5min
Module 1: The Lean Startup Mindset
Module 2: Defining Your Vision
Module 3: The Build-Measure-Learn Feedback Loop
Module 4: The Minimum Viable Product (MVP)
Module 5: Validating with Customers
Module 6: Pivoting or Persevering
18/23 Lessons
Content
Assignment
In the Lean Startup, we use a combination of qualitative and quantitative data to validate our ideas. While interviews tell you the "why" behind customer behavior, tools like A/B testing and cohort analysis give you objective data on the "what."
1. The Direct Answer: A/B Testing
An A/B test is a simple experiment where you compare two versions of a product or webpage to see which one performs better. It’s a powerful way to test a specific hypothesis and get a clear, data-driven answer.
How it Works:
You create two versions—Version A (the control) and Version B (the variation)—and show them to different groups of users at the same time. The goal is to test a single variable. For example, if your Leap-of-Faith Assumption is that a new headline will increase sign-ups, you would send 50% of your traffic to the page with the original headline and 50% to the page with the new headline.
You let the experiment run for a set period and then analyze which version resulted in more sign-ups. The winner tells you which hypothesis was correct.
Here is an example:
2. The Long-Term View: Cohort Analysis
A cohort is a group of users who share a common characteristic, usually when they first signed up or started using your product. Cohort analysis involves tracking the behavior of this group over time to see if your changes are having a lasting impact.
How it Works:
Imagine you launch a new email marketing campaign in January. You would treat all the new sign-ups from that month as a "January cohort." Then, you would track their retention and engagement over the next several months to see if they are more active than the "December cohort" that signed up before your new campaign.
This analysis helps you move beyond short-term "spikes" in engagement and see if your product improvements are actually leading to long-term user retention and satisfaction.
3. A/B Testing & Cohort Analysis
Purpose
Focus
Example
A/B Testing
To get a definitive answer on a single change
Short-term, specific behavioral changes
Does a blue button get more clicks than a red button?
Cohort Analysis
To see if changes have a lasting impact on user behavior
Long-term retention and engagement trends
Are customers who signed up after a feature launch more likely to return after 3 months?