Build/measure/learn does not prescribe building first to learn

Lean Startup was introduced at a time when there was a waterfall mindset to building products. Innovations in the Cloud was making it easier and cheaper to build, creating a huge advantage for StartUps who could build, launch, learn and iterate. StartUps could gain 10X learnings before incumbents even put out their first version. Today, there are simply too many products resulting in choice overload.

However, there are easier and cheaper ways to test prototypes before building. The result, the principles remain the same but we no longer need to build first.

Photo by Jannis Blume on Unsplash

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Build a product, get it into the real world, measure customers' reactions and behaviors, learn from this, and use what you've learned to build something better. Repeat, learning whether to iterate, pivot or restart until you have something that customers love. - Steve Blank

A core component of Lean Startup methodology is the build-measure-learn feedback loop. The first step is figuring out the problem that needs to be solved and then developing a minimum viable product (MVP) to begin the process of learning as quickly as possible. - Eric Ries

Those who have studied both Steve Blank and Eric Ries know that what they had proposed was to leverage the principles of the scientific method; the process of objectively establishing facts through observation, testing and experimentation. The process involves making an observation, forming a hypothesis, making a prediction, conducting an experiment and analyzing the results. You do this with speed until you’ve wow’d even users, established category leadership, or created the category altogether. All this before the incumbent has even woken up.

But in 2022, the game has changed. Technology advancements have made it even cheaper to build, resulting in too many new products. Students out of college and seasoned professionals alike don’t want to work for the bureaucratic companies of their parents’ age, so there is high supply of talent for StartUps. Combined with cheap capital, global talent pool, the result is even more new products.

The result is waste and a very inefficient economy where people suffer.

Testing hypotheses with experiments at speed:

The game is the same, generating and testing hypotheses with experiments at speed. What do we believe to be true that needs to be tested? Are we building the right thing? Are we going to make money?

There are hard questions to answer. But with today’s innovations in augmented reality, low code prototyping products, design simulation tools like Figma, etc. it is possible to test hypotheses before building.

1. First, frame your hypotheses based on the riskiest things that need to be true for your product and business model to succeed.

2. Create a prototype, conducting an experiment and analyze the results to validate/invalidate the hypothesis.

Identify the riskiest hypotheses and assumptions to test with experiments:

Hypothesis: educated guesses or bets you hope to test in your experiment, the hypothesis will predict an experimental result.

Assumption: things you assume to be true about the world; customers, markets, economy, technology feasibility, trends, customer behavior, competitors, etc.

• Problem experiments: 

  • Is the product solving the core pain or job? 

  • Is there a need? Is it something people want?

  • Are the solution requests proxies for real & valuable problems?

  • Do people actually have the problem you believe they have?

  • Do people actually want what you’re planning to offer them?

• Pricing and value experiments:

  • Does the product deliver value to the customer? 

  • Is there enough value for customers that they are willing to switch?

  • Are customers willing to pay for it?

• Solution experiments: 

  • Is it doing the job correctly for the target personas? 

  • Does it deliver on all the expected promises and use cases needs?

  • Does it fit into the customer’s lives? 

  • Does it overcome the constraints? 

  • Are our assumptions accurate?  

• Usability experiments:

  • Can the target personas easily access the value?

  • Is it easy to understand and use? 

• Growth & adoption experiments: 

  • Is it easy to signup, demo, onboard, try, self-serve? 

  • Does it entice switching and overcome frictions of the new way?

  • Does it drive engagement / value realization? 

  • Does it have a flywheel and loops that drive acquisition, activation, upsell, retention? 

Test experiments with prototypes before building:

All of these risky hypotheses and assumptions can be tested in experiments without huge investments in building a product. Some examples of tools:

• Figma

• InVision Studio

• Adobe XD

• Webflow

• Axure RP

• Origami Studio

• Balsamiq

• Autodesk

• FreeCAD

• Raspberry PI

• Blender

Testing upfront with experiments allows product teams to de-risk investment decisions, increase the probability of wow’ing customers, place talent on the right products that have a decent chance of success, and prevent the hire/layoff cycle when product initiatives fail.