Lean Startup Best Practices: Build a culture that supports learning.

Building valuable products for customers is challenging, and Lean Startup methodologies can help. The very act of learning itself is fundamental to these principles, and it’s worth attending to: one of the core tenets of Lean Startup practice is the Build-Measure-Learn loop, an application of the scientific method.  But how do you maximize your learning with every pass through the loop, in the face of both success and inevitable failure? A healthy culture that supports learning can help ensure that you gain maximum value from every experiment you run.

It’s easy when your data comes back validating your hypotheses about your customer and business, but what happens when you upset your beliefs about your customer or business?I’ve seen too many teams ignore hard data and learnings only to maintaining a course toward product development failure.  And even science-based Lean Startup methods will fail without a culture that supports learning. To illustrate why, I’ll briefly draw your attention to an example from the history of science.

Copernicus, Giordano Bruno, and even Galileo, widely known as a founder of modern science, were persecuted, arrested, and even burned at the stake, in part because the prevailing culture didn’t support their scientific methods of learning and the data and findings they presented.

The Catholic Church began the Inquisition in 12th-century France to combat heresy—but it took its toll on science throughout the 16th and 17th centuries before it was finally abolished in the early 19th century.

inquisition

A product manager presenting customer data to the executive team, or two priests showing the application of torture under the supervision of the Inquisition?

Nicolaus Copernicus formulated the heliocentric model of cosmology, placing the Sun at the center of the universe rather than the Earth. This was literally Earth shattering news at the time for the Catholic Church, which saw this as a threat to their authority  based in their belief system—which included the Earth being at the center of things.

Instead of dealing with the facts of the situation, they attacked the messengers. Copernicus so feared the Inquisition that he delayed publishing his findings until he was on his deathbed in 1543. Giordano Bruno followed Copernicus and was burned at the stake. Galileo was placed under house arrest until his death.

Why was is so hard for these scientists to share their data and findings? The answer is complicated, but many product development professionals know the answer all too well. Suffice it to say that the prevailing culture espoused by the leadership at the time did not support their findings, particularly when that learning contradicted the prevailing belief system.

The parallels with modern product development teams, even or perhaps especially those practicing Lean Startup principles, are stark.

What is the culture like in your company? Is it hard to speak up when you have learnings to share and the news seems to contradict your quarterly goals or yearly plan? Does the highest-paid person’s opinion matter more than your customer data? Do teams and leaders prefer to stay the course, even when data is signaling a change might be warranted? I’m aware of too many companies that suffer from a combination of hubris, denial of reality, and delusions that everything is going according to plan—even as the projects, teams, and in the worst case, the entire company, is headed toward failure.

So what can we do?

The point I’m making is that culture matters. Creating a culture that supports continuous learning can help protect you in the face of visionary leaders with compelling reality distortion fields or your own subconscious desire to believe that your project is on track, even when it’s not.

Creating a safe space to learn within your company—a culture that supports learning in the face of both success and failure—while challenging, is worth the effort. Even with the world’s best product team  building, experimenting, and measuring results, Lean Startup methodologies won’t work without a culture that supports learning from your data.

I’ve discussed previously why it’s often better to reorder the Build-Measure-Learn loop to start with learning rather than building, as building without an understanding of your customer is superstition, not science. Learning literally comes first, beginning with what you already know about your customers and your business. Good hypotheses are also required, as they drive experiments that you truly can learn from.

But what of culture? Culture is at once a process and a product. This means we have the power to change culture, but it requires conscious, meaningful effort. Think about how you want your culture to be, then take action. Everyone on your team contributes in minute ways to building, changing, and refreshing your culture with every interaction and every word spoken.

Culture is at once both a process and a product; we can make change with conscious, meaningful effort. —Tweet This.

Leaders have a responsibility here, of course. Culture starts at the top and permeates your organization. Your leadership team must be aligned and supportive—but that is not enough.

Leaders must make conscious efforts to build and maintain a culture of learning. Candor, based in vulnerability and accountability, is required. Clear communication about how the culture is meant to be will help–it should be written down, easily accessible, and referenced often in your practice.

Leaders, like everyone else, need to make an ongoing, conscious effort to build and maintain a culture that supports learning. Practice is key, and leading by example works. Executives can and should conduct root cause analysis when things don’t go as expected, and discuss the learnings and action items openly within the company.

It’s won’t be enough that one of your executive, engineering, product, or other teams practice a learning culture in isolation, so plan ways to ensure everyone practices together. Most teams are multidisciplinary, so ensure that everyone participates in learning moments through reporting on experiments, and during project retrospectives and postmortems. Learnings and action items should be communicated widely, and used as inputs to your product development and planning processes.

Building and maintaining a healthy culture that supports learning will help you maximize value from every turn through the Learn-Build-Measure loop, and help you make good on the promise of the Lean Startup.

Lean Startup Best Practices: Write hypotheses you can learn from.

If you practice Lean Startup methodologies, you’re likely familiar with Build-Measure-Learn. It seems like the directive here is to first build something quickly, then  measure some results, and then try to learn, all as quickly as possible. The problem is that too many people have taken this literally, building without first pausing to articulate what they think they know about their business and customer, and what it is they want to learn next.

What we actually need to do is Learn-Build-Measure. In other words, think first about what you already know about your business and customer, then form a testable hypothesis to validate, and then move on to building and measuring.

Now you might be thinking, “But it’s a loop, so why should it matter where we start?”

It matters because building without some reasonable understanding of your business and customer is just guessing. And if you’re guessing, you’re in the realm of superstition rather than science.

Building without an understanding of your customer is superstition, not science. – Tweet This

Unless you’ve put some serious thought into testing hypothesesrunning experiments, and applying Lean Startup methodologies, it’s likely that you’ve been guessing about the results of your experiments rather than learning from them. Every time this happens, you lose a valuable chance to validate and improve your understanding of your business and customers, and you waste time and money.

Writing a good hypothesis is hard, but it’s worth the effort. A good hypothesis makes it easy to design an experiment to test and learn from it—by making clear what it is you are validating or invalidating—usually your current understanding of your business and customers.

A proper hypothesis is an educated guess—just like we learned in our science classes growing up. What I see most often, though, are merely guesses: spaghetti on the wall and shots in the dark. How could this be happening, with all of our understanding of Lean Startup, and of science?

The Lean Startup Build-Measure-Learn loop is really an application of the scientific method. Check it out (slide 24 from that presentation, which touches on several Lean Startup practices):

scientific-method-lean-startup

This means that we need to use care to ensure we take our learnings and shape them into new ideas and generate new hypotheses.

tweet-thisThe Lean Startup Build-Measure-Learn loop is an application of the scientific method. – Tweet This

Many people are unable to clearly articulate their current understanding and assumptions about their business and their customer. If you are unable to clearly articulate these and you are trying to run experiments, it’s a red flag. It’s probably better to start with developing a better understanding of your customer than running haphazard experiments.

Here is a template to use for crafting hypotheses that you can learn from, and hopefully help clarify whether running an experiment is the next right step.  If you can write a proper hypothesis, you’re probably ready to run an experiment. I’ll start with this simple version:

Because we believe X, if we do Y, we expect Z to happen.

Writing a hypothesis with details about your business requires a solid understanding of our business and customer.  We must assert something about what we know, or have already learned that we can then validate. This is true validated learning.

Let’s add more detail using a lemonade stand business as an example and get one step closer to having a hypothesis we can validate:

Because we know our customers prefer colder lemonade in warmer weather, if we add ice to each cup of lemonade we sell, we expect higher customer satisfaction and more sales.

I like to go even further, adding an assessment of the potential outcomes to the experiment plan. This gives you a better idea of why you’re running the experiment in the first place, forces you to think about how you’ll use what you learn, and importantly, forces you to consider some of your assumptions:

If we are correct, we would also expect that customer surveys would also validate a preference for ice, that we might be able to run tests to find the quantity of ice that maximizes sales, and that our Net Promoter Score would increase.  If we are wrong, it might indicate that customer preference for cold lemonade might be influenced by other factors such as the precise ratio of ice to lemonade, the exact temperature outside, the quantity of sugar, lemon juice, and water in our recipe, or the price of a drink from your competitor down the street.

This assessment is crucial, and perhaps the hardest part of running an experiment as it reflects the thinking behind the experiment. Good experiments require thoughtful planning up front, even when practicing Lean Startup methodologies.

tweet-graphic-transGood experiments require thoughtful design, even for a lean startup. – Tweet This

Even our simple example exposes many factors that might affect experiment design and results interpretation. Taking time to think, articulate what you know, and expose your assumptions while forming a proper, testable hypothesis will help you do truly validated learning, and help you to fulfill the promise of the Lean Startup.