Seven Peaks Insights

The Build Measure Learn Model

Why it’s important for software development




The Build Measure Learn Model, also known as the validated learning loop, is the process of applying knowledge to your product by verifying essential business reasons for its existence.

Instead of going all in with your instincts or guesses about a company concept, the Build Measure Learn Model uses a set of data metrics to validate the outcome by constructing a feature, then measuring the outcomes from that feature, and then assessing what to do next for continuous ongoing development.

The importance of Build Measure Learn model

Using a validated learning loop in your product development process has numerous significant advantages over traditional learning approaches, including:

  • Faster development – allows for greater focus for your business’ product to progress towards the release state faster.
  • Action-based metrics – learning from measurable data to ensure the final product aligns closely to the user’s needs.
  • Lower costs – greater learning leads to more efficient development, which saves time and cost throughout the product development process.
  • Agile methodology – supports and implements agile working with quick development, allowing for better organizational flexibility

Our validated learning loop at Seven Peaks Software:


Build Measure Learn – Fundamentals

The Build Measure Learn model is used in scrum agile working processes. The term was introduced by Eric Ries back in the year 2011.

In simple terms, a validated learning loop is a unit of development that concludes by assessing ideas and then comparing them against future clients to approve the outcome.

The Build Measure Learn model is the most utilized on the internet because analytics can follow visitor activity and provide precise statistics and insight into how website features work in practice. This method may be used everywhere and for anything; all it requires is a creative approach to examining and optimizing important measures.

The standard actions of validated learning loop are:

  1. Defining a goal
  2. Defining a metric that outlines your goal
  3. Performing necessary actions to accomplish your goal
  4. Analyze the metric – have you gotten closer to your goal?
  5. Adjust, change, and attempt again

Breaking it down – The Build Measure Learn Model Of The Validated Learning Loop

Eric Ries’ Build Measure Learn model encourages sustainable innovation and lean product progression while reducing risk through the validated learning loop model described below.


The Build Measure Learn model starts with Build.

The first step is to create a plan to “build” on, based on a notion you want to test or understand. This idea will become your hypothesis, a forecast of what you will create.

The next phase is to build while considering the hypothesis since the two can help answer a vital question: “At what point in the engineering effort is the potential value to the business no longer worth it?”

Is it still a gain if a certain product feature for a mobile application has the potential to generate an extra $15,000 in revenue this quarter but costs $40,000 to develop?

The question you must ask yourself is, “At what point does the software development effort become more expensive than the potential benefit to the business?”

To better defy these questions, your opportunity evaluation will necessitate A/B testing as well as more development, which will include:

  • Building two variants
  • Developing and implementing an A/B setup code
  • Removing the setup code
  • Removing the losing variant after the A/B testing finishes

The build process is not intended to prevent you or your team from experimenting, but rather to highlight high-impact possibilities and supporting data.


After you’ve developed the feature using analytics and deployed it during the build process, the next step is to go on to the measure phase and track the metrics to evaluate how your hypothesis works.

To start, you must get a sample size, which may vary. Follow your measurements for at least two weeks before making a choice on your desired outcome to decide your sample size.

Collect your final analytical data on the same day you started your experiment, for example, if you launched on a Monday morning, try to collect your final data two weeks later on a Monday morning.

Once this is done, it’s good to ask your team these questions:

  • How did the exact outcome compare to the hypothesis?
  • Was the outcome more or less successful than calculated?
  • What were the major changes?

Analyzing how your hypothesis performed increases your understanding of the product and its consumers, allowing you to make more reliable predictions in the future while also driving process changes and future experiments.

Make sure to share your results with your product team after you’ve assessed the conclusion of your hypothesis!  Discuss it at a meeting with your colleagues and follow up on your findings.

Don’t forget to communicate your results with stakeholders and clients to see how your new features are working, as stakeholders are involved in the product’s success and have their metrics to track as well.


Once you have determined the outcome of the product you created based on the data revealed during the measurement process, you must select what to do next.

Using the measured and analyzed data, compare the outcome to the hypothesis established in the first stage. There are two possible conclusions:

  • If your hypothesis was correct, persevere with the process and proceed to the next step of your plan. You may apply the Build Measure Learn model again when applying the next product feature, starting with drawing a new hypothesis and so on.
  • If your hypothesis was incorrect: pivot to a different direction. With the findings you’ve gathered, you should be able to tell what doesn’t work. Then, you may have to rethink your hypothesis and decide what changes to make, which direction to take next. Once you have come up with a new plan, you can then apply the validation learning process to re-build, re-measure and re-learn all over again.

Remember to include the product team in the decision-making process so that they have a better understanding of what’s coming up and may highlight supporting analysis and/or raise any concerns.

By proactively keeping them in the loop and applying the same Build Measure Learn model as described above, you will be able to build a common mental model of the product cycle.

Be sure to record the outcome of your experiment in a place or platform that’s easily accessible for everyone to access.

How and why is all this important in software development?

The validated learning loop that we mentioned above is an important technique for demonstrating progress toward corporate goals when other popular key performance indicators become less relevant.

In summary, it is a miniature system of development that can be swiftly tested to determine whether a chosen direction is accurate or not.

The Build Measure Learn model is particularly effective at assisting startups and SMEs in avoiding the development of features that their consumers do not require.

Those that use the Build Measure Learn model will be more likely to demonstrate success against more traditional key performance measures, like revenue, by regularly verifying what matters most to consumers.

This model can indeed be applied well to various software development projects.



To conclude, the Build Measure Learn Model is all about testing and verifying your product ideas before investing time, effort, and money in producing them.

By testing and confirming your product ideas with your consumers and stakeholders regularly, you may sharpen your product with great efficiency and minimal work, reducing the risk of spending your resources on inefficient development.

“Test before you invest!”

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