RevOps
Turn traffic and conversion rates into MQL volume and cost per MQL.
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- Claude, via MCP
How do you calculate MQLs and cost per MQL?
MQLs = website visitors × visitor-to-lead rate × lead-to-MQL rate. Cost per MQL = marketing spend ÷ MQLs. With 20,000 visitors, 2.5% becoming leads and 40% of leads qualifying, you get 500 leads and 200 MQLs; on $150,000 of spend, that is $750 per MQL. To plan backwards, divide the customers you need by your MQL-to-customer rate.
You’re generating 200 MQLs/month at $750 each. If sales is rejecting a meaningful share of these, the fix usually isn’t more MQL volume, it’s tightening the lead → MQL threshold so what crosses it is actually sales-ready.
Sales rejecting MQLs at scale? Use the MQL to SQL Disconnect Analyzer to size what that’s actually costing you.
About this calculator
Before diagnosing why sales rejects MQLs or why cost per lead looks high, you need the base math: how many MQLs does your traffic actually produce, and what does each one cost. This calculator turns website visitors, your visitor-to-lead rate, and your lead-to-MQL rate into monthly lead and MQL volume, plus cost per lead and cost per MQL from your spend.
How to use it
- Enter monthly website visitors and your visitor → lead conversion rate.
- Enter your lead → MQL rate, the share of leads that clear whatever threshold your team uses to call a lead marketing-qualified.
- Enter monthly marketing spend for the period.
- Read monthly leads, monthly MQLs, cost per lead, and cost per MQL.
Methodology
Leads per month is visitors × visitor-to-lead conversion rate. MQLs per month is leads × lead-to-MQL rate, a straight two-stage funnel calculation.
Cost per lead is total spend ÷ leads. Cost per MQL is total spend ÷ MQLs. Both use the same spend figure, so cost per MQL will always be higher than cost per lead whenever the lead-to-MQL rate is under 100%.
This calculator assumes spend is fully attributable to the traffic and leads entered, if your spend figure covers channels or activity that didn't drive this specific visitor volume, cost per lead and cost per MQL will be inflated.
It doesn't account for what happens after the MQL stage. Volume and cost here say nothing about whether sales actually accepts and converts those MQLs, that's a separate question this tool deliberately doesn't answer.
MQL volume, cost per MQL and MQLs-needed formulas
Leads = Visitors × Visitor-to-lead rate MQLs = Leads × Lead-to-MQL rate Cost per lead = Marketing spend ÷ Leads Cost per MQL = Marketing spend ÷ MQLs MQLs needed (work by hand) = New customers needed ÷ (MQL-to-SQL rate × SQL-to-customer rate)
- Visitors
- Monthly website visitors (users or sessions, used consistently).
- Visitor-to-lead rate
- Share of visitors who submit a form, book a call or otherwise become a known contact.
- Lead-to-MQL rate
- Share of leads that meet your MQL definition, usually a lead score or fit-plus-intent rule.
- Marketing spend
- Monthly spend that produced this traffic: media, content, tools and agency fees if you want a fully loaded figure.
- MQL-to-SQL and SQL-to-customer rates
- Your own downstream conversion rates from the CRM. These are not inputs in this tool.
The first four lines are what the calculator computes. The last line is the reverse plan that most people searching for an MQL calculator also need: start from revenue, convert it to customers (revenue target ÷ average deal value), then to SQLs and MQLs using your own conversion rates, and compare the result with the MQL volume your traffic produces here. Cost per MQL is always cost per lead ÷ lead-to-MQL rate, so tightening the MQL definition raises cost per MQL even when nothing else changes; judge it against cost per customer, not in isolation. In HubSpot's default lifecycle stages an MQL is a contact "your marketing team has qualified as ready for the sales team" (HubSpot), so the rate depends entirely on the rule your team sets.
Worked example: an Indian B2B SaaS team planning to a customer target
A Kolkata-based B2B SaaS company gets 30,000 website visitors a month. 2% become leads and 35% of leads meet its MQL score. Monthly marketing spend is ₹6 lakh. From the CRM, 25% of MQLs become SQLs and 20% of SQLs become customers. The plan needs 10 new customers a month.
- Leads = 30,000 × 0.02 = 600
- MQLs = 600 × 0.35 = 210
- Cost per lead = ₹6,00,000 ÷ 600 = ₹1,000
- Cost per MQL = ₹6,00,000 ÷ 210 = ₹2,857
- MQLs needed = 10 ÷ (0.25 × 0.20) = 10 ÷ 0.05 = 200
- Customers from current MQLs = 210 × 0.25 × 0.20 = 10.5, so marketing cost per customer ≈ ₹6,00,000 ÷ 10.5 = ₹57,143
At 210 MQLs the team is just above the 200 it needs, so volume is not the constraint. If sales asks for a stricter MQL rule that cuts lead-to-MQL to 25%, MQLs fall to 150 and cost per MQL rises to ₹4,000; that only makes sense if MQL-to-SQL rises above 33% (10 ÷ (150 × 0.20)), otherwise the company misses its customer target. Run the stricter rule as a test and measure both rates before switching.
MQL to SQL conversion rate by industry
| Industry | MQL to SQL rate |
|---|---|
| B2B SaaS | 13% |
| Software development | 14% |
| IT & managed services | 13% |
| Financial services | 13% |
| Fintech | 11% |
| Healthcare | 13% |
| Manufacturing | 16% |
| Transportation & logistics | 19% |
| Higher education | 21% |
| eCommerce | 23% |
| Real estate | 10% |
| Legal services | 10% |
What is a good MQL rate and cost per MQL?
Good is whatever produces enough customers at an acceptable cost per customer. Three checks are more useful than a single benchmark:
- Visitor-to-lead rate against page type. Unbounce reports a 2024 median of 6.6% for dedicated landing pages, while whole-site rates are usually much lower because most visitors are not on an offer page: First Page Sage puts the average B2B SaaS visitor-to-lead rate at 1.1% in its 2026 report, with most industries between 1.1% and 3.1%.
- MQL-to-SQL rate as the test of your MQL definition. First Page Sage puts B2B SaaS at 13% in its client data. If sales accepts far fewer of your MQLs than that, the definition is probably too loose; if it accepts almost all, you may be filtering out leads sales would take.
- Cost per MQL against deal value. Cost per MQL ÷ (MQL-to-SQL × SQL-to-customer) gives marketing cost per customer. Compare that with first-year deal value and your LTV : CAC ratio.
A decision rule: if marketing cost per customer is comfortably inside your CAC target and sales has capacity, buy more MQLs; if sales rejects many of them, fix the definition first with the lead scoring logic architect and the MQL to SQL handoff playbook.
MQLs in India vs the US
Indian B2B teams receive a large share of enquiries through WhatsApp, phone calls and marketplaces such as IndiaMART as well as web forms, so a website-only visitor-to-lead rate undercounts leads. Log every source in the CRM before calculating MQL rates, or the lead-to-MQL rate will look higher than it is. Cost per lead in rupees is typically much lower than US figures, which makes it tempting to accept loose MQL rules; the cost that matters is sales time spent on poor leads. The average cost per lead by industry in India guide gives local context, and a HubSpot consultant in India or Zoho CRM consultant can set up scoring so MQLs are counted the same way each month.
US paid search leads are expensive: LocaliQ's 2025 Google Ads benchmarks put the average cost per lead at $70.11 across industries (PPC Land), before any MQL filter is applied. At a 40% lead-to-MQL rate that would mean about $175 per MQL from search alone. US B2B teams also tend to run formal lead scoring in a marketing automation platform, so the lead-to-MQL rate is usually a score threshold that can be tuned, which makes the reverse calculation above the main planning tool.
Common MQL calculation mistakes
- Counting MQLs that sales has never seen, for example leads scored as MQL but not routed to anyone.
- Changing the MQL definition mid-quarter and comparing the new MQL count with the old one.
- Dividing all marketing spend, including brand and events that produced no web traffic, by web-sourced MQLs.
- Reporting cost per MQL without cost per SQL or per customer, which rewards loose qualification.
- Using a single blended lead-to-MQL rate when demo requests and content downloads qualify at very different rates.
- Planning MQL targets from an industry benchmark instead of your own MQL-to-SQL and win rates.
Sources
- Use contact and company lifecycle stages, HubSpot Knowledge Base. Accessed 2026-10-17.
- MQL to SQL Conversion Rate By Industry: 2026 Report, First Page Sage. Accessed 2026-10-17.
- B2B Conversion Rates By Industry: 2026, First Page Sage. Accessed 2026-10-18.
- Conversion Benchmark Report 2024, Unbounce. Accessed 2026-10-17.
- Google Ads costs rise 12.88%, PPC Land (reporting LocaliQ 2025 Google Ads benchmarks). Accessed 2026-10-17.
Go deeper
FAQ
It varies widely by industry, traffic quality and offer. First Page Sage's B2B Conversion Rates By Industry 2026 report (its client data, January 2022 to August 2025) puts the average visitor-to-lead rate at 1.1% for B2B SaaS, and every industry it covers except legal services (7.4%) falls between 1.1% and 3.1%. High-intent bottom-of-funnel traffic converts much higher; broad top-of-funnel content traffic converts much lower.
Not necessarily, cost per MQL only tells you what a qualified lead costs to generate, not whether it's worth it. Compare cost per MQL against your average deal value and sales-accept rate before concluding the number is a problem.
That's a separate, more diagnostic question, this tool only computes volume and cost, not disconnect between marketing's MQL definition and what sales considers sales-ready. Use the MQL to SQL Disconnect Analyzer once you know your MQL volume and want to size what rejected leads are actually costing.
Usually, yes, if sales is rejecting a meaningful share of MQLs, the fix is typically a tighter lead-to-MQL threshold rather than more raw lead volume. Fewer, better-qualified MQLs at a slightly higher cost per MQL often produce more revenue than a larger volume of loosely qualified ones.
An MQL is a lead marketing judges ready for sales, usually based on fit and engagement. An SQL is a lead sales has reviewed and accepted as a real potential customer. HubSpot's default lifecycle stages use these same definitions, and the conversion between the two is the best test of whether your MQL rule works.
Divide the revenue target by average deal value to get customers needed, then divide by your MQL-to-SQL rate and your SQL-to-customer rate. For 10 customers at a 25% MQL-to-SQL rate and a 20% SQL-to-customer rate, you need 200 MQLs. Then check whether the MQLs this calculator produces from your traffic cover that.
First Page Sage reports 13% for B2B SaaS in its client data, with industries ranging from about 10% to 26%. Definitions vary a lot between companies, so your own trend matters more than the benchmark: a falling rate usually means MQL criteria have loosened or lead sources have changed.
One where marketing cost per customer stays within your CAC target. Work it out as cost per MQL ÷ (MQL-to-SQL rate × SQL-to-customer rate) and compare with first-year deal value. A high cost per MQL is fine for large deals; a low one can still be poor value if few MQLs convert.
Combine fit (company size, industry, role) with intent (pricing page visits, demo requests, repeat engagement), agree the rule with sales in writing, and review it each quarter against MQL-to-SQL data. A written service-level agreement on follow-up time keeps the definition honest.
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.
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