Category Archives: Business Model Canvas

What is Validation?

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What constitutes validation is one of the first, essential questions we ask in our work with entrepreneurs using the Business Canvas Model (BMC). We also discuss value proposition, targeting customers, and product/market fit. However, validation for all of the components in the Canvas is never really defined.

Validation is the ultimate goal of the process using the BMC. Through validation we are looking to determine the sufficient number of paying customers that creates a market adequate enough to create a business opportunity.

Entrepreneurs do not have the luxury of knowing how many paying customers they have when beginning to pursue an opportunity. Many teachers of entrepreneurship, including Steve Blank and those at the Innovation Corp program at the National Science Foundation, claim that an entrepreneur needs to talk to at least 100 customers in order to reduce the uncertainty surrounding a startup. In fact, the more confirmation achieved, the less the uncertainty. The specific number of 100 is likely derived from qualitative studies performed by social scientists who claim that a population of 100 in a survey makes for a valid survey. In reality, most entrepreneurs find that after talking to about ten customers, the results tend to be the same. So, what constitutes a sufficient number of interviews?

In the past, I have stated that entrepreneurs should interview as many people as necessary to confirm the valid, the uncertainty of the market to a comfortable level. Of course, that omits the concept of confirmation bias. Entrepreneurs need to be mindful to avoid thinking: “My invention is great, so I’ll do anything to make the numbers believable.” Do not fall prey to your own lies, damn lies and statistics.

In research methodology, validity is the soundness of the design of each test and methodology used. Validity shows that the findings/results truly represent the phenomenon claiming to be measured. We cannot talk about validity without discussing reliability. Can the test be repeated or replicated with another population and obtain similar results? Is the test inherently repeatable?

Entrepreneurial validity means using good methods to test hypothesis obtaining data with observable facts that can be measured and are relevant. In addition, the test results must end up with a binary result. The test either passes or fails. There is no “close enough” response. As Yoda says, “Do or do not. There is no try.”

During hypothesis testing, an entrepreneur must “draw the line in the sand.” Ask whether your metric provides you with a level of success that gives rise to doubling down and taking the next action step? Can you tell the difference between complete failure and overwhelming success? Where does your opportunity fall?

What happens if you land close to the line in the sand but do not pass over? There are two possible scenarios: One would come from the norms of your industry. In other words, understanding how your competitors view this metric would provide the necessary knowledge to take action. The second should come from the business model. How many positive responses are required in order to be successful?

Now that you understand what validity is and why it’s important, make sure that you understand exactly how to test for validity. Testing for validity must correspond to the extent to which a concept, conclusion or measurement is well-founded and corresponds accurately to the real world.

 

Restart – Both the Blog and our Program

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I’m back from a break in blogging with renewed optimism and an excellent new staff at the Advantage Accelerator. In reviewing our program over the past two years, I note we’ve had a number of successful ventures, but still find issues relying solely on Lean Launch Pad methodology.

The greatest issue is the time it takes entrepreneurs to acquire a product market fit. Product market fit is vital because it shows customer validation and the discovery of a repeatable sales process. In order to develop a successful fit, the entrepreneur must focus on finding a reasonably sized market for growth.

Our current Accelerator program runs five months and operates as an expanded version of the Lean Launch Pad. Many entrepreneurs successfully complete the Accelerator program in record pace; others struggle with product market fit that may cause a client to struggle. Finding the best response to the iteration or pivot while learning new skills can cause the most seasoned veterans of startup cultures to stumble.

The Lean Launch Pad model requires that product market fit must be validated. If product market fit is false, then the operational side of the startup is invalid. Moving forward into the execution side of the canvas would be wasteful based upon an invalid product market fit.

After considerable thought, we will now be dividing our current five-month accelerator program into two parts: “Accelerate” and “Launch!” These programs will be offered in addition to our existing pre-Accelerator program, “Iterate!”

Iterate is a pre-accelerator program that is focused on problem/solution fit. The program has four, 2-hour workshop sessions with outcomes based upon entrepreneurial thinking, value proposition, an introduction to the Business Model Canvas as a tool, a basic understanding of the customer discovery process, and an introduction to hypothesis testing and validation. These are all necessary tools designed to help understand the hard work in the process of becoming an entrepreneur.

The program homework is optional (hint: but it is a great screening tool for us) and all team members are strongly encouraged to attend all workshops. In order to move into the next phase, Accelerate, and gain acceptance into the Accelerator, there is an expectation of progress, hustle, and “grit.” These qualities are not the only qualifications for acceptance into the Accelerator, but they are helpful considerations in our decision-making.

Accelerate is focused on product/market fit. The objectives of the program are to confirm the entrepreneurial opportunity, define and build a minimal viable product, validate product market fit, complete a first sales call and develop a repeatable sales model, complete building the team, and to be ready to execute and build operations. The program is eight weeks and is backed with significant resources including our intern program, mentors, and first looks by our Executives in Residence and early investors.

Entrepreneurs may repeat the Accelerate program twice if an iteration or pivot is required. A major focus of this program is to reduce the uncertainty of the startup through validation of product/market fit. Successful clients may move forward to the Launch! Program.

Launch is a five-month program focused on taking the company to the build and sell level. There are two major goals: Fulfillment of build and execution on an operation growth plan, and realization of a repeatable sale process. Each month’s program is focused on a specific topic toward a deep dive into a milestone based growth plan. There is work for each week of the program that starts with the introduction of a topic, private coaching sessions, a cohort workshop and roundtable with the last session of each month being a formal Advisory Board session that consists of our senior programming staff, Executives in Residence and any mentor or advisor to the company.

Overall, we feel that separating the completion of product market fit until the concept is validated makes more sense than attempting to move on to build a company with less certainty of success.

Lessons Learned

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I recently returned from a three day educators workshop on the Business Model Canvas (BMC) and Lean entrepreneurial process. There were a number of important points from the workshop that served as reminders for me, and I thought this should pass them on.

People, money and knowledge are the three ingredients that bridge opportunity and value. I think of these as the triangle of success. Miss one of these items and your startup is destined for failure. A caveat about money: In this success triangle, consider your customers. Customers lead to money, not the other way around.

Story telling is one way entrepreneurs can convey their value proposition. All entrepreneurs should become good storytellers. Everyone remembers a good story, but when was the last time you remembered a great statistic? Entrepreneurs need to understand the nature of a good story arc and bring that arc into all their pitches and conversations. People remember stories – make yours a good one.

Business Model Canvas (BMC) is founded upon evidence-based entrepreneurship. The Business Model Canvas is uses the scientific method to reduce the uncertainty in a startup. Get out of the building and get the facts. Facts are the evidence that will lead to better results.

It is okay to receive affirmation for being wrong. Stay true to the lean process. Fail fast and reduce uncertainty. Either the hypothesis is valid or not. An entrepreneur will do an injustice if they try to justify their results to meet expectations.

In an educational setting or classroom, the focus of BMC tends to be on the product market fit because many individuals going through the program are not ready for the strategic partners, activities and resources sections. This may be true in the classroom, but in our accelerator companies must be ready to launch. All nine components of BMC are important. There two basic section of BMC: The front end deals with product market fit and the back end deals with startup operations and activities. The product market fit must be established and validated before contemplating the operations and activities of BMC. Based on my participation in this workshop, I can see how a two section approach to teaching the BMC could be valuable.

There were other lessons learned as well and will be incorporated into our next cohort. A few improvements will be adding videos for client portfolios, an increased focus on understanding customer archetypes and graduating our clients with an eleven item portfolio.

Hypothesis Testing For Entrepreneurs

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Hypothesis testing appears to be a simple task. Just write down a question, devise a methodology to test it, elicit a response and analyze the results. Some entrepreneurial experts suggest that these tests must be pass or fail. In other words, either the hypothesis is true or it is not. In my experience pass/fail questions created without consideration of other factors is not effective.

For example, Team A reports: “Well, we thought we would get a 50% hit rate, but only got as high as 38%. That is good enough. We pass the test.” Did Team A pass the test?

The first two rules of entrepreneurship are (1) to be honest with yourself and (2) learn from your mistakes. Team A just violated both rules. First, they justified their projected hit rate and were not honest with themselves about what that really meant to their company. Secondly, they didn’t learn from the exercise. They never found out WHY they only had a 38% hit rate, rather than their predicted 50%. This is a terrible, missed opportunity. Why did they originally believe that they could get 50%, and why didn’t that occur? What needs to be changed? Can it be changed? Is it the test or the product? There are too many important questions in this scenario that will never be answered.

One interesting model for creating a more quantifiable hypothesis testing is the HOPE model. This model looks at four factors:

Hypothesis: What is your theory? Is it both “falsifiable” and quantifiable?

Objective: Are your tests objective rather than subjective?

Prediction: What do you think you will find?

Execution: How are you going to test?

The most important element of creating a hypothesis is that it must be “falsifiable.” That means your guess can be rejected after an initial experiment of the hypothesis. If your plan is to see what happens, then your hypothesis will always be true.

Second, all hypotheses should be quantifiable. In other words, you must be able to predict, account, and analyze your results. A good hypothesis includes both a question and good methodology to uncover the results. After determining the question and developing your methodology, you should then run a test to analyze the information obtained.

Additionally, your tests must have a good source of data, as well as represent your demographic population as accurately as possible. Your results should be objective rather than subjective.

Conducting good tests is a subject unto itself, and requires a more lengthy discussion than this blog entry addresses. I will save that for another day.

In my work with both scientists and entrepreneurs, the predictive element is often missing in hypothesis testing. This is even true of scientists and economists who use hypothesis testing on a regular basis. Included within a good hypothesis test must be a predictive indicator of the results. A predictive indicator might include how fast an event might occur and whether there are any stress points in the experiment and where the stress might be located. I believe that failure to quantify your results may mean that the hypothesis is not completely tested, and the result is incomplete. However, if you place a value or a number in the hypothesis, you can learn more about how close you came to hitting the mark.

Without quantifying hypotheses there is a tendency to justify the data to fit the results. In analyzing the results, teams need to be careful to differentiate between causation and correlation. For example, more ice cream is sold in the summer. More people drown in the summer. Therefore, they must be related. Of course, they are not.

Scientists and statisticians also discuss null hypothesis—a hypothesis that is assumed to be true, (e.g. in a courtroom, the defendant is presumed innocent until proved guilty) as opposed to alternative hypothesis—a statement that contradicts the null hypothesis (e.g., the courts would rather the guilty go free than send innocents to jail). What I am advocating in statistical terms is a criterion of judgment based on probability in quantifiable statements. For example, in the courtroom jurors would be asked to determine “beyond a reasonable doubt” whether the defendant is guilty.

So, in your hypothesis testing, will your test confirm beyond a reasonable doubt that your hypothesis is true? If you tested correctly, then you know the honest answer and just reduced the uncertainty of moving forward with your enterprise.

Decision Making with Data and Measurement

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As many of you know, the mantra for the Business Model Canvas is to get out of the office and interview customers, partners, channels and others. In fact, talking to experts and potential customers is the only true way to reduce uncertainty and to study the value of a product or service. In fact, I believe that it is the basis for all relevant qualitative research in entrepreneurship. As I work actively with the Business Model Canvas, I am convinced that getting out of the office and into the world is only the first small step in the entrepreneurial journey.

Real world data collection and analysis is a key component to reduce the uncertainty of a startup. The starting point is to understand how much is currently known about the problem and what is it worth. What decision will this measurement help us make? Is this an important enough decision to collect more data? Otherwise, what is the value in measuring? Will sufficient additional information be gained from the measurement exercise? If not, why then why bother to measure? What additional value will the measurement add to help with the decision? All of these are crucial considerations. The starting point should not be an identifying what is to be measured, but a reflection of why the measurement is necessary.

The next issue in data collection is to decide what creates a good metric to measure. First, a good metric must be (1) understandable and comparative (shown as a rate or ratio), (2) important to collect and (3) lead to an action directly related to the original required decision. Thus, the results of the data collection should relatively easy to collect, consistent, usable, and can capture information that is relevant to the company.

There are a few simple rules to help an entrepreneur get stated with data. The first set of data is usually exploratory for a startup. Exploratory research means it is okay to through darts. Use the shotgun, throw spaghetti against the wall, see what sticks. At this stage, exploratory data may not have specific decisions for collecting data other than the process of elimination.

The next rule regards checking the data collected and making sure that the right questions were asked. Was the variance of the sample population diffuse enough to provide a good sampling? Did outliers have any effect on the results? Were any assumptions made or any context involved that might invalidate the test?

Another question to ask about collected data is whether it constitutes a leading or lagging indicator? Leading indicators are indicative of future events; lagging indicators follow the event and advise what happened. Also, consider whether the data represents a correlation or causal relationship? A correlation does not mean that one variable or change in variable causes the other. A correlation only indicates that a relationship may exist or not. There just may be some type of association. On the other hand a causal relationship or  “cause and effect” means that is, a relationship between two things or events exists if one occurs because of the other.

Measurement tools and data analytics will not bring perfect decisions, but good and appropriate measurement may reduce uncertainty with significant decisions. While hypothesis testing is important in building an effective canvas, it is also important to use suitable and valid measurement tools ( the specifics of these tools will be another blog post).

Here are a few good resources to assist in the development of data skills:

How to Measure Anything Douglas Hubbard focuses on measuring intangibles—the value of patents, copyrights and trademarks; management effectiveness, quality, and public image.

Lean Analytics Alistair Croll and Benjamin Yoskovitz takes a good look into the quantitative side of measurement specifically directed to entrepreneurs.

How to Start Think Like a Data Scientist Thomas Redmond writes a brief NBR article on getting started.

An Introduction to Data-Driven Decisions for Managers Who Don’t Like Math Walter Frick on why data matters.

Start with the Business Model Canvas – Not Yet

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We at the Oregon State University Advantage Accelerator are big believers in the Business Model Canvas methodology. We use the Canvas, we also use software for the Canvas, and we make our clients read the books by Steve Blank and Alex Ostervalder. However, the fact is that Canvas may put a technology entrepreneur at a disadvantage before he or she gets out of the lab.

Our clients at the University tend to be in the early stages of their development. Most of our researchers are doing cutting edge research. The entire set of potential opportunities for these clients have not yet been examined. Under normal circumstances, using the Canvas, clients would start with one or two potential target markets then try to validate the opportunity.

I suggest that this may not be the best way to begin. One of the tools, we use at our Accelerator is the opportunity matrix. The founder or Principal Investigator (PI), my co-director, mentor(s), intern(s), and I brainstorm on the possibilities of applications and industries in which this innovation can be productized. We also look at the numerous variables that could affect market entry. This tool was originated by the strategist Igor Ansoff and there are many versions found online.

The matrix provides focus and guides decision making prior to a long course of validating tests as required by Canvas methodology. Along the y-axis, we list the potential products and/or industries in that the innovation may be successful. Along the x-axis, we list variables such as size of market, ease of entry, competitive response and so on. The list of variables can be quite large and is on my version. The purpose is to determine through online research, phone calls with industry experts, which industry or market should be the top areas of concentration, which then becomes the business focus. This leads to a much clearer start on the Canvas.

The technology also needs to be checked for the opportunity as well. We have a great spreadsheet that checks on the viability of commercialization for the technology. It is similar to the opportunity matrix in that the various markets or projects are listed on the y-axis and a number of strategic questions about commercialization of technology are listed with weighting scores along the x-axis. This is another easy way to envision the technology side of the opportunity. Send me a note and I will send you either matrix.

These pre-cursors to the validating steps in Canvas will shorten the steps from hypothesis to validation testing.

It is highly likely that an entrepreneur will save time and money by doing the secondary research up front. This also creates a more focused entrepreneur who can easily begin the primary research work on Canvas.

There are a number of other activities that we take our clients through before beginning to work on Canvas. But overall, in the early assessment stages, we are seeking feasibility. Is the technology feasible within the means of customer wants? Does the business proposition make sense both in terms of its ability to succeed and financial viability?

Overall the big questions in this stage are:

  • Do I have a technology that has potential applications in the commercial market?
  • Are there customers and a market of sufficient size to make the concept for this technology viable?
  • Based on estimates of sales and expenses, do the capital and other resource requirements to start make sense? And;
  • Can you create an appropriate start-up or management team to execute the concept?

Just like in the Canvas, the answer to all the above questions is not that you believe the response, but rather, I know my response is true and here is why.

This early work provides sufficient data to understand the industry, examine an early value chain and process flow, understand your potential first and/or second market, organize yourself for validation of market(s) and get an early justification of pricing.

As I stated above, the secondary research requirements will enhance the primary research efforts required by Canvas. Go in smarter and ask better questions in order to obtain better results.

Out-Of-The-Building Blocks

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I had the opportunity to chat with Steve Blank about the Business Model Canvas and his Stanford class. I suggested that there is much work to be accomplished even before embarking on the Lean Launchpad Class. When I taught at the University of Southern California (USC), the cornerstone class before the Business Plan class was the Feasibility study. In fact, there were two tracks for entrepreneurs: one for technology and another for other businesses, and each had separate feasibility and business plan classes. (Disclaimer: I am not a believer in the traditional business plan, but rather focus on the operational and business-building sides of startups.)

I adopted many of the topics in both courses as a precursor to the Lean Launchpad class for mechanical engineers at Cal State Los Angeles, which I developed. When I taught at USC, the feasibility class was part of the MBA program. Therefore, many of the entrepreneurial and business concepts were already familiar to many of the students. However, the engineers at Cal State had no experience with business and entrepreneurial terminology and concepts.

I found the key to starting a new feasibility class for engineers was to introduce a few creativity concepts and exercises as a way of enriching their way of thinking. Engineers tend to think in a very linear fashion, and are sometimes uncomfortable with ambiguity in business. They weren’t as interested in variations in valuations or marketing—they just wanted the formula.

At some point in any business process, the research stops and the marketing begins. I am also a big subscriber to effectuation – the concept that entrepreneurs must think differently than ongoing businesses; using evolving means to reach new goals. Startups that are resource poor can’t throw money at a problem in order to make it sellable. A large business may be able to afford such a play, but not a startup. Entrepreneurs must use a different toolkit and a different way of thinking in order to succeed. Both effectuation and the business model canvas provide those tools.

In my entrepreneurship class at USC, students were required to meet 25 strangers. Steve Blank’s classic line is “to get entrepreneurs out of the building.” Entrepreneurial knowledge can only be gained through meeting people in their industry, advisors, and anyone else who can help budding entrepreneurs think through the parts of their business, how they compete, what niche does their competitor target, and just about everything required to be known before thinking about spending a dime.

So what kind of research could an entrepreneur do before starting on their entrepreneurial journey? This is secondary research to be completed before getting out the door. This needs to be done before to make the primary research (getting out the door) more valuable and shorter in duration. After all, having better hypotheses will yield more confirmations:

  1. Know thyself. What are you good at, who do you know that can advance the concept, and why is this an important undertaking? Can you articulate the value and benefits? Is the opportunity clear?
  2. Know the industry. Every aspect of the industry, every competitor and their niche, how would you find the pattern of change in the industry to become the leader of the pack, who are the major suppliers, can you successfully target the underserved market?
  3. Know the value chain – who gets paid and how, can you gain access to critical supplies on good terms? Do you know every step of your business process from order taking to fulfillment and post sale customer service? Where is the most value added?
  4. What is the best way to reach the customer? What effects their buying decisions?
  5. Who are your target customers and their demographic data? Are they big enough to constitute a market? What color underwear do they wear? Yes, you need to know everything about the client down to their undergarments.
  6. Financials – Don’t even think of continuing if you can’t do a spreadsheet forecast called a pro-forma. We all know the spreadsheet is a work of fiction, but how can you guestimate the entrepreneur’s bet? What are the premises underlying the data on your targeted financials? What is the delivered cost of the product? What are the multiple streams of revenue? Get the premises right and be convincing to yourself.

Possibly the most important skill to teach engineers and principle investigators is a little marketing and a whole lot of sales. Let’s face it; with few exceptions scientists are not famous for being extroverts. We need to impart to them that sales is not a shady profession, but rather an exercise in building relationships and helping potential clients solve problems. Good sales and marketing skills help build relationships allowing engineers to fix the world and its problems. Sales and engineering make for a good match.

However, relationship building is sometimes difficult for engineers and principle investigators. Social engagement must be built into all classes. In my class, everyone had to do a one-minute sales pitch that you could give someone in an elevator—a classic elevator pitch. After the first three or four intrepid students made their pitches, the whole class understood the key elements. Not everyone ended up being comfortable with selling, but then again not everyone will become a CEO.

So remember that before jumping into the Business Model Canvas, there is still much thought and research to put into your plan. If you are able to do the homework before embarking on validation tests in the canvas, my hypothesis is that you may have better information through good secondary research techniques before embarking on validation testing in each of the nine building blocks.