Category Archives: Decision making

Opportunities

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Our new program, Accelerate, asks entrepreneurs to be mindful of opportunity recognition in a number of ways. Technology researchers and developers often see many options to pursue. An important goal for very early stage entrepreneurs is to ideate as many possibilities for their technology in order to determine as many potential products or markets as possible. Then, with the use of a few tools and secondary research, they need to narrow their focus to only a few reasonable and potentially profitable choices.

At our Accelerator we start with two tools: One is to examine the technology opportunity and a second to represent the business opportunity. This approach helps provide an opportunity to examine the scope of the opportunity and make better choices. The narrowing of opportunities is usually represented by easier adoption rates, shorter buying cycles, and leverage to a larger market.

With this data in place, we have our interns dig in and research the market and industry both on a macro and micro basis. On a macro level, we assess the market size and industry attractiveness. Markets are composed of groups of buyers so that determining which groups compose the Total Available Market (TAM), Served Available Market (SAM) and Target Market (TM) is important to determine which should be pursued. Also, by definition, an industry is a group of sellers that represent potential completion. As it turns out, some markets and industries may be clearly more attractive for an individual startup to enter than others. On a micro level, industry attractiveness is, in part determined by the competitive response and current benefits offered by competitors. On the market side, we focus on whether the real or perceived benefits a startup offers are better, different, faster or less expensive than the competition. We ask whether a startup can create a perceived differentiation of improved benefits or costs in the minds of their target market?

We examine trends in both the startup’s market and industry. Is the startup ahead of the trend analysis? If it is too far ahead it becomes difficult to sell the value and benefits that are offered and the market will likely not see the need. The dead pool is littered with products and startups that never materialized. Some examples include Webvan, Ask Jeeves, Pets.com. If it is too far behind in the trend analysis, the startup will never catch up. The goal is to ride the wave of each trend with a base of intellectual property that protects the opportunity and stalls or slows the competition. We also investigate the economic and social forces potentially impacting the startup. Is there a political or regulatory change? Is there a technological advance that provides a customer desire? Do you have the window of opportunity? Good entrepreneurs know that timing is everything.

In the early stage, an entrepreneur needs to access an ability to execute on a potential opportunity. However, validation of the opportunity, product market fit and Business Model Canvas must be present while confirming that financial viability is likely.

The Short Rules of Entrepreneurship

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Today I offer you my personal rules for entrepreneurship. This set is by no means complete, but they are hopefully food for thought.

Rule 1: Entrepreneurship is about action – The Captain’s chair is yours.

Rule 2: Some people dream about doing great things. Keep your eyes wide open and your feet on the ground, and then do great things.

Rule 3: A sports metaphor for Rule 2. There is no crying in baseball. There is no sleeping in entrepreneurship.

Rule 4: Focus on customers and building a business that is client centered rather than focused on technology. Make sure your company solves customer pain or creates great results for your customer.

Rule 5: Entrepreneurship is not fair. Neither is angel or venture capital funding.

Rule 6: Treat your 3F (Friends, Family and others (known as Fools) round as if they are professional investors. All 3F angel investors have an investment committee – their spouse. Respect the relationship.

Rule 7: Angel and venture capital is like a series of locked doors. Someone must unlock the door for you. Find the people with keys.

Rule 8: Build your company by building a customer base. Build for one client at a time. Later, build for multiple clients.

Rule 9: You have exactly one minute to make your pitch. Practice making them.

Rule 10: Learn the language of entrepreneurship and tell the truth. (Do not tell the typical lies: “Our market cap will scale up to $27.1 billion in five years” or “our competition is too big and slow to move as fast as us,” unless you are Elon Musk and build spaceships in your spare time.)

Rule 11: Get a champion who will work with you.

Rule 12: Bootstrap. Frugality is a virtue. Put some skin in the game.

Rule 13: You are only as good as your cash.

Rule 14: So what? Why you? What have you accomplished so far?

Rule 15: Build a team. One person cannot do everything.

Rule 16: Be a good listener and a better filter.

Rule 17: Network!

Rule 18: If you build it they will not come. You must sell to them.

Rule 19: Never BS yourself or your team. Always pause to understand the bias in all decision making.

Rule 20: This rulebook is incomplete.

Do you have additional tips for entrepreneurs? Feel free to add to the list.

Managing Risk and Managing Decisions

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Early in my career I worked in investment banking and international banking. So, later on when I taught finance I reflected on the issue of risk, and the basic elements of financial risk that all business ventures should consider:

  • Understanding the types of risk;
  • The fundamentals of risk; and
  • Managing the risk.

The fundamentals of risk management involve:

  1. Identifying the risks
  2. Measuring the potential impact
  3. Deciding how each risk should be handled.

In a recent Harvard Business Review article on business model innovation, I noticed a commentary on marketing. This particular article contained a major section about when to make key decisions and identifying who should make those decisions. It occurred to me that the elements of risk are almost identical to the innovation model. Both focus on reducing risk in a venture.

In a past blog I discussed the garbage can model of decision-making, and focused on being novel in decisions. Today, I am looking at reducing the risk and uncertainty inherent in any startup.

Risk identification is a process that systematically and continuously identifies current and potential risks that might have an adverse affect on a startup. The impact of the risk is affected by both frequency (lots of events) and severity (potential big losses). Most companies don’t worry much about frequent small losses. Office supplies disappearing or the local candy store missing a few small low cost items are two examples of a small loss. However, with severe losses, many organizations take precautions to protect events from occurring. Examples here might include large ticket items missing in the isles of retailers or a few items being chained down so that only department managers can help you try on the expensive goods.

In decision-making, the risk inherent about when key decisions should be made is often due to the lack of sufficient information to reduce the uncertainty. Strategies to deal with this issue may include:

  1. Postponing the decision. Sometimes decisions appear urgent but are not.
  2. Splitting up the decision into a series of real options. Break the decision into small bite size pieces this reducing any significant investment.
  3. Changing the order of decisions. Sometimes a client may ask for customization that may not benefit the strategic direction or value of the organization. In response, the startup can change the sequence to only payment upfront or with proof of performance before investing in the customization.

The CEO may not always initiate all important decision-making. Empowering employees is a very effective way to deal with every day minor decisions. If the decision is too important to delegate, then another way to manage the risk is to consult with a board member or trusted advisor. Even then, always try to find the HIPPO (the industry’s Highest Paid Person’s Opinion). Delegating up or outsourcing may be the best option when dealing with risk issues.

Managing risk and decisions can also be accomplished through insurance, or outsourcing. Startups often hire distributors and/or transportation companies to take on the logistics of moving product not only because it is less expensive but also because these outsource companies have the know-how to manage these specific operational risk.

In startups there are other types of identifiable risk that may include market risk, supplier risk, default risk by clients and others that are usually addressed in the planning process. Managing risk and managing decisions travel in lock step with similar processes. However you manage risk, make sure the process is around a strategic framework and one that allows for continuous monitoring.

Altruisms

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Words for thought. I’m taking a brief respite from serious writing to offer you some of my favorite lines about entrepreneurial thinking.

The true economic stimulus exists in the entrepreneurial spirit.

Customers buy success, entrepreneurs sell benefits.

There is a fine line between perseverance and obstinance. Entrepreneurs need to know when to adapt and change direction.

Tom Hanks once said there is no crying in baseball. Entrepreneurs know that there is no sleeping in startups.

The faster you drive a car that you don’t know how to drive, the more likely you are to crash.

Killing time murders opportunity.

Spreadsheet: a matrix showing how many days of the month you have to eat PB&J sandwiches

Spreadsheet (2) – what startups do before they bed down in their office

Messaround Round: Venture capital obtained by a company that really doesn’t need the money but wants it just to “make sure” of things (& then they promptly spend it on ill-advised items).

Dude Diligence: Investigating the one-owner, one-person business (or dudette diligence, in the feminine).

Small Business Disvelopment Corporation = a very poorly managed SBDC.

Non-intellectual Property (NP) = an invention that’s, let’s face it, not very good.

Entremanure: A client whose business plan stinks

Benchmarks: Sweat left at the gym while avoiding facing issues in your business.

Innervation: Tremors and sweating associated with starting a new company

Fornivator: Someone who screws up your program, as in, “The county commissioners really fornivated us in the new budget.”

An entrepreneur is one that leaves a 9-to-5 job with a steady paycheck, vacation and sick time with limited responsibilities in order to become an owner of a business, working 24 hours a day 7 days a week with uncertain income, no vacation and placing their life savings and family time at risk, all in the name of personal freedom.

A mentor is a person whose hindsight becomes your foresight.

Entrepreneurship is rarely a do it yourself sport.

There is no finish line in entrepreneurship.

Remember that you are unique, just like everyone else.

Learning happens when you have the courage to invalidate your hypothesis.

If you have the data then let’s look at the data. If all we have is opinions, then let’s go with mine.

Instincts are experiments. Data is proof.

Markets that don’t exist don’t care how smart you are.

Finally, wrapping up with a quote from Theodor Geisel,  AKA Dr. Seuss:

“Congratulations!
Today is your day.
You’re off to Great Places!
You’re off and away!

You have brains in your head.
You have feet in your shoes
You can steer yourself
any direction you choose.
You’re on your own. And you know what you know.
And YOU are the guy who’ll decide where to go.”

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.

Garbage In, Garbage Out: The Garbage Can Solutions Model

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The children’s story “Alice in Wonderland clearly identifies the paradigm of the Garbage Can Solution model. When Alice meets the ever-elusive Cheshire Cat they have this conversation:

‘Would you tell me, please, which way I ought to go from here?’

`That depends a good deal on where you want to get to,’ said the Cat.

`I don’t much care where–‘ said Alice.

`Then it doesn’t matter which way you go,’ said the Cat.

`–so long as I get SOMEWHERE,’ Alice added as an explanation.

`Oh, you’re sure to do that,’ said the Cat, `if you only walk long enough.’

The question is if SOMEWHERE is the right place.

“Entrepreneurship is […]a way of thinking that emphasizes opportunities over threats,” according to strategic thinkers such as Krueger, Reilly and Carsrud. The “somewhere” alluded to by Alice can be either threatening or opportunistic for an entrepreneur. It can also be both. The trick is to know the difference.

The “Garbage Can Model” is one tool that is often used by entrepreneurs. The garbage can model is one where all of the entrepreneur’s historical decisions and solutions are thrown into a metaphorical can. When a problem arises, the entrepreneur is able to reach into the can to find a solution to their current problem. We see this often with serial entrepreneurs who look to their past success to solve a different set of problems. This has also been referred to as the sophomore jinx.

Traditionally, the Garbage Can Solution model describes the accidental or random confluence of four streams. A number of academics believe that decision making occurs in a random meeting of: choices looking for problems, problems looking for choices, solutions looking for problems to answer and decision makers looking for something to decide.”

In fact, one well known academic questions the validity of this particular model when she asks, “Does the garbage can model describe actual decision making or is it simply a labeling of the unexplained variance of other, more powerful, descriptions of strategic decision making?”

Additionally, does the garbage can take into account our existing bias based decisions? If we fall back on choices that worked for us in the past, does that mean they will work for us today? Do we need to solely rely on what is currently in our leaders’ bag of tricks to creatively develop new ideas, solutions, or products?

In more establish organizations, famous social scientists Cyert & March tell us that “Exogenous, time dependent arrivals of choice opportunities, problems, solutions and decision makers” are thrown together so that any solution can be associated with any choice. Never a good way to approach decision-making. What they are alluding to is that solutions, problems are often thrown together from previous experience with the hope that the right problem hooks up with the right solution. With unlimited resources and time, this may result in relevant information.

However, is the time-constrained, resource scarce environment of the entrepreneur an appropriate place to utilize this model? This is exactly where entrepreneurs slip. In seeking repeatable processes, creativity is lost. All start-ups should look to the creative solution making process as much as possible.

The answer, as usual, is it depends. Although The Garbage Can Model is not a rational method of strategic thinking, there is significant research backing up this school of thought on decision-making. On first look, this is not a particularly creative approach, nor is it direct and focused on finding specific problems and solutions. By definition the Garbage Can model of decision-making assumes that nothing new is added. The only items in the can are what has already been done or considered. It is history rather than innovation that drives this approach.

However, the creative entrepreneur is not focused on what’s already in the garbage can, but rather what the entrepreneur could be doing to add to the can in order to make rational, novel, and strategic decisions. Unfortunately, for many entrepreneurs, the right solution never gets added to the mix of ideas, problems and solutions.

The best response for entrepreneurs is to find creative answers for their start-ups that are removed as much as possible from prior bias. In order to accomplish this, entrepreneurs must be exploratory and experiential, note boundary limits and consciously develop an environment where all parties involved in the project have a strong, relevant voice. This assures more team buy in to the project. Eliminate power plays and look for the important breaks in typical industry patterns.

So get out of the building, find customer data (however imperfect it may be) and go somewhere. Whether your team decides to dumpster dive or not, I will leave that up to you. However, you should be aware of the upside and limitations for utilizing this business model in your start-up.

A Meditation on Entrepreneurial Strategy

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Most of contemporary strategy literature is based on big company strategy. Larger companies focus their strategies on a cost based model because costs are within their realm of control. They almost never think about the revenue side of the equation. After all, one can control costs, but the customer controls your revenue. This is where entrepreneurial training differs, and provides a winning strategy.

If you think of strategy in terms of costs you can win only half the battle. A recent Harvard Business Review Article by Roger Martin, calls this The Big Lie of Strategic Planning. Martin clearly sees the entrepreneurial view, to focus on the sources of revenue, i.e., customers as the key element of strategy.

Henry Mintzberg called this differential—intended strategies versus emergent strategies. Entrepreneurs work in the emergent section, because they are very opportunistic about revenues. Good entrepreneurs learn quickly that you cannot control the future, but you can try to reduce the uncertainty in getting there. Strategists would call this the resource-based view of strategy.

Resource based strategy states that an organization should use the strongest competencies of a firm to determine a strategy. Entrepreneurs think about what they could be doing with the resources in hand in order to find the opportunities. The planning school holds the thought about what the organization “should be doing” corner of the spectrum rather than the “could be doing” corner. Other strategists might view this as the Blue Ocean strategy. Swim to where no one else is playing; find a niche where there is no competition. Entrepreneurial strategy might also fall into the Michael Porter School of Positioning strategy, which is very analytical.

For information of the various schools of strategy read Henry Mintzberg’s book Strategy Safari. Unfortunately, the entrepreneurial school has changed dramatically since the book was published and it shows less relevance for entrepreneurs. However, this book is recommended as a great summary on the various strategic schools of thought and it is still relevant today as a great primer on the major thought patterns in the strategy discipline.

A good strategy (or whatever term is used – mission statement, mantra, culture) communicates behavior to employees. This strategy communicates what decisions should be made and the boundary limits for what should be the focus of the organization.

Another way to determine and validate a strategy, entrepreneurs may prefer the VRIO framework as popularized by Jay Barney. Are you building something Valuable? Is it Rare? Can it be easily Imitated? And can your Organization implement on the concept?

Possibly, the most important considerations for a startup concern (1) whether a strategy is necessary, and (2) at what point does a strategy become necessary for an organization. Should every company act like entrepreneurs and be opportunistic? Early stage startups do not necessarily engage in long-term strategies. For them, it comes down to tactics and execution. Execution trumps all organizational strengths every time.

The bottom line is that entrepreneurs should talk to their customers. Entrepreneurs have a venture. A business is created when the product or service of the venture can reach at least twenty customers who will make a purchase at a price that provides sufficient margins. If the entrepreneur doesn’t have a product and a price then they don’t have a business…yet.

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.