Growth Model Design // Supanote.ai
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Growth Model Design // Supanote.ai

Growth Equation

Supanote's revenue growth can be broken down into four key components:

Revenue = f(acquisition, onboarding, EnR, monetization)

ComponentKey MetricsPrimary Goal

Acquisition

Visit-to-signup ratio

Increase qualified traffic

Onboarding

Signup-to-activation rate

Drive first note creation

EnR

DAU/MAU, retention rates

Progress users through segments

Monetization

Free-to-paid conversion

Optimize ARPU

Note that for Supanote's stage (although we have PMF already but we aren't in early scaling stage either), we have to focus on % new user retention and % activation rate.

User Segments


User Type

Key Metrics

Casual Users

Logged in ≤ 3 times in the last 30 days. Created ≤ 2 notes, no integrations, minimal engagement.

Core Users

Logged in 4–10 times in the last 30 days. Regular note creation, some template usage, or basic collaboration.

Power Users

Logged in > 10 times in the last 30 days. Use advanced features (templates, integrations, collaboration).


Aspect

Casual Users

Core Users

Power Users

Login Frequency

Sporadic (monthly)

Regular (weekly)

Daily or multiple times per day

Note Usage

Simple notes, infrequent

Organized notes, frequent

Highly detailed and structured notes

Feature Adoption

Minimal, basic features

Moderate, templates/integrations

Extensive, advanced features

Collaboration

Rarely collaborate

Occasional collaboration

Frequent and active collaboration

Integrations

None

Exploring basic integrations

Fully integrated with workflow





Each user segment has distinct characteristics and behaviors:

MetricCasual UsersCore UsersPower Users

Monthly Logins

≤3

4-10

>10

Notes Created

≤2

3-10

>10

Features Used

Basic notes only

Templates & basic integrations

Advanced features

Collaboration

None

Occasional

Frequent

Key Questions by Area

Acquisition

  • Current channels effectiveness
  • Traffic quality by source
  • CAC by channel
  • Organic vs paid mix

Onboarding

Key areas to investigate:

  • First note creation success rate
  • Dictation accuracy metrics
  • Template discovery rate
  • Time to client addition

Engagement & Retention

Focus metrics:

  • Note creation frequency
  • Template usage patterns
  • Integration adoption rate
  • Collaboration frequency

Monetization

Priority questions:

  • Free-to-paid conversion drivers
  • Feature usage correlation with conversion
  • Pricing tier effectiveness
  • User segment LTV

Implementation Plan

PostHog Events Setup

Essential events to track:

  1. User Actions
    • Note creation
    • Template usage
    • Integration connections
    • Client additions
  2. User Properties
    • Segment type
    • Feature adoption level
    • Activity frequency
    • Collaboration patterns

Quick Wins

Near-term opportunities:

AreaActionExpected Impact

Onboarding

Streamline first note

+20% activation

Templates

Improve discovery

+15% adoption

Integrations

Simplify setup

+10% connection rate

Growth Model Application

Focus on:

  1. Activation Metrics
    • First note creation
    • Template usage
    • % activation rate
  2. Progression Metrics
    • Casual to core conversion
    • Core to power conversion
    • Feature adoption velocity
    • % new user retention

"Aha Moments" Framework

Critical user realizations:

MomentTriggerImpact

Dictation Accuracy

First successful note

Initial activation

Template Value

Time saved

Core user conversion

Integration Benefits

Workflow automation

Power user conversion

Next Steps

Immediate priorities:

  1. Set up PostHog tracking
  2. Implement user segmentation
  3. Launch onboarding improvements
  4. Develop progression triggers

Medium-term focus:

  1. Optimize monetization flows
  2. Enhance template system
  3. Improve collaboration features






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