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Stanfy | San Francisco Mobile App Developers

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It is just so simple to work through a hypothesis that no one actually does it. When you think about a new product you create hypotheses about the market, user needs and the product.

The quicker you make a reality check (of your hypotheses) the more chances to succeed you will have along the road. And the longer you are focusing on the easy things instead of the hard things, the farther you become from the product success.

Here is a simple and naive framework to work through your initial idea and product vision.

There are just six steps to execute an algorithm.

1. Learn the potential customers, problem, and market

Speak with the users. Find out how they solve their problem right now.

What tools, services, methods do they use currently? Are they satisfied with the tools?

Where might there be room for improvements ? Why?

How big is the market? Is it growing?

What is the estimation for the market size in 5-7 years?

What technology stack is widely used in space?

What emerging technology might be used efficiently for the current problem?

2. Make a hypothesis about user behavior, the product or the market

Formulate your idea about the user, problem and market in an ordered list of hypotheses by priority. The top-most hypothesis will be a starting point for your experiments.

Hypothesis is a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation. - Oxford dictionary

3. Create a test (the simplest one)

Create a test aimed to prove/verify or disprove your assumptions.

Possible tests are:

Simple Web page

Facebook Ads

Questionnaire with Google Forms

Blog post with subscription to newsletter link or wait list

Set of interviews with different offers for the users

Working MVP (app, website, bot, etc) with built-in analytics to gather key metrics (Activation, Engagement, etc)

Don't forget about analytics for key metrics of your tests (i.e., Activation, Engagement, etc).

4. Run the test and collect data

Launch the test. Decide on the minimum amount of data points you would need to statistically measure the received results. You can't learn anything if you don't have enough data points so you should plan in advance how you will get enough users to get the statistical significance in your results.

5. Analyze the data and prove or disprove the current hypothesis

It's time to crunch the numbers and look as closely as possible at the received data. Do these numbers work to support your initial hypothesis? Or did your idea just fall short and you actually disproved the hypothesis?

Think about other possibilities for developing your current hypothesis further. When you come up with it, create and add a new hypothesis to the list from step #2.

6. Select the top hypothesis from the list and continue

Repeat the process until you have enough data to support the core idea. Once you have enough evidence from the completed tests that it is worth proceeding further, you can consider investing greater resources and building the real thing, a much better version of the initial test. Then build a first version of the product and iterate.

Searching for the new product or idea is nothing but a computational task.

Joscha Bach, Cognitive Scientist at MIT Media Lab, recently wrote in his article Everything is Computation:

"Computation changes our idea of knowledge: instead of treating it as justified true belief, knowledge describes a local minimum in capturing regularities between observables. Knowledge is almost never static, but progressing on a gradient through a state space of possible world views. We will no longer aspire to teach our children the truth, because like us, they will never stop changing their minds. We will teach them how to productively change their minds, how to explore the never ending land of insight."

So we can say that the main goal of the described process with hypothesis was "to productively change" your mind and understand the current state space of the world views for the given product/market space.

Hypothesize. Test. Build.
Read more:

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Focus on Hypotheses - The 6 Step Algorithm

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It is just so simple to work through a hypothesis that no one actually does it. When you think about a new product you create hypotheses about the market, user needs and the product. The quicker you make a reality check (of your hypotheses) the more chances to succeed you will have along the road.

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I am always surprised when critics complain that the Lean Startup's Build, Measure, Learn approach is nothing more than "throwing incomplete products out of the building to see if they work."

Unfortunately the Build, Measure, Learn diagram is the cause of that confusion. At first glance it seems like a fire-ready-aim process.

It's time to update Build, Measure, Learn to what we now know is the best way to build Lean startups.

Here's how.

Build, Measure, Learn sounds pretty simple. 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.

Waterfall Development
While it sounds simple, the Build Measure Learn approach to product development is a radical improvement over the traditional Waterfall model used throughout the 20 th century to build and ship products. Back then, an entrepreneur used a serial product development process that proceeded step-by-step with little if any customer feedback. Founders assumed they understood customer problems/needs, wrote engineering requirements documents, designed the product, implemented/built the hardware/software, verified that it worked by testing it, and then introduced the product to customers in a formal coming out called first customer ship.

Waterfall Development was all about execution of the requirements document. While early versions of the product were shared with customers in Alpha and Beta Testing, the goal of early customer access to the product was to uncover bugs not to provide feedback on features or usability. Only after shipping and attempting to sell the product would a startup hear any substantive feedback from customers. And too often, after months or even years of development, entrepreneurs learned the hard way that customers were not buying their product because they did not need or want most of its features.

It often took companies three tries to get products right. Version 1 was built without customer feedback, and before version 1 was complete work had already started on version 2 so it took till version 3 before the customer was really heard (e.g. Microsoft Windows 3.0)

Best practices in software development started to move to agile development in the early 2000's. This methodology improved on waterfall by building software iteratively and involving the customer. But it lacked a framework for testing all commercialization hypotheses outside of the building. With Agile you could end up satisfying every feature a customer asked for and still go out of business.

Then came the Build-Measure-learn focus of the Lean Startup.

Build-Measure-Learn
The goal of Build-Measure-Learn is not to build a final product to ship or even to build a prototype of a product, but to maximize learning through incremental and iterative engineering. (Learning could be about product features, customer needs, the right pricing and distribution channel, etc.) The "build" step refers to building a minimal viable product (an MVP.) It's critical to understand that an MVP is not the product with fewer features. Rather it is the simplest thing that you can show to customers to get the most learning at that point in time. Early on in a startup, an MVP could simply be a PowerPoint slide, wireframe, clay model, sample data set, etc. Each time you build an MVP you also define what you are trying to test/ measure. Later, as more is learned, the MVP's go from low-fidelity to higher fidelity, but the goal continues to be to maximize learning not to build a beta/fully featured prototype of the product.

A major improvement over Waterfall development, Build Measure Learn lets startups be fast, agile and efficient.

The three-circle diagram of Build Measure Learn is good approximation of the process. Unfortunately, using the word "build" first often confuses people. The diagram does seem to imply build stuff and throw it out of the building. A more detailed version of the Build Measure Learn diagram helps to clarify the meaning by adding three more elements: Ideas-Build- Code-Measure- Data-Learn.

The five-part version of the Build Measure Learn diagram helps us see that the real intent of building is to test "ideas" - not just to build blindly without an objective. The circle labeled "code" could easily be labeled "build hardware" or "build artificial genome." The circle labeled "data" indicates that after we measure our experiments we'll use the data to further refine our learning. And the new learning will influence our next ideas. So we can see that the goal of Build-Measure-Learn isn't just to build things, the goal is to build things to validate or invalidate the initial idea.

The focus on testing specific ideas counters the concern that build-measure-learn is just throwing things against the wall and see if they work.

But it's still not good enough. We can now do better.

Start With Hypotheses
What Build-Measure-Learn misses is that new ventures (both startups and new ideas in existing companies) don't start with "ideas", they start with hypotheses (a fancy word for guesses.) It's important to understand that the words "idea " and "hypotheses" mean two very different things. For most innovators the word "idea" conjures up an insight that immediately requires a plan to bring it to fruition. In contrast, a hypothesis means we have an educated guess that requires experimentation and data to validate or invalidate.

These hypotheses span the gamut from who's the customer(s), to what's the value proposition (product/service features), pricing, distribution channel, and demand creation (customer acquisition, activation, retention, etc.)

That the Lean Startup begins with acknowledging that your idea is simply a series of untested hypotheses is a big idea. It's a really big idea because what you build needs to match the hypothesis you want to test.

The minimum viable product you'll need to build to find the right customers is different from the minimum viable product you need for testing pricing, which is different from an MVP you would build to test specific product features. And all of these hypotheses (and minimal viable products) change over time as you learn more. So instead of Build-Measure-Learn, the diagram for building minimal viable products in a Lean Startup looks like Hypotheses - Experiments - Tests - Insights.

Generating Hypotheses
Using this new Hypotheses - Experiments - Tests - Insights diagram the question then becomes, "What hypotheses should I test?" Luckily Alexander Osterwalder's business model canvas presents a visual overview of the nine components of a business on one page. They are:

value proposition, product/service the company offers (along with its benefits to customers)

customer segments, such as users and payers or moms or teens

distribution channels to reach customers and offer them the value proposition

customer relationships to create demand

revenue streams generated by the value proposition(s)

activities necessary to implement the business model

resources needed to make the activities possible

partners 3rd parties needed to make the activities possible

cost structure resulting from the business model

And it brings us to the definition of a startup: A startup is a temporary organization designed to search for a repeatable and scalable business model.

Testing Hypotheses
And once these hypotheses fill the Business Model Canvas, how does an entrepreneur go about testing them? If you're a scientist the answer is easy: you run experiments. The same is true in a Lean Startup. (The National Science Foundation described the Lean LaunchPad class as the scientific method for entrepreneurship.)

The Customer Development process is a simple methodology for taking new venture hypotheses and getting out of the building to test them. Customer discovery captures the founders' vision and turns it into a series of business model hypotheses. Then it develops a series of experiments to test customer reactions to those hypotheses and turn them into facts. The experiments can be a series of questions you ask customers but most often a minimal viable product to help potential customers understand your solution accompanies the questions.

So another big idea here is startups are not building minimal viable products to build a prototype. They are building minimal viable products to learn the most they can.

Finally, the goal of designing these experiments and minimal viable products is not to get data. The data is not the endpoint. Anyone can collect data. Focus groups collect data. This is not a focus group. The goal is to get insight. The entire point of getting out of the building is to inform the founder's vision. The insight may come from analyzing customer responses, but it also may come from ignoring the data or realizing that what you are describing is a new, disruptive market that doesn't exist, and that you need to change your experiments from measuring specifics to inventing the future.
Lessons Learned

Build, Measure, Learn is a great improvement over Waterfall product development and provided the framework to truly join the customer to agile development

However, emphasizing "Build" or "Ideas" as the first step misses the key insight about a Lean Startup - you are starting with hypotheses to be tested and are searching for repeatable and scalable business model

Hypotheses, Experiments, Test, Insights better represents the Lean startup process:
Use the Business Model Canvas to frame hypotheses, Customer Development to get out of the building to test hypotheses, and Agile Engineering to build the product iteratively and incrementally

Filed under: Business Model versus Business Plan, Corporate Innovation, Customer Development, Lean LaunchPad, Teaching |

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Why Build, Measure, Learn - isn't just throwing things against the wall to see if they work - the Minimal Viable Product

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I am always surprised when critics complain that the Lean Startup's Build, Measure, Learn approach is nothing more than "throwing incomplete products out of the building to see if they work." Unfortunately the Build, Measure, Learn diagram is the cause of that confusion. At first glance it seems like a fire-ready-aim process.

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The core idea of the Lean Startup movement is to validate your business ideas with customer experiments/tests before scaling them. We came up with a simple tool to better design those experiments/tests, because we weren't good at it in the early days of Strategyzer. Watch the video in this post to learn about the Test Card.
At Strategyzer, we talk to customers a lot and continuously conduct experiments before we launch something new. Yet, in the early days, we didn't learn as much as we should have despite talking to tons of customers. We were all over the place. That's why we invented the Test Card for ourselves.

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The core idea of the Lean Startup movement is to validate your business ideas with customer experiments/tests before scaling them. We came up with a simple tool to better design those experiments/tests, because we weren't good at it in the early days of Strategyzer.

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How to Use the Validation Board to Test Your Startup Idea

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Get your free copy of the Validation Board here: http://validationboard.com The Validation Board is a free tool to test your startup idea without wasting time or money. After launching on PandoDaily, TheNextWeb, & Betabeat, thousands of entrepreneurs have used the Validation Board to build better products. What is the Validation Board?

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bol.com | Value Proposition Design, Alexander Osterwalder | 9781118968055 | Boeken

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Value Proposition Design (onbekende bindwijze). The authors of the international bestseller Business Model Generation explain how to create value propositions customers can't resistValue Proposition Design helps you tackle a core challenge of every business - creating compelling products and serv...

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The Unofficial Startup Real Book Explained in 7 Minutes

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Figuring out which experiment or research we should run starts by laying out our assumptions and learning priorities. The Unofficial Startup Real Book helps us organize our assumptions and learning priorities by asking two key questions: - Which type of information are we gathering: Generative or Evaluative?

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46 ways to find your first customers or community members