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Fast Growth Playbook for LearnLens
Created by Shaunak Ghosh
Build a measurable, high-velocity growth system to take LearnLens Studio from initial traction to thousands of users. You’ll define the right North Star metric, instrument a lightweight experiment loop, design product-led sharing, execute high-intent content/SEO, and make your product machine-readable for both Google and AI assistants.
Requirements
- Comfort using basic web analytics dashboards (pageviews, sources, conversions)
- Basic understanding of what a funnel is (people drop off in steps)
- Ability to edit your website (pages, copy, technical snippets) or work with someone who can
- Willingness to run weekly growth experiments and review results
What you'll learn
- Draft a LearnLens North Star metric and a lightweight AARRR scorecard you can review weekly.
- Define activation and retention in measurable terms (events, cohorts) and identify the highest-leverage funnel constraint.
- Design a PLG-aligned free/trial boundary and a clear before/after value demonstration that accelerates activation and sharing.
- Choose and prioritize high-intent content topics using awareness level plus keyword intent/difficulty.
- Run creator/community distribution as a repeatable system, including creative hook testing and iteration.
- Ensure key pages are indexed and machine-readable with schema, then apply GEO tactics aligned with how RAG-based AI systems retrieve sources.
Learning path
8 modules • Each builds on the previous one
1
North Star and funnel metrics
Define the one metric that represents real learning value (North Star), then map acquisition-to-retention using a simple funnel (AARRR) so every marketing action ties to measurable movement.
5 videos14 min
2
Analytics-driven iteration and experiments
Set up an experiment loop: instrument key events, diagnose funnel drop-offs, run hypothesis-driven tests, and decide with statistical/decision thresholds appropriate for low-traffic stages.
3 videos8 min
3
PLG activation and onboarding design
Design the product experience so users reach “aha” fast, then naturally create public artifacts worth sharing (e.g., course summaries, study plans, progress proof) that drive the next users.
2 videos5 min
4
Viral loops and K-factor math
Understand viral growth mechanics: define the viral loop, measure K-factor, and design product triggers that increase invites/conversions while keeping the loop aligned with real user value.
1 video3 min
5
High-intent content and programmatic SEO
Create content that captures users already trying to learn from YouTube: high-intent landing pages, “course from video” pages, comparisons, and programmatic templates designed to convert.
3 videos11 min
6
Community-led growth and creator partners
Build repeatable acquisition through communities and creators: ambassador programs, study groups, and partnerships with YouTube educators where LearnLens becomes part of their workflow.
2 videos8 min
7
Technical SEO and schema markup
Make your site and product machine-readable: implement core technical SEO, add JSON-LD schema (SoftwareApplication, FAQ, HowTo, Article), and build internal linking so both search engines and AI crawlers understand LearnLens.
2 videos8 min
8
GEO and AI search discovery
Learn how LLM-based recommendations happen (training data + retrieval from the web + citations/knowledge graphs) and implement Generative Engine Optimization: trusted citations, consistent entity signals, structured pages, and distribution into sources AI systems pull from.
1 video4 min
Start Learning
Begin your learning journey
Modules8
Duration58 min
Science-backed learning
In-video quizzes and scaffolded content to maximize retention.
Key concepts
North Star Metric And AARRR Funnel ScorecardAnalytics Instrumentation, Funnels, And Experiment CadenceProduct-Led Activation, Onboarding Value Proof, And Freemium BoundariesViral Loop Fundamentals And Network Effects IntuitionHigh-Intent Content Strategy (Awareness Levels + Keyword Intent)Creator/Community Distribution Systems And Creative TestingTechnical SEO Fundamentals (Indexing) And Structured Data (Schema/JSON-LD)AI/LLM Discovery Mechanics (Retrieval, Citations, And GEO)