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mql-definition

This skill should be used when the user asks to "define MQL", "what is an MQL", "set MQL criteria", "build an MQL definition", "design MQL qualification criteria", "define marketing qualified lead", "when should a lead become an MQL", "what makes a lead an MQL", "create MQL standards for our team", or any variation of defining, designing, or aligning on MQL criteria for B2B SaaS.

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MQL Definition Framework

An MQL (Marketing Qualified Lead) is a lead that marketing has determined is worth sales reviewing based on objective criteria. The MQL definition is the contract between marketing and sales. Marketing says "this lead meets our bar." Sales says "I'll follow up within the SLA." Without a shared, objective definition, marketing passes junk, sales ignores good leads, and both blame each other for pipeline misses.

The principle: an MQL definition must be objective, measurable, and derived from data. "Marketing thinks they're ready" is not a definition. "ICP fit score ≥ 20 AND behavior score ≥ 30, OR submitted a demo request with fit score ≥ 20" is a definition.

What Makes a Good MQL Definition

The 4 requirements

Requirement	What it means	Test
Objective	Based on measurable criteria, not subjective judgment	Can a machine apply the definition with no human input?
Data-derived	Built from analysis of closed-won deals, not from sales team opinions	Can you show the data that supports each criterion?
Agreed-upon	Marketing AND sales signed off on the same definition	Do both teams reference the same document?
Calibrated	Regularly adjusted based on MQL acceptance rate and conversion data	When was the last time you reviewed and adjusted the criteria?

If any requirement fails, the MQL definition is broken. An objective definition that sales didn't agree to gets ignored. A definition both teams agreed to but never calibrated drifts out of alignment with reality.

Building the MQL Definition

Step 1: Analyze closed-won deals

Pull the last 50-100 closed-won deals. For each, look at the contact who was the primary MQL (or the first marketing-sourced contact).

Data to capture per deal:

Data point	Where to find it	What it tells you
Company size at time of MQL	Enrichment data or CRM	Which company sizes actually buy
Industry	Enrichment data or CRM	Which verticals convert
Contact title/seniority	CRM	Which personas buy
Lead source	CRM	Which channels produce buyers
First conversion action	Marketing automation	What the buyer did first
MQL trigger action	Marketing automation	What action pushed them to MQL
Days from lead creation to MQL	CRM timestamps	How long the typical journey takes
Days from MQL to opportunity	CRM timestamps	How quickly MQLs convert to pipeline

Step 2: Identify patterns

From the closed-won data, answer:

What do MQLs who become customers look like? (firmographic profile)
What did they do before becoming an MQL? (behavioral pattern)
What was the trigger that made them sales-ready? (MQL trigger)

Common patterns in B2B SaaS:

Pattern	What it looks like	Implication for MQL definition
Most closed-won contacts are Directors+ at 50-500 employee SaaS companies	Your ICP is narrow. Fit criteria should be strict	High fit threshold. Low-fit leads should never MQL
70% of closed-won contacts submitted a demo request as their MQL trigger	Demo request is the primary buying signal	Demo request should be an instant MQL trigger (if fit passes)
Contacts who viewed the pricing page 2+ times close at 3x the rate of those who didn't	Pricing page visits are a strong buying signal	Add pricing page visits as a high-weight behavior trigger
Average time from lead to MQL is 45 days	Your nurture cycle is long	Don't expect instant MQLs from content downloads. Budget for nurture
Leads from organic search convert 2x better than paid social	Not all sources are equal	Consider source-weighted scoring or source-specific MQL thresholds

Step 3: Define the criteria

Based on the patterns, define the MQL criteria as a clear, binary rule set.

MQL definition template:

A contact becomes an MQL when:

1. FIT GATE (must pass):
   - Company size: [range] employees
   - Industry: [list of qualifying industries]
   - Geography: [qualifying regions]
   - Seniority: [minimum level]
   - NOT a competitor, student, consultant, or job seeker

AND

2. BEHAVIOR TRIGGER (any one of):
   - Submitted a demo request form
   - Submitted a contact sales form
   - Visited pricing page 2+ times in 7 days
   - Lead score reached [threshold] points

OR

3. INSTANT MQL OVERRIDE:
   - Submitted demo request AND company size ≥ [minimum]
   (Bypasses cumulative behavior score. Fit gate still applies)

Step 4: Get sales sign-off

Present the definition to sales leadership (VP Sales, SDR Manager) with the supporting data.

What to share:

The closed-won analysis showing which leads convert
The proposed MQL criteria
The estimated volume: "Based on last quarter's data, this definition would produce ~[X] MQLs per month"
The expected acceptance rate: "Based on historical data, we expect [30-50]% of these MQLs to be accepted by SDRs"

Sign-off rules:

Both sides must agree in writing (a shared doc, not a verbal agreement)
Include the SLA: marketing commits to delivering MQLs meeting this definition. Sales commits to following up within [X minutes/hours]
Set a review date: "We'll revisit this definition at the end of [quarter]"

MQL Trigger Types

Trigger hierarchy

Not all MQL triggers are equal. Rank triggers by how strongly they predict buying intent.

Tier	Trigger	Intent signal strength	Example
Tier 1 (Instant MQL)	Direct sales request	Very high. The prospect explicitly asked for a sales conversation	Demo request, contact sales, pricing inquiry
Tier 2 (Strong signal)	High-intent page engagement	High. The prospect is actively evaluating	2+ pricing page visits, comparison page visit, case study + pricing page in same session
Tier 3 (Cumulative)	Score threshold reached	Medium. Accumulated engagement signals suggest readiness	Lead score ≥ 50 from multiple content interactions over time
Tier 4 (Assisted)	Sales-requested MQL	Variable. Sales saw something marketing didn't	SDR asks marketing to qualify a specific contact they found through outbound or referral

Trigger rules

Tier 1 triggers bypass cumulative scoring. A demo request from a fit lead is an MQL regardless of how many blog posts they read. Don't make them earn additional points
Tier 2 triggers should have a recency requirement. Pricing page visits from 6 months ago are stale. Require Tier 2 actions within the last 14-30 days
Tier 3 (score threshold) is the catch-all. It captures leads who engage heavily but never take a Tier 1 or 2 action. The threshold must be calibrated from closed-won data
Tier 4 is manual. Sales sometimes has context marketing doesn't (a referral, a LinkedIn conversation, a signal not tracked in marketing automation). Allow manual MQL creation but require the same fit gate

MQL vs Not-MQL: Common Edge Cases

Scenario	MQL?	Why
CEO of a 200-person SaaS company downloads an ebook	Not yet	Fit is great. Behavior is one low-intent action. Nurture until a higher-intent signal
Marketing intern at a 500-person company requests a demo	No	Fails the seniority fit gate. Alert marketing to assess if they represent a buying committee
VP Sales at a 10-person agency requests a demo	Depends	Passes seniority but may fail company size fit. If agencies are in your ICP, yes. If not, no
Director of Sales at a competitor company requests a demo	No	Competitor. Automatic disqualification. Route to marketing leadership for review
Anonymous visitor views pricing page 5 times, then identifies via chatbot	Yes (if fit passes)	High-intent behavior. Once identified, score fit. If fit passes, instant MQL
Contact from existing customer company fills demo form	No (not MQL)	Route to account owner. This is an expansion or cross-sell signal, not a new MQL
Lead submitted demo request 90 days ago, no response, re-submits today	Yes	Fresh intent. Re-create MQL. The 90-day gap means this is a new buying cycle
Lead has high score from 200 blog visits but no high-intent actions	Review	Might be a researcher, journalist, or competitor. High volume + low intent = suspicious. Manual review

The MQL SLA

The MQL definition is only useful if there's an SLA governing what happens after MQL creation.

Marketing SLA (to sales)

Commitment	Standard
MQL quality	≥ 70% of MQLs meet the documented fit criteria
MQL volume	Marketing commits to delivering [X] MQLs per month/quarter
Data completeness	Every MQL has: email, name, company, title, lead source, MQL trigger reason
Notification	Sales is notified within 30 seconds of MQL creation (Slack, push, email)

Sales SLA (to marketing)

Commitment	Standard
Response time	First outreach within [X] minutes of MQL notification
Acceptance/rejection	SDR accepts or rejects the MQL within 48 hours with a reason
Follow-up sequence	Accepted MQLs receive minimum 3-touch follow-up within 7 days
Feedback	Sales provides rejection reasons that marketing can use to calibrate

SLA response time by lead type

MQL trigger	Response SLA	Why
Demo request	< 5 minutes	Highest intent. Prospect is actively evaluating. Speed = competitive advantage
Contact sales	< 5 minutes	Same as demo request
Pricing page conversion	< 15 minutes	High intent but may not expect an immediate call
Score threshold MQL	< 4 hours	Lower urgency. Prospect didn't explicitly ask for contact
Content-triggered MQL	< 24 hours (business day)	Lowest urgency. Prospect may not expect outreach

MQL Acceptance and Rejection

Acceptance criteria

An SDR accepts an MQL when:

The contact is reachable (valid email, responds to outreach, or answers phone)
The contact confirms a relevant need or interest
The contact's company matches ICP on manual review

Rejection reasons (standardize these)

Rejection reason	Definition	What marketing should do
Bad fit (company)	Company doesn't match ICP (too small, wrong industry, wrong geo)	Review fit scoring. If recurring, tighten the fit gate
Bad fit (person)	Wrong role, too junior, no buying authority	Review seniority/title criteria. If recurring, add title filters
Not interested	Reached the contact, they have no interest or need	Review behavior scoring. These leads may have inflated scores from non-buying activity
Unreachable	Can't contact after 3+ attempts across channels	Check data quality. Verify emails before MQL creation
Duplicate	Already in an active deal or previously disqualified	Improve dedup logic in routing workflow
Competitor / student / job seeker	Not a real prospect	Add to disqualification list. Strengthen negative scoring
Already a customer	Contact is at an existing customer company	Improve account-match routing. Route to CSM, not SDR
Timing	Right fit but explicitly not buying now	Recycle to nurture. Re-engage in 30-90 days

Acceptance rate targets

MQL acceptance rate	What it means	Action
> 60%	Threshold may be too high. You might be leaving pipeline on the table	Lower the threshold slightly. Test with 10% lower score requirement
40-60%	Healthy. Good alignment between marketing scoring and sales expectations	Maintain. Calibrate quarterly
30-40%	Acceptable. Some misalignment. Identify top rejection reasons	Fix the top 2 rejection reasons. Usually a fit or scoring issue
< 30%	Broken. Sales doesn't trust MQLs. Follow-up quality degrades	Urgent fix. Raise the threshold. Tighten fit criteria. Review the closed-won data

Calibration Process

Quarterly MQL review

Step	Action	Data source
1	Pull MQL acceptance rate for the quarter	CRM (accepted vs rejected MQLs)
2	Pull top 5 rejection reasons	CRM (rejection reason field on MQL records)
3	Pull MQL-to-opportunity conversion rate	CRM (opportunities created from MQLs)
4	Pull false negatives (closed-won deals that were never MQLs)	CRM (cross-reference closed-won contacts with MQL history)
5	Compare current ICP to closed-won firmographics	Has the customer profile shifted?
6	Adjust criteria based on findings	Update scoring, threshold, or fit gate
7	Present changes to sales. Get re-sign-off	Shared doc update

Calibration rules

If acceptance rate < 30%: The top rejection reason tells you what to fix. If "bad fit (company)" is #1, tighten the company size or industry gate. If "not interested" is #1, raise the behavior threshold
If acceptance rate > 60%: You may be too conservative. Test lowering the threshold by 10%. Track whether the incremental MQLs convert at an acceptable rate
If false negatives > 10%: Deals are closing without marketing ever MQLing the contact. Either the scoring model misses key behaviors, or outbound-sourced deals aren't being tracked through the MQL path. Investigate both

MQL Definition by Company Stage

Stage	MQL definition approach	Typical volume
Pre-$1M ARR	No formal MQL. Every inbound demo request goes directly to the founder	5-20/month
$1-3M ARR	Simple definition: demo request + basic fit check (company size ≥ X, not a competitor)	20-50/month
$3-10M ARR	Full definition: fit scoring + behavior scoring + MQL threshold + SLA	50-200/month
$10-30M ARR	Sophisticated: segment-specific MQL definitions (enterprise vs mid-market vs SMB may have different criteria)	200-1000/month
$30M+ ARR	Advanced: ML-assisted scoring, predictive lead models, multiple MQL definitions by motion (inbound, PLG, ABM)	1000+/month

Measurement

Metric	Definition	Target	Frequency
MQL volume	Total MQLs created per period	Trending with growth targets	Weekly
MQL acceptance rate	Accepted / total MQLs	30-50%	Weekly
MQL-to-SQL rate	SQLs / accepted MQLs	50-70%	Monthly
MQL-to-Opportunity rate	Opps / total MQLs	15-25%	Monthly
MQL-to-Closed Won rate	Closed-won / total MQLs	3-8%	Quarterly
MQL velocity	Average days from lead creation to MQL	Tracking trend (lower = better)	Monthly
Response time compliance	% of MQLs responded to within SLA	> 85%	Weekly
False negative rate	Closed-won deals never MQL'd / total closed-won	< 10%	Quarterly

Anti-Pattern Check

MQL definition is "marketing thinks they're ready." Not objective. Not measurable. Not reproducible. Define specific criteria that a machine can evaluate
Marketing and sales use different MQL definitions. Marketing counts form fills. Sales expects demo requests only. Align on one written, shared definition with both teams' sign-off
No fit gate. Leads MQL purely on behavior (downloaded 5 ebooks = MQL) regardless of company size, industry, or role. A student downloading every resource is not an MQL. Fit is a prerequisite
MQL threshold hasn't changed in 12 months. Your ICP has evolved. Your content has changed. Your traffic sources have shifted. Calibrate quarterly
No rejection reason tracking. SDRs reject MQLs but don't say why. Without structured rejection data, marketing can't improve the definition. Make rejection reason a required field
Demo requests require additional scoring before MQL. A prospect who explicitly asks for a demo has declared intent. Don't make them also reach a cumulative score from blog visits. Demo request + fit = instant MQL
MQL SLA exists but isn't enforced. A response time SLA without escalation when it's missed is a suggestion, not an SLA. Automate escalation at the SLA deadline
Treating all MQLs the same. A demo request MQL and a content-download MQL have very different intent levels. At minimum, flag the MQL trigger type so the SDR knows what action the prospect took

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# MQL Definition Framework

## What Makes a Good MQL Definition

### The 4 requirements

| Requirement | What it means | Test |
|-------------|-------------|------|
| Objective | Based on measurable criteria, not subjective judgment | Can a machine apply the definition with no human input? |
| Data-derived | Built from analysis of closed-won deals, not from sales team opinions | Can you show the data that supports each criterion? |
| Agreed-upon | Marketing AND sales signed off on the same definition | Do both teams reference the same document? |
| Calibrated | Regularly adjusted based on MQL acceptance rate and conversion data | When was the last time you reviewed and adjusted the criteria? |

**If any requirement fails, the MQL definition is broken.** An objective definition that sales didn't agree to gets ignored. A definition both teams agreed to but never calibrated drifts out of alignment with reality.

---

## Building the MQL Definition

### Step 1: Analyze closed-won deals

Pull the last 50-100 closed-won deals. For each, look at the contact who was the primary MQL (or the first marketing-sourced contact).

**Data to capture per deal:**

| Data point | Where to find it | What it tells you |
|-----------|-----------------|-------------------|
| Company size at time of MQL | Enrichment data or CRM | Which company sizes actually buy |
| Industry | Enrichment data or CRM | Which verticals convert |
| Contact title/seniority | CRM | Which personas buy |
| Lead source | CRM | Which channels produce buyers |
| First conversion action | Marketing automation | What the buyer did first |
| MQL trigger action | Marketing automation | What action pushed them to MQL |
| Days from lead creation to MQL | CRM timestamps | How long the typical journey takes |
| Days from MQL to opportunity | CRM timestamps | How quickly MQLs convert to pipeline |

### Step 2: Identify patterns

From the closed-won data, answer:

1. **What do MQLs who become customers look like?** (firmographic profile)
2. **What did they do before becoming an MQL?** (behavioral pattern)
3. **What was the trigger that made them sales-ready?** (MQL trigger)

**Common patterns in B2B SaaS:**

| Pattern | What it looks like | Implication for MQL definition |
|---------|-------------------|-------------------------------|
| Most closed-won contacts are Directors+ at 50-500 employee SaaS companies | Your ICP is narrow. Fit criteria should be strict | High fit threshold. Low-fit leads should never MQL |
| 70% of closed-won contacts submitted a demo request as their MQL trigger | Demo request is the primary buying signal | Demo request should be an instant MQL trigger (if fit passes) |
| Contacts who viewed the pricing page 2+ times close at 3x the rate of those who didn't | Pricing page visits are a strong buying signal | Add pricing page visits as a high-weight behavior trigger |
| Average time from lead to MQL is 45 days | Your nurture cycle is long | Don't expect instant MQLs from content downloads. Budget for nurture |
| Leads from organic search convert 2x better than paid social | Not all sources are equal | Consider source-weighted scoring or source-specific MQL thresholds |

### Step 3: Define the criteria

Based on the patterns, define the MQL criteria as a clear, binary rule set.

**MQL definition template:**

```
A contact becomes an MQL when:

1. FIT GATE (must pass):
   - Company size: [range] employees
   - Industry: [list of qualifying industries]
   - Geography: [qualifying regions]
   - Seniority: [minimum level]
   - NOT a competitor, student, consultant, or job seeker

AND

2. BEHAVIOR TRIGGER (any one of):
   - Submitted a demo request form
   - Submitted a contact sales form
   - Visited pricing page 2+ times in 7 days
   - Lead score reached [threshold] points

3. INSTANT MQL OVERRIDE:
   - Submitted demo request AND company size ≥ [minimum]
   (Bypasses cumulative behavior score. Fit gate still applies)
```

### Step 4: Get sales sign-off

Present the definition to sales leadership (VP Sales, SDR Manager) with the supporting data.

**What to share:**
- The closed-won analysis showing which leads convert
- The proposed MQL criteria
- The estimated volume: "Based on last quarter's data, this definition would produce ~[X] MQLs per month"
- The expected acceptance rate: "Based on historical data, we expect [30-50]% of these MQLs to be accepted by SDRs"

**Sign-off rules:**
- Both sides must agree in writing (a shared doc, not a verbal agreement)
- Include the SLA: marketing commits to delivering MQLs meeting this definition. Sales commits to following up within [X minutes/hours]
- Set a review date: "We'll revisit this definition at the end of [quarter]"

---

## MQL Trigger Types

### Trigger hierarchy

Not all MQL triggers are equal. Rank triggers by how strongly they predict buying intent.

| Tier | Trigger | Intent signal strength | Example |
|------|---------|----------------------|---------|
| Tier 1 (Instant MQL) | Direct sales request | Very high. The prospect explicitly asked for a sales conversation | Demo request, contact sales, pricing inquiry |
| Tier 2 (Strong signal) | High-intent page engagement | High. The prospect is actively evaluating | 2+ pricing page visits, comparison page visit, case study + pricing page in same session |
| Tier 3 (Cumulative) | Score threshold reached | Medium. Accumulated engagement signals suggest readiness | Lead score ≥ 50 from multiple content interactions over time |
| Tier 4 (Assisted) | Sales-requested MQL | Variable. Sales saw something marketing didn't | SDR asks marketing to qualify a specific contact they found through outbound or referral |

### Trigger rules

- **Tier 1 triggers bypass cumulative scoring.** A demo request from a fit lead is an MQL regardless of how many blog posts they read. Don't make them earn additional points
- **Tier 2 triggers should have a recency requirement.** Pricing page visits from 6 months ago are stale. Require Tier 2 actions within the last 14-30 days
- **Tier 3 (score threshold) is the catch-all.** It captures leads who engage heavily but never take a Tier 1 or 2 action. The threshold must be calibrated from closed-won data
- **Tier 4 is manual.** Sales sometimes has context marketing doesn't (a referral, a LinkedIn conversation, a signal not tracked in marketing automation). Allow manual MQL creation but require the same fit gate

---

## MQL vs Not-MQL: Common Edge Cases

| Scenario | MQL? | Why |
|----------|------|-----|
| CEO of a 200-person SaaS company downloads an ebook | Not yet | Fit is great. Behavior is one low-intent action. Nurture until a higher-intent signal |
| Marketing intern at a 500-person company requests a demo | No | Fails the seniority fit gate. Alert marketing to assess if they represent a buying committee |
| VP Sales at a 10-person agency requests a demo | Depends | Passes seniority but may fail company size fit. If agencies are in your ICP, yes. If not, no |
| Director of Sales at a competitor company requests a demo | No | Competitor. Automatic disqualification. Route to marketing leadership for review |
| Anonymous visitor views pricing page 5 times, then identifies via chatbot | Yes (if fit passes) | High-intent behavior. Once identified, score fit. If fit passes, instant MQL |
| Contact from existing customer company fills demo form | No (not MQL) | Route to account owner. This is an expansion or cross-sell signal, not a new MQL |
| Lead submitted demo request 90 days ago, no response, re-submits today | Yes | Fresh intent. Re-create MQL. The 90-day gap means this is a new buying cycle |
| Lead has high score from 200 blog visits but no high-intent actions | Review | Might be a researcher, journalist, or competitor. High volume + low intent = suspicious. Manual review |

---

## The MQL SLA

The MQL definition is only useful if there's an SLA governing what happens after MQL creation.

### Marketing SLA (to sales)

| Commitment | Standard |
|-----------|----------|
| MQL quality | ≥ 70% of MQLs meet the documented fit criteria |
| MQL volume | Marketing commits to delivering [X] MQLs per month/quarter |
| Data completeness | Every MQL has: email, name, company, title, lead source, MQL trigger reason |
| Notification | Sales is notified within 30 seconds of MQL creation (Slack, push, email) |

### Sales SLA (to marketing)

| Commitment | Standard |
|-----------|----------|
| Response time | First outreach within [X] minutes of MQL notification |
| Acceptance/rejection | SDR accepts or rejects the MQL within 48 hours with a reason |
| Follow-up sequence | Accepted MQLs receive minimum 3-touch follow-up within 7 days |
| Feedback | Sales provides rejection reasons that marketing can use to calibrate |

### SLA response time by lead type

| MQL trigger | Response SLA | Why |
|------------|-------------|-----|
| Demo request | < 5 minutes | Highest intent. Prospect is actively evaluating. Speed = competitive advantage |
| Contact sales | < 5 minutes | Same as demo request |
| Pricing page conversion | < 15 minutes | High intent but may not expect an immediate call |
| Score threshold MQL | < 4 hours | Lower urgency. Prospect didn't explicitly ask for contact |
| Content-triggered MQL | < 24 hours (business day) | Lowest urgency. Prospect may not expect outreach |

---

## MQL Acceptance and Rejection

### Acceptance criteria

An SDR accepts an MQL when:
- The contact is reachable (valid email, responds to outreach, or answers phone)
- The contact confirms a relevant need or interest
- The contact's company matches ICP on manual review

### Rejection reasons (standardize these)

| Rejection reason | Definition | What marketing should do |
|-----------------|-----------|------------------------|
| Bad fit (company) | Company doesn't match ICP (too small, wrong industry, wrong geo) | Review fit scoring. If recurring, tighten the fit gate |
| Bad fit (person) | Wrong role, too junior, no buying authority | Review seniority/title criteria. If recurring, add title filters |
| Not interested | Reached the contact, they have no interest or need | Review behavior scoring. These leads may have inflated scores from non-buying activity |
| Unreachable | Can't contact after 3+ attempts across channels | Check data quality. Verify emails before MQL creation |
| Duplicate | Already in an active deal or previously disqualified | Improve dedup logic in routing workflow |
| Competitor / student / job seeker | Not a real prospect | Add to disqualification list. Strengthen negative scoring |
| Already a customer | Contact is at an existing customer company | Improve account-match routing. Route to CSM, not SDR |
| Timing | Right fit but explicitly not buying now | Recycle to nurture. Re-engage in 30-90 days |

### Acceptance rate targets

| MQL acceptance rate | What it means | Action |
|-------------------|---------------|--------|
| > 60% | Threshold may be too high. You might be leaving pipeline on the table | Lower the threshold slightly. Test with 10% lower score requirement |
| 40-60% | Healthy. Good alignment between marketing scoring and sales expectations | Maintain. Calibrate quarterly |
| 30-40% | Acceptable. Some misalignment. Identify top rejection reasons | Fix the top 2 rejection reasons. Usually a fit or scoring issue |
| < 30% | Broken. Sales doesn't trust MQLs. Follow-up quality degrades | Urgent fix. Raise the threshold. Tighten fit criteria. Review the closed-won data |

---

## Calibration Process

### Quarterly MQL review

| Step | Action | Data source |
|------|--------|-------------|
| 1 | Pull MQL acceptance rate for the quarter | CRM (accepted vs rejected MQLs) |
| 2 | Pull top 5 rejection reasons | CRM (rejection reason field on MQL records) |
| 3 | Pull MQL-to-opportunity conversion rate | CRM (opportunities created from MQLs) |
| 4 | Pull false negatives (closed-won deals that were never MQLs) | CRM (cross-reference closed-won contacts with MQL history) |
| 5 | Compare current ICP to closed-won firmographics | Has the customer profile shifted? |
| 6 | Adjust criteria based on findings | Update scoring, threshold, or fit gate |
| 7 | Present changes to sales. Get re-sign-off | Shared doc update |

### Calibration rules

- **If acceptance rate < 30%:** The top rejection reason tells you what to fix. If "bad fit (company)" is #1, tighten the company size or industry gate. If "not interested" is #1, raise the behavior threshold
- **If acceptance rate > 60%:** You may be too conservative. Test lowering the threshold by 10%. Track whether the incremental MQLs convert at an acceptable rate
- **If false negatives > 10%:** Deals are closing without marketing ever MQLing the contact. Either the scoring model misses key behaviors, or outbound-sourced deals aren't being tracked through the MQL path. Investigate both

---

## MQL Definition by Company Stage

| Stage | MQL definition approach | Typical volume |
|-------|------------------------|---------------|
| Pre-$1M ARR | No formal MQL. Every inbound demo request goes directly to the founder | 5-20/month |
| $1-3M ARR | Simple definition: demo request + basic fit check (company size ≥ X, not a competitor) | 20-50/month |
| $3-10M ARR | Full definition: fit scoring + behavior scoring + MQL threshold + SLA | 50-200/month |
| $10-30M ARR | Sophisticated: segment-specific MQL definitions (enterprise vs mid-market vs SMB may have different criteria) | 200-1000/month |
| $30M+ ARR | Advanced: ML-assisted scoring, predictive lead models, multiple MQL definitions by motion (inbound, PLG, ABM) | 1000+/month |

---

## Measurement

| Metric | Definition | Target | Frequency |
|--------|-----------|--------|-----------|
| MQL volume | Total MQLs created per period | Trending with growth targets | Weekly |
| MQL acceptance rate | Accepted / total MQLs | 30-50% | Weekly |
| MQL-to-SQL rate | SQLs / accepted MQLs | 50-70% | Monthly |
| MQL-to-Opportunity rate | Opps / total MQLs | 15-25% | Monthly |
| MQL-to-Closed Won rate | Closed-won / total MQLs | 3-8% | Quarterly |
| MQL velocity | Average days from lead creation to MQL | Tracking trend (lower = better) | Monthly |
| Response time compliance | % of MQLs responded to within SLA | > 85% | Weekly |
| False negative rate | Closed-won deals never MQL'd / total closed-won | < 10% | Quarterly |

---

## Anti-Pattern Check

- MQL definition is "marketing thinks they're ready." Not objective. Not measurable. Not reproducible. Define specific criteria that a machine can evaluate
- Marketing and sales use different MQL definitions. Marketing counts form fills. Sales expects demo requests only. Align on one written, shared definition with both teams' sign-off
- No fit gate. Leads MQL purely on behavior (downloaded 5 ebooks = MQL) regardless of company size, industry, or role. A student downloading every resource is not an MQL. Fit is a prerequisite
- MQL threshold hasn't changed in 12 months. Your ICP has evolved. Your content has changed. Your traffic sources have shifted. Calibrate quarterly
- No rejection reason tracking. SDRs reject MQLs but don't say why. Without structured rejection data, marketing can't improve the definition. Make rejection reason a required field
- Demo requests require additional scoring before MQL. A prospect who explicitly asks for a demo has declared intent. Don't make them also reach a cumulative score from blog visits. Demo request + fit = instant MQL
- MQL SLA exists but isn't enforced. A response time SLA without escalation when it's missed is a suggestion, not an SLA. Automate escalation at the SLA deadline
- Treating all MQLs the same. A demo request MQL and a content-download MQL have very different intent levels. At minimum, flag the MQL trigger type so the SDR knows what action the prospect took