Product-Led Growth
PLG Motion Design
PLG means the product itself drives acquisition, conversion, and expansion — not a sales team.
Three PLG Motions
| Motion |
How it works |
Example |
| Free-to-paid |
Free tier → hit limit → upgrade |
Notion, Figma |
| Free trial |
Full access for X days → pay or lose access |
Loom, Superhuman |
| Usage-based |
Start free/cheap → pay as you scale |
Stripe, Twilio |
PLG vs Sales-Led Growth (SLG)
|
PLG |
SLG |
| First touch |
Product (free/trial) |
Sales call |
| ACV |
$0–$10k self-serve |
$25k+ enterprise |
| CAC |
Low |
High |
| Sales cycle |
Days |
Months |
| Revenue predictability |
Lower initially |
Higher |
| Best for |
Bottoms-up, individual users |
Top-down, complex needs |
Self-Serve Onboarding Optimization
Time-to-Value Framework
Goal: get the user to the aha moment as fast as possible.
Signup → Setup → First action → Aha moment → Habit loop
| | | | |
<2 min <5 min <10 min Day 1–3 Day 7+
Onboarding Principles
- Remove every step that doesn't move toward aha moment
- Default to working state (pre-populate templates, sample data)
- Progressive disclosure: show basic path first, advanced options later
- Contextual tooltips > long walkthroughs
- Celebrate first milestone (empty state → first success)
Activation Funnel Metrics
| Step |
Target conversion |
| Signup → Completed profile |
> 80% |
| Completed profile → Core action |
> 60% |
| Core action → Aha moment |
> 50% |
| Aha moment → Return Day 3 |
> 40% |
Aha Moment Definition and Measurement
Finding the Aha Moment
- Pull D30 retained vs churned cohorts
- Compare feature usage in first session
- Find the action that predicts retention with statistical significance
- Validate: do users who take action X retain at 2× rate?
Aha Moment Examples
| Product |
Aha moment |
| Slack |
Team sent 2,000 messages |
| Dropbox |
Uploaded 1 file across 2 devices |
| Figma |
Shared a design with a collaborator |
| HubSpot |
Created 1 contact + sent 1 email |
| Loom |
Sent a Loom that was watched |
Measuring Time-to-Aha
- Track: median minutes/hours from signup to aha event
- Segment by: acquisition channel, plan, device
- Goal: reduce by 20% each quarter
Product Qualified Leads (PQLs)
A PQL is a free/trial user who has hit usage signals indicating sales-readiness.
PQL Criteria Framework
Define PQL when user meets threshold on:
- Usage depth: Used X features or completed Y actions
- Usage frequency: Active on Z of last 7 days
- Team signal: Invited ≥ 2 teammates
- Upgrade signal: Hit plan limit, visited pricing page ≥ 2×
- Intent signal: Requested demo, contacted support about enterprise
PQL Score Example
| Signal |
Points |
| Invited 3+ teammates |
30 |
| Used core feature 5+ times |
20 |
| Active 5+ days in last 7 |
20 |
| Visited pricing page |
15 |
| Exported data |
10 |
| Hit usage limit |
15 |
PQL threshold: score ≥ 70 → route to sales
Expansion Revenue
Expansion Triggers
- User hits plan limit (seats, storage, API calls)
- Team grows (more seats needed)
- Usage spikes (seasonal, growth event)
- New use case discovered in product
- Upgrade path surfaced in-product
Net Revenue Retention (NRR) Formula
NRR = (Starting MRR + Expansion - Contraction - Churn) / Starting MRR × 100%
- NRR > 100%: expansion outpaces churn (grows without new customers)
- NRR > 120%: elite PLG benchmark
- NRR < 100%: churn exceeds expansion — unsustainable
In-Product Expansion Triggers
- Show usage meter approaching limit (80%, 95%, 100%)
- Unlock preview of paid features at the right moment
- Team member limit reached → prompt to upgrade for the whole team
- Export/share blocked → upgrade CTA with clear value prop
PLG Health Metrics Dashboard
| Metric |
Target |
Frequency |
| Free → paid conversion |
3–8% (SaaS avg) |
Weekly |
| Time to first value |
< 5 min |
Weekly |
| D7 activation rate |
> 40% |
Weekly |
| PQL volume |
Growing MoM |
Weekly |
| PQL → paid conversion |
20–40% |
Monthly |
| NRR |
> 110% |
Monthly |
| Viral coefficient (K-factor) |
> 0.5 |
Monthly |
Viral Coefficient (K-Factor)
K = i × c
i = average invites sent per user
c = conversion rate of invitees
Example: average user invites 3 people, 20% convert
K = 3 × 0.20 = 0.6
K > 1: viral growth (each user brings >1 new user)
K = 0.5–1: meaningful organic boost
K < 0.5: word of mouth exists but not compounding
Improving K-Factor
- Add team-based features (collaboration requires invites)
- Share-by-default outputs (exported files link back to product)
- Referral incentive (both parties rewarded)
- Network effects that make product better with more users
Freemium Conversion Optimization
Conversion Levers
- Limit selection: Limit the right thing (seats, not core value)
- Upgrade moment: Surface upgrade at point of pain, not randomly
- Trial element: Time-limited access to paid features
- Social proof: Show what paying customers achieve
- ROI calculator: Make value concrete before asking for payment
Common Freemium Mistakes
- Limiting so aggressively free users get no value (never convert)
- Giving away too much (no reason to upgrade)
- Upgrade CTA buried in settings (not in context of use)
- One-size upgrade path (no monthly option for price-sensitive users)
1---2name: product-led-growth3description: When to activate: PLG, product-led growth, self-serve, freemium, PQL, product qualified lead, bottoms-up GTM, expansion revenue, free trial, viral coefficient4---56# Product-Led Growth78## PLG Motion Design910PLG means the product itself drives acquisition, conversion, and expansion — not a sales team.1112### Three PLG Motions13| Motion | How it works | Example |14|--------|-------------|---------|15| **Free-to-paid** | Free tier → hit limit → upgrade | Notion, Figma |16| **Free trial** | Full access for X days → pay or lose access | Loom, Superhuman |17| **Usage-based** | Start free/cheap → pay as you scale | Stripe, Twilio |1819### PLG vs Sales-Led Growth (SLG)20| | PLG | SLG |21|-|-----|-----|22| First touch | Product (free/trial) | Sales call |23| ACV | $0–$10k self-serve | $25k+ enterprise |24| CAC | Low | High |25| Sales cycle | Days | Months |26| Revenue predictability | Lower initially | Higher |27| Best for | Bottoms-up, individual users | Top-down, complex needs |2829## Self-Serve Onboarding Optimization3031### Time-to-Value Framework32Goal: get the user to the aha moment as fast as possible.3334```35Signup → Setup → First action → Aha moment → Habit loop36 | | | | |37 <2 min <5 min <10 min Day 1–3 Day 7+38```3940### Onboarding Principles41- Remove every step that doesn't move toward aha moment42- Default to working state (pre-populate templates, sample data)43- Progressive disclosure: show basic path first, advanced options later44- Contextual tooltips > long walkthroughs45- Celebrate first milestone (empty state → first success)4647### Activation Funnel Metrics48| Step | Target conversion |49|------|------------------|50| Signup → Completed profile | > 80% |51| Completed profile → Core action | > 60% |52| Core action → Aha moment | > 50% |53| Aha moment → Return Day 3 | > 40% |5455## Aha Moment Definition and Measurement5657### Finding the Aha Moment581. Pull D30 retained vs churned cohorts592. Compare feature usage in first session603. Find the action that predicts retention with statistical significance614. Validate: do users who take action X retain at 2× rate?6263### Aha Moment Examples64| Product | Aha moment |65|---------|-----------|66| Slack | Team sent 2,000 messages |67| Dropbox | Uploaded 1 file across 2 devices |68| Figma | Shared a design with a collaborator |69| HubSpot | Created 1 contact + sent 1 email |70| Loom | Sent a Loom that was watched |7172### Measuring Time-to-Aha73- Track: median minutes/hours from signup to aha event74- Segment by: acquisition channel, plan, device75- Goal: reduce by 20% each quarter7677## Product Qualified Leads (PQLs)7879A PQL is a free/trial user who has hit usage signals indicating sales-readiness.8081### PQL Criteria Framework82Define PQL when user meets threshold on:83- **Usage depth**: Used X features or completed Y actions84- **Usage frequency**: Active on Z of last 7 days85- **Team signal**: Invited ≥ 2 teammates86- **Upgrade signal**: Hit plan limit, visited pricing page ≥ 2×87- **Intent signal**: Requested demo, contacted support about enterprise8889### PQL Score Example90| Signal | Points |91|--------|--------|92| Invited 3+ teammates | 30 |93| Used core feature 5+ times | 20 |94| Active 5+ days in last 7 | 20 |95| Visited pricing page | 15 |96| Exported data | 10 |97| Hit usage limit | 15 |9899**PQL threshold**: score ≥ 70 → route to sales100101## Expansion Revenue102103### Expansion Triggers104- User hits plan limit (seats, storage, API calls)105- Team grows (more seats needed)106- Usage spikes (seasonal, growth event)107- New use case discovered in product108- Upgrade path surfaced in-product109110### Net Revenue Retention (NRR) Formula111```112NRR = (Starting MRR + Expansion - Contraction - Churn) / Starting MRR × 100%113```114- NRR > 100%: expansion outpaces churn (grows without new customers)115- NRR > 120%: elite PLG benchmark116- NRR < 100%: churn exceeds expansion — unsustainable117118### In-Product Expansion Triggers119- Show usage meter approaching limit (80%, 95%, 100%)120- Unlock preview of paid features at the right moment121- Team member limit reached → prompt to upgrade for the whole team122- Export/share blocked → upgrade CTA with clear value prop123124## PLG Health Metrics Dashboard125126| Metric | Target | Frequency |127|--------|--------|-----------|128| Free → paid conversion | 3–8% (SaaS avg) | Weekly |129| Time to first value | < 5 min | Weekly |130| D7 activation rate | > 40% | Weekly |131| PQL volume | Growing MoM | Weekly |132| PQL → paid conversion | 20–40% | Monthly |133| NRR | > 110% | Monthly |134| Viral coefficient (K-factor) | > 0.5 | Monthly |135136## Viral Coefficient (K-Factor)137138```139K = i × c140141i = average invites sent per user142c = conversion rate of invitees143144Example: average user invites 3 people, 20% convert145K = 3 × 0.20 = 0.6146147K > 1: viral growth (each user brings >1 new user)148K = 0.5–1: meaningful organic boost149K < 0.5: word of mouth exists but not compounding150```151152### Improving K-Factor153- Add team-based features (collaboration requires invites)154- Share-by-default outputs (exported files link back to product)155- Referral incentive (both parties rewarded)156- Network effects that make product better with more users157158## Freemium Conversion Optimization159160### Conversion Levers1611. **Limit selection**: Limit the right thing (seats, not core value)1622. **Upgrade moment**: Surface upgrade at point of pain, not randomly1633. **Trial element**: Time-limited access to paid features1644. **Social proof**: Show what paying customers achieve1655. **ROI calculator**: Make value concrete before asking for payment166167### Common Freemium Mistakes168- Limiting so aggressively free users get no value (never convert)169- Giving away too much (no reason to upgrade)170- Upgrade CTA buried in settings (not in context of use)171- One-size upgrade path (no monthly option for price-sensitive users)