PLG Metrics That Matter: From MQL to PQL
PLG Metrics That Matter: From MQL to PQL
Product-led growth (PLG) fundamentally changes how we measure success. Traditional sales-led metrics like MQL (Marketing Qualified Lead) lose relevance when the product itself generates, qualifies, and converts users. You need a new measurement framework built around the product.
Why Traditional Metrics Do Not Work for PLG
MQL vs PQL - The Fundamental Difference
MQL (Marketing Qualified Lead):
- Downloaded whitepaper or e-book
- Visited pricing page
- Filled out website form
- Conversion rate to paying customer: 1-3%
PQL (Product Qualified Lead):
- Achieved activation in product
- Uses key features
- Demonstrates buying intent in product
- Conversion rate to paying customer: 15-30%
Data from OpenView shows that PQLs have 5-10x higher conversion rate than traditional MQLs. Why? Because PQLs have already experienced product value - it is not just theoretical interest.
How to Define PQL for Your Product
PQL Definition Components
Every PQL definition should contain three elements:
1. Product Usage Threshold What must the user do in the product?
| Product | Usage Threshold |
|---|---|
| Slack | Sent 2000+ messages in team |
| Dropbox | Uploaded file to shared folder |
| Zoom | Hosted meeting with 3+ participants |
| Figma | Invited colleague to project |
2. Engagement Level How consistently do they use the product?
| Level | Definition |
|---|---|
| Low | 1-2 sessions per month |
| Medium | Weekly active |
| High | Daily active |
3. Fit Criteria Does the user match your ICP?
- Company size (employees, revenue)
- Industry
- Use case
- Role/seniority
Practical Example: PQL Definition for B2B SaaS
Weak PQL: User registered and logged in at least 2x.
Strong PQL: User who:
- Completed onboarding (5 steps)
- Invited at least 1 team member
- Created at least 3 projects
- Is from company with 50+ employees
- Has manager role or higher
PLG Metrics Stack
Tier 1: Acquisition Metrics
| Metric | Definition | Benchmark |
|---|---|---|
| Sign-ups | New registrations | Growth rate >10% MoM |
| Organic ratio | % sign-ups from organic | >50% |
| CAC | Cost per acquisition | <12 months LTV |
| Sign-up to activation | % who reach activation | >25% |
| Channel efficiency | CAC by channel | Varies by channel |
Pro tip: In PLG, organic sign-up share should be significantly higher than in sales-led. If <40%, you have a problem with product-market fit or viral coefficient.
Tier 2: Activation Metrics
| Metric | Definition | Benchmark |
|---|---|---|
| Time to Value (TTV) | Time to first value | <5 minutes ideally |
| Activation rate | % users reaching activation | >40% |
| Aha moment completion | % experiencing aha moment | >60% |
| Onboarding completion | % completing onboarding | >70% |
| Setup time | Time to full setup | <24h |
Aha moment is a critical concept. It is the moment when the user first understands product value. Examples:
- Slack: First conversation with colleague
- Dropbox: Accessing file from another device
- Canva: Exporting first design
Tier 3: Engagement Metrics
| Metric | Definition | Benchmark |
|---|---|---|
| DAU/MAU ratio | Stickiness | >20% |
| Feature adoption | % of features used | >40% core features |
| Session frequency | Sessions per week | >3 |
| Session depth | Actions per session | >5 |
| Time in app | Average time in app | Depends on product |
DAU/MAU ratio (also called stickiness) is a key indicator:
- <10%: Engagement problem
- 10-20%: Average
- 20-50%: Good
-
50%: Excellent (typically communication tools)
Tier 4: Monetization Metrics
| Metric | Definition | Benchmark |
|---|---|---|
| Free to paid conversion | % free users who pay | 2-5% freemium, 10-25% trial |
| PQL to customer rate | % PQLs who convert | 15-30% |
| Time to conversion | Days from sign-up to payment | <30 days ideally |
| ARPU | Average Revenue Per User | Depends on pricing |
| Expansion revenue | % revenue from expansion | >30% |
Tier 5: Retention Metrics
| Metric | Definition | Benchmark |
|---|---|---|
| Logo retention | % customers who stay | >85% annually |
| Revenue retention (NRR) | Net Revenue Retention | >100%, ideally >120% |
| Engagement retention | % active users | Week 4 retention >40% |
| Feature retention | Continuous feature usage | >60% |
| Cohort curves | Retention by cohort | Flattening curve |
North Star Metric for PLG
North Star Metric is the single metric that best captures the value the product delivers to customers. In PLG model it should:
- Measure value for customer (not just revenue)
- Be a leading indicator of growth
- Be influenceable by the entire team
North Star Metric Examples:
| Company | North Star Metric |
|---|---|
| Slack | Daily Active Users sending messages |
| Airbnb | Nights booked |
| Spotify | Time spent listening |
| HubSpot | Weekly Active Teams |
| Figma | Weekly Active Editors |
Building a PLG Dashboard
Executive Dashboard (Weekly Review)
Key Metrics:
- North Star Metric (trend + WoW change)
- New sign-ups
- Activation rate
- PQL count
- Free to paid conversion
- NRR
Product Team Dashboard (Daily)
Acquisition:
- Sign-ups by channel
- Landing page conversion
Activation:
- Onboarding funnel
- TTV distribution
- Activation rate by segment
Engagement:
- DAU/WAU/MAU
- Feature usage heat map
- Session analytics
Growth Team Dashboard
Conversion:
- PQL pipeline
- Conversion funnel
- Trial analytics
Expansion:
- Usage patterns
- Upgrade triggers
- Seat expansion opportunities
Common Mistakes in PLG Metrics
1. Tracking vanity metrics
Sign-ups without activation context are meaningless. Always pair with quality.
2. Ignoring segmentation
SMB and Enterprise have dramatically different benchmarks. Segment metrics.
3. Too many metrics
Start with 5-7 key ones. Add gradually.
4. Static PQL definitions
PQL definition should evolve with product. Revalidate quarterly.
5. Missing cohort analysis
Aggregate metrics mask problems. Always analyze by cohorts.
Implementation: From Zero to PLG Metrics
Phase 1: Foundation (Week 1-2)
- Implement product analytics (Amplitude, Mixpanel)
- Define key events
- Set up basic tracking
Phase 2: Activation (Week 3-4)
- Define activation
- Map aha moment
- Set up activation funnel
Phase 3: PQL (Week 5-6)
- Analyze past conversions
- Define PQL criteria
- Implement PQL scoring
Phase 4: Dashboard (Week 7-8)
- Create executive dashboard
- Set up alerting
- Start weekly reviews
Case Study: Transformation to PLG Metrics
Situation: B2B SaaS transitioning from sales-led to PLG
Before (Sales-led metrics):
- MQL count
- SQL count
- Pipeline value
- Win rate
After (PLG metrics):
- Sign-ups (weekly)
- Activation rate
- PQL count and conversion
- Self-serve revenue %
- NRR
Results after 6 months:
- Sales efficiency: +40%
- CAC: -35%
- Time to close: -50%
- Self-serve revenue: 0% -> 25%
Conclusion
Transitioning from MQL to PQL is not just a metric change - it is a mindset change. In PLG world, product qualifies leads better than any sales process. Key to success:
- Clearly define your aha moment
- Create robust PQL definition
- Measure entire funnel from sign-up to expansion
- Iterate based on data
