Growth Experiments A/B Testing Log
Growth
Pre-Seed
KEY OUTCOMES
A systematic, data-driven approach to growth experimentation that enables startups to refine their marketing, product, and retention strategies through structured A/B testing and continuous iteration.
CREATED BY
Kendra Garagan
Founder
@
Kenergy
1-3 HOURS
TO COMPLETE
THE OVERVIEW
This template helps founders and growth teams plan, execute, and track growth experiments, whether for marketing, product, pricing, or customer retention. It provides a clear hypothesis-driven approach to A/B testing, helping startups make data-backed decisions that drive scalable growth.
WHAT PROBLEM IT SOLVES
Many startups run growth experiments and A/B tests without structure, leading to unclear results, wasted time, and a lack of actionable insights. Without a centralized way to track experiments, teams struggle to learn from past tests and refine future strategies. This template provides a systematic way to document, execute, and analyze growth experiments, ensuring startups optimize their acquisition, retention, and conversion strategies efficiently.
WHAT'S INSIDE
Growth Experiment Tracker: A structured log for documenting each test, including hypothesis, goals, and results A/B Testing Setup Guide: A step-by-step breakdown of how to design effective A/B tests Data Collection & Insights Log: A space for tracking test results, conversion metrics, and key takeaways Iteration & Next Steps Framework: Guidelines for scaling successful experiments and pivoting from failed ones
BEFORE YOU BEGIN
Define one key metric you want to improve, whether conversion rate, activation rate, or user engagement. Ensure your team aligns on the primary growth lever you’re testing.
IDEAL FOR
Founders and growth teams testing acquisition, retention, and conversion strategies Startups experimenting with pricing, messaging, and onboarding flows Companies looking to run data-driven growth experiments without guesswork
THE BENEFITS
Provides a structured testing framework to optimize growth and marketing efforts Helps teams track and analyze A/B test results in a centralized place Reduces guesswork by enabling data-driven decision-making Ensures startups iterate efficiently instead of repeating failed experiments
CREATOR TIP
Do not run random experiments—every test should be hypothesis-driven and tied to a business goal.
