More in Growth & Experimentation — page 9
AARRR: Pirate Metrics for the Customer Lifecycle
The AARRR framework maps your customer's journey through five key stages: Acquisition, Activation, Retention, Referral, and Revenue. It helps startups diagnose leaks in their growth funnel, from first visit to final sale.
Growth Loops: The Engine of Product Growth
A growth loop is a closed system where outputs are reinvested to generate more inputs, creating compounding growth. Unlike linear funnels, this model unifies product, marketing, and monetization. The footgun is using funnels, which creates silos.
Experiment-Driven Organization: Run Your Org Like a Lab
Treat organizational change like a lab experiment, not a big-bang project. This helps companies adapt by testing new processes on a small scale first, generating evidence before committing to a full rollout. The footgun is mistaking chaos for experimentation.
The North Star Metric: Your Company's One True Focus
A North Star Metric (NSM) is your company's compass: a single metric capturing the core value you deliver to customers. Spotify uses 'time spent listening' to align teams on user engagement.
Growth Hacking: Marketing via Rapid Experimentation
Growth hacking treats marketing like a science experiment, using rapid, low-cost tests to find what grows a user base instead of relying on big, slow campaigns. It's used by startups to A/B test ideas before investing resources.
Growth vs. Fixed Mindset: Your Beliefs Shape Your Abilities
A growth mindset sees ability as a muscle to be built, not a static trait you're born with. It's key when facing tough problems or critical feedback. The footgun is believing you're 'not a natural' at something, which prevents you from even trying.
The Traditional Career Ladder: A Single Path Up
The traditional career ladder is a single, vertical track for promotion, moving from entry-level to executive. It's common in large, established organizations with well-defined roles. The footgun is its rigidity, forcing experts into management to advance.
Growth Squads: Centralized vs. Decentralized
A growth squad is a trade-off between speed and cultural change. A centralized team optimizes for velocity with dedicated members, while a decentralized (embedded) team spreads the growth mindset by borrowing staff.
OKRs: Frame Growth Goals as Measurable Outcomes
OKRs separate your ambitious goal (Objective) from the measurable results that prove you're there (Key Results). Growth teams use this to align on what success looks like.
Experiment Knowledge Base: Don't Repeat Failed Tests
An Experiment Knowledge Base is your company's collective memory for A/B tests, centralizing hypotheses, results, and learnings. It prevents re-running old tests and surfaces past insights.
The Independent Growth Team Model
An independent growth team is a startup-within-a-company, given autonomy to run experiments across the funnel. Use it to break silos and accelerate learning. The main footgun is isolation, creating a rogue unit whose wins are difficult to integrate.
Growth Meeting Cadence: Focus on Learnings, Not Updates
Run your weekly growth meeting like a learning synthesizer, not a status report. Focus on extracting insights from experiments to drive future impact. The biggest mistake is wasting time on "what" you're doing; handle status updates asynchronously.
Growth Product Manager: Driving Metrics, Not Just Features
A Growth PM is a business optimizer for an existing product. They focus on moving a single metric like user activation or retention, often through rapid experimentation. This role is key in product-led companies where the product must sell itself.
What is an Experimentation Stats Engine?
A stats engine is the brain of an A/B testing platform, turning raw data into reliable 'which version won?' decisions. It powers tools that analyze feature rollouts, ensuring statistical rigor.
Metrics Layer: The Dictionary for Your Data
A metrics layer is the central dictionary for your company's numbers, defining what "Revenue" or "Active User" means once for everyone. It ensures teams and AI agents get consistent answers from a single source of truth, preventing conflicting reports.
Server-Side Experimentation: Testing Your Backend Logic
Server-side experimentation renders A/B test variations on the server before sending the page. Use it for testing deep backend logic like search algorithms or to avoid the visual 'flicker' of client-side tests. The footgun: it requires developer cycles.
Feature Management: Control Releases After You Deploy
A feature management platform decouples code deploys from feature releases. It centralizes control over who sees what, turning simple code toggles into a powerful system for canary releases, A/B tests, and targeted rollouts, all from a UI.
ETL vs. ELT: When to Transform Your Data
ETL (Extract, Transform, Load) preps data before storage, like a chef prepping ingredients. ELT loads raw data first, transforming it inside the warehouse. Use ETL for structured reporting; use ELT for flexibility with raw data.
System Dynamics: Modeling with Stocks, Flows, and Feedback
System Dynamics models the world as interconnected stocks (like users) and flows (like signups), governed by feedback loops. Use it to understand why growth stalls or why hiring lags behind need.
Causal Impact: Measuring Effects Without an A/B Test
Causal Impact estimates an intervention's effect by modeling a 'what if' counterfactual. It's used to measure lift from a new feature or ad campaign when a clean A/B test isn't possible.