Build-Measure-Learn cycle – A framework for smarter startup investments
How startups can iterate faster and grow smarter?
Introduction
Venture capitalists invest in startups that promise rapid growth and market impact. However, without a clear mechanism for learning and adapting, many startups burn through capital-chasing assumptions rather than proven business models.
The Build-Measure-Learn (BML) cycle, a core principle of the Lean Startup methodology, provides a structured way for startups to test hypotheses, analyze customer feedback, and pivot or persevere based on real-world data. For investors, understanding how well a startup implements this cycle can mean the difference between backing a winner or funding a failed experiment.
In this edition of VentureBizz, we’ll explore how the Build-Measure-Learn cycle helps startups minimize waste, accelerate learning, and optimize capital allocation. We’ll also discuss how venture capitalists can assess whether a startup is using this framework effectively before making an investment decision.
What is the BML cycle?
The BML cycle is a continuous loop that allows startups to validate their ideas with minimal risk:
Build – Develop a Minimum Viable Product (MVP) to test a key hypothesis.
Measure – Collect actionable data from early adopters.
Learn – Analyze results to determine whether to pivot or persevere.
The goal is to shorten feedback loops, allowing startups to make informed decisions quickly rather than wasting time on unvalidated assumptions.
Why venture capitalists should care about BML?
1. Preventing wasteful capital deployment
Startups that ignore the BML cycle often overbuild products, spending months (or years) developing features that customers don’t want. By ensuring that a startup is following an iterative approach, investors can help companies use capital more efficiently.
Case Study: Intuit’s lean experimentation
Intuit, a large financial software company, applies the BML framework even at scale. Instead of building full-featured products, Intuit teams launch small, rapid experiments. One such test was a simplified tax filing feature, which was validated within weeks before being integrated into TurboTax (Blank, 2020).
Investor Insight:
Before investing, ask:
How frequently does the startup run experiments?
What is the cost per experiment, and what have they learned so far?
Are they burning cash on development before confirming demand?
2. Identifying startups with strong data-driven cultures
A startup that follows the BML cycle builds a culture of testing, measuring, and adapting rather than making decisions based on intuition alone. Investors should look for founders who prioritize data over assumptions.
Case Study: Booking.com
Booking.com became a travel industry giant by embedding the BML methodology into its DNA. Instead of making large-scale changes, they run thousands of A/B tests simultaneously, measuring user behavior and optimizing based on data. This iterative approach has been a key driver of their success (Thomke, 2020).
Investor Insight:
When evaluating a startup’s approach to measurement, ask:
What key metrics do they track, and how do they determine success?
How frequently do they change product features based on real data?
Do they rely on A/B testing and user behavior analysis?
3. Recognizing when to pivot vs. persevere
Startups that follow the BML cycle know when to adjust their strategy based on feedback. If customer data indicates a lack of demand, smart founders pivot early rather than doubling down on a failing idea.
Case Study: Instagram’s pivot from Burbn
Instagram began as Burbn, an app that combined photo-sharing with location check-ins and gaming mechanics. The founders noticed that users were mostly engaging with the photo-sharing feature, so they stripped everything else away and rebranded it as Instagram. The result was a platform that grew to over a billion users before being acquired by Facebook (Stone, 2017).
Case Study: Slack’s shift from gaming to messaging
Slack started as an internal messaging tool within a failing gaming company. The founders realized that the messaging tool was more valuable than the game itself and pivoted, leading to one of the most successful enterprise communication tools (Butterfield, 2021).
Investor Insight:
To assess whether a startup understands when to pivot, ask:
What hypotheses have they disproven so far?
How have they incorporated feedback into their current roadmap?
What contingency plans do they have if their current approach fails?
4. Scaling only when the data supports it
A common mistake among startups is scaling prematurely—spending aggressively on marketing and hiring before achieving product-market fit. The BML cycle helps prevent this by ensuring that scaling efforts are based on proven customer demand rather than speculation.
Case Study: LinkedIn’s measured growth
LinkedIn followed a gradual expansion strategy, first launching with a basic networking tool and refining its features based on user behavior before aggressively scaling. By measuring engagement and optimizing features like endorsements and content sharing, LinkedIn grew into the world’s largest professional network (Hoffman, 2015).
Investor Insight:
Before investing in a growth-stage startup, check:
Has the company validated demand before scaling efforts?
Are they spending on growth before proving retention?
Do they have a structured process for testing new markets?
How VCs can evaluate a startup’s use of BML?
To determine if a startup is effectively using the Build-Measure-Learn cycle, ask:
How many iterations has the product gone through since its initial MVP?
What key insights have been learned from user testing?
How often does the team run experiments, and how do they measure success?
Have they demonstrated an ability to pivot based on data?
Are they making decisions based on real customer feedback or gut instinct?
Founders who embrace BML are less likely to burn through capital on unproven ideas and more likely to adapt to market needs effectively.
Conclusion: The importance of investing in lean startups
For venture capitalists, backing startups that follow the Build-Measure-Learn cycle is a strategic way to minimize risk and maximize return. Startups that experiment quickly, analyze real data, and iterate based on insights are more likely to find product-market fit and scale successfully.
By prioritizing lean, data-driven, and adaptable companies, investors can ensure their capital is fueling real, sustainable growth rather than unvalidated assumptions.
In the next edition of VentureBizz, we’ll explore The Pivot Decision—How to Recognize When a Startup Should Change Direction and How Investors Can Support That Process.
Sources:
Blank, S. (2013). Why the Lean Startup Changes Everything. Harvard Business Review.
Hoffman, R. (2012). The Startup of You. Crown Currency.
Ries, E. (2011). The Lean Startup. Crown Currency.
Stone, B. (2017). The Upstarts: How Uber, Airbnb, and the Killer Companies of the New Silicon Valley Are Changing the World. Little, Brown and Company.
Thomke, S. (2020). Experimentation Works: The Surprising Power of Business Experiments. Harvard Business Review Press.