strongest stage by 10 points does far less than improving your weakest stage by the same 10 points. A stage already converting at 70% has little room to improve; one converting at 15% usually has the most fixable friction and the most leverage.
Most commonly a mismatch between marketing's MQL definition and what sales actually considers qualified, plus routing delay (leads going stale before a rep reaches them) and unclear follow-up ownership. It's rarely a top-of-funnel volume problem.
Use a rolling 3-6 month average where possible. Single-month rates are noisy, especially at the opportunity→win stage where deal cycles can span months and distort a single period's numbers.
It guarantees more deals surviving that stage, which flows through to more wins if downstream stages hold steady. It doesn't guarantee deal value or win-rate at later stages stays the same as a higher volume of deals moves through them, watch for quality dilution if the fix just lowers the qualification bar.
A pipeline leak is a stage where more prospects drop out than they should, compared with your history or a reasonable benchmark. Leaks are found by tracking conversion between each pair of stages.
Cohort data is more accurate: take the leads created in one month and follow them through every stage. Monthly snapshots mix leads from different months and can hide or exaggerate leaks.
Enough to show where deals stall, usually four to seven, each with a clear entry rule. Stages without a clear rule make conversion rates meaningless.
Numbers looking off?
If the model says one thing and the pipeline says another, that gap is usually where the revenue is. A 30-minute call finds it.
It starts with a 30-minute call. Pick a time below.