Product led growth

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

1. Pull D30 retained vs churned cohorts
2. Compare feature usage in first session
3. Find the action that predicts retention with statistical significance
4. 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

1. Limit selection: Limit the right thing (seats, not core value)
2. Upgrade moment: Surface upgrade at point of pain, not randomly
3. Trial element: Time-limited access to paid features
4. Social proof: Show what paying customers achieve
5. 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)