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Minimum Viable Product (MVP)

A Minimum Viable Product (MVP) is a concept from Lean Startup methodology that emphasizes the impact of learning in new product development. Eric Ries, the creator of the Lean Startup methodology, defines an MVP as a version of a new product that allows a team to collect the maximum amount of validated learnings about customers with the least effort.

The goal of an MVP is to test fundamental business hypotheses (or leap-of-faith assumptions) and to help entrepreneurs begin the learning process as quickly as possible. It is not necessarily the smallest product imaginable, though; it is simply the fastest way to start learning how to build a sustainable business with the minimum amount of effort.

For example, if a company is building a new app, instead of spending months perfecting every feature, they might create a basic version with just the core features that solve the problem the app is addressing. This MVP is then released to a small set of customers or users, and their feedback is used to iterate and improve the product.

In the context provided, Emma Dahl from Statsig talks about the value of MVPs. She mentions that building an MVP allows you to test an idea quickly and learn from it. Even if the product or idea fails, the learnings from the MVP can inform future direction and potentially salvage elements for iteration.

Here's an example of an MVP in action:

Before building the full product, the team at Dropbox put together a simple video explaining how the service would work. The video was aimed at a community of tech early adopters and showed the technology in a way that allowed potential users to visualize how it would solve their problems. The video drove hundreds of thousands of people to their website to sign up for the beta product. This validated their leap-of-faith assumption that people needed an easy way to store and share files online.

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What builders love about us

OpenAI OpenAI
Brex Brex
Notion Notion
SoundCloud SoundCloud
Ancestry Ancestry
At OpenAI, we want to iterate as fast as possible. Statsig enables us to grow, scale, and learn efficiently. Integrating experimentation with product analytics and feature flagging has been crucial for quickly understanding and addressing our users' top priorities.
OpenAI
Dave Cummings
Engineering Manager, ChatGPT
Brex's mission is to help businesses move fast. Statsig is now helping our engineers move fast. It has been a game changer to automate the manual lift typical to running experiments and has helped product teams ship the right features to their users quickly.
Brex
Karandeep Anand
President
At Notion, we're continuously learning what our users value and want every team to run experiments to learn more. It’s also critical to maintain speed as a habit. Statsig's experimentation platform enables both this speed and learning for us.
Notion
Mengying Li
Data Science Manager
We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion.
SoundCloud
Don Browning
SVP, Data & Platform Engineering
We only had so many analysts. Statsig provided the necessary tools to remove the bottleneck. I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig.
Ancestry
Partha Sarathi
Director of Engineering
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