How to efficiently find a product that sells

To find product-market fit efficiently, test two very different MVPs at the same time that are of high enough quality

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You may be familiar with the Lean Startup method by Steve Blank (Stanford University) to create successful products by trial and error: 

  • Create a minimal viable product (MVP)

  • Try to sell it to your target customers

  • If people buy it, proceed and build the full product

  • If it doesn’t sell, update it or create a different MVP and try again. Repeat until it sells

This method is useful for companies of any size, not only startups.

Today’s research from University College London and Boston College uses a (Bayesian) statistical model to determine how to accurately find an MVP that sells, using the least amount of resources to get there.

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Test intermediate quality MVPs against each other. Disproving an MVP is just as valuable as proving one.

Impacted metrics: Customer acquisition
Channels: Product
For: Both B2C and B2B

Tip type: New research (March 2021)
Previous tip: Which ads work best in emerging vs developed markets (All tips here)


To find product-market fit using different minimal viable products (MVPs) as efficiently as possible (accurately, economically, and fast), you should:

  • Test two (or more) significantly different MVPs at the same time. Avoid common features. This maximizes learning (e.g. what people liked of each) and allows you to compare sales (otherwise, you’re making assumptions of what are ‘good’ vs ‘bad’ sales).

  • Use MVPs of sufficiently high quality. If quality is too low, nobody will buy it, this gives us little or - worse - wrong information. If quality is too high, you’ve invested too many resources.

  • Use higher quality MVPs for product versions you think are more likely to work, and lower quality versions for products you want to test but are less likely to succeed (failure to sell is just as informative as succeeding).


  • An MVP is a version of the product that communicates the unique selling point of the product but without the “bells and whistles” (e.g. a website that has 10% of the functionality that you envision).

  • For new products (e.g. Clubhouse, the first iPhone), it’s often a challenge to understand customer demand without launching the product. Testing different MVPs until one works offers a way around that.

  • The method in the Recommendation section maximizes learning (of what works vs doesn’t) while minimizing the risk of choosing the wrong product.

  • Higher levels of learning from MVP testing are related to higher profits of the final product. The more you learn, the more you earn.

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Why it works

  • Mathematically, this method maximizes the probability of learning with the least number of different MVPs without sacrificing the accuracy of which product is best for the market.


  • This research is a theoretical analysis of the lean startup method. Since this was not its purpose, it lacks lab experiments or real-world observations of MVPs to observe what happens when they follow this theoretically optimal method.

  • In a perfect world, numbers don’t lie. But the world isn’t perfect (e.g. the researchers could have failed to take something into account in their model), so be cautious when applying this.

Companies using this

  • Startups often make the mistake of launching an MVP as a ‘fast way to launch’, and then build on it, rather than a way to test core product assumptions. If they do test different MVPs, they usually differ in design but are too similar in terms of functionality. This is suboptimal and limits learning.

Steps to implement

  • Finding product-market fit through MVPs is

    • Ideal when

      • You’re uncertain if a need exists (e.g. an app that plays soft music for your plants). Not if you’re quite certain it is (e.g. there’s a need for more fuel-efficient cars)

      • People in your target group (e.g. B2B marketers) have similar needs, so a few successful sales are a signal the whole group would buy. Otherwise, you risk misinterpreting sales in a subset of customers (e.g. B2B marketers in manufacturing) as demand for the whole group

      • Product changes are relatively easy and can be easily upgraded (e.g. software)

    • Limited when a product needs to have a high level of quality (e.g. luxury products) or completeness (e.g. a cargo ship) in order to sell

  • Build MVPs with different functionality assumptions and test them against each other. For example, a B2B accounting software could test two different core functionality assumptions with two MVPs:

    • Product A’s unique selling point is seamless integration with bank account data

    • Product B’s main value is extensive spending analytics instead

  • If you have many functionalities you want to test with different MVPs, you could start with testing options that you think will be discarded using low-quality MVPs (but not too low quality, otherwise nobody will buy and you won’t learn). Then, for the most promising one(s), you can create a higher quality MVP so you will have a headstart in product development if it sticks.

  • Testing just one time (two different MVPs) can be enough if it leads to sufficient learning (e.g. you see a large difference in sales).

Study type

Theoretical model (of optimal decision-making and captured market demand, using Bayesian learning)


Yoo, O. S., Huang, T., & Arifoğlu, K. (March 2021). A Theoretical Analysis of the Lean Start-up Method. Marketing Science.

[Link to paper]


UCL School of Management, University College London and Carroll School of Management, Boston College. United Kingdom and United States

Remember: Because of the groundbreaking nature of this paper, it could be disproven in the future (although this is rare). It also may not be generalizable to your situation. If it’s a risky change, always test it on a small scale before rolling it out widely.

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