Business Thoughts for a Friend

A friend of mine is looking to start a business. I’m meeting him tomorrow for coffee. I’ve been thinking this week about what to say to him as he’s getting started.

A Business is more than a job you’ve made for yourself

Often when people say they want to “start a business” they really mean, without realizing it, “I would like to be a freelancer that markets.” Graphic artists, photographers, writers, and others providing services are probably the most likely to be looking for this type of business. This isn’t a wrong or bad, in fact it can be very good.

The distinction remains important though. Unless the choice, business vs. freelancer, is considered, bigger possibilities may be missed.

My definitions:

  • A business provides value to customers in return for payment.
  • Freelancers get people to pay for a specific set of services they provide.

At first glance they seem pretty similar, but there is a very important difference in focus. The customer has the focus in the first example, the Freelance is the focus in the second.

When a freelancer thinks about their “business” only as providing graphic design, for example, they are limiting themselves to selling this specific service that they know how to do. The focus is on what they can provide, not on what the customer wants. If, instead, the business was defined as “providing communication services for medium sized manufacturing businesses” now a wider range of possibilities will reveal itself. The focus is on the customer and what would bring value to them. The number of potential ways to bring value to that customer are enormous.

It’s also important to note, the definition “A business provides value to customers in return for payment”  doesn’t specify who’s doing the work. As long as the value is delivered, it could be the founder, an employee, or a contractor. A business need not be limited by the skills (or lack) of its founder.

So “business” or freelancer, what’s the right answer for my friend? Neither, it’s up to him. But I’d like him to be aware that it is a choice and it could change the way he looks to build his business.

Why Businesses Fail. Cash flow is king

There seems to be a euphoria that overcomes founders when they startup a business. Success is right around the corner and the money will soon be flowing, they are sure. Sometimes this is true, but too many times this is not the case and spending eats through the foundation of the business.

I ran across a great set of statistics from on why businesses fail. They sourced the stats from a University of Tennessee study. It should be required reading for any founder. Learning from others is much easier on the bank account, psyche, and marriage.

Direct from the source, here are the top causes of failure:

  • 46% of failures are caused by “Incompetence.” Specific pitfalls: Emotional Pricing, Living too high for the business, Nonpayment of taxes, No knowledge of pricing, Lack of planning, No knowledge of financing, No experience in record-keeping
  • 30% of failures are caused by “Unbalanced Experience or Lack of Managerial Experience.” Specific pitfalls: Poor credit granting practices, Expansion too rapid, Inadequate borrowing practices
  • 11% of failures are caused by “Lack of Experiences in line of goods or services.” Specific pitfalls: Carry inadequate inventory, No knowledge of suppliers, Wasted advertising budget
  • Only 1% of failures are caused by “Neglect, fraud, or disaster.”

The top failure factor can be reduced at a simmer to: pricing and basic finance. Or even farther, to cash flow. Pricing problems reduce cash coming in (either lost business or reduced margins) and basic finance problems increase cash going out (no budgeting, paying fees, spending too much).

Pricing problems can be solved if the founder understands the value that the business provides (and some marketing knowledge helps as well). Good pricing is a generally triangulation between cost+margin pricing, market-based pricing, and value-based pricing.

Basic Finance problems are most likely due to self-control issues, management issues, and basic accounting competencies.

That’s It!

There are a number of other topics I’d like to talk over with my friend, after hearing about his vision. Topics such as business structure, marketing, business processes, and others. But these two seemed especially important.


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47% of jobs could be automated – A warning, opportunity, and vision

Just read an astounding study by a couple of scholars at Oxford who claim that 47% of current jobs could be computerized. That’s 64.0 million of the current 136.1 million jobs in the US!

It is a great read. They provide a lot of historical background on technology adoption and its impact on job creation/destruction. And the study itself provides a vision into a possible future as machine learning (ML) and robotics are improving rapidly. Computers can now complete tasks we once thought, not that long ago, they would never be capable of. Tasks like:

 Obviously this puts some jobs at risk and means there is probably a lot of change coming our way over the next few decades. Truck drivers (2,822,080 currently employed) and Bus Drivers (652,590 currently employed) should be working on their resumes. But even with amazing advances in technology there are some jobs that will never be taken over by a machine. Either because the cost outweighs the benefit and/or the work is inherently difficult to computerize.

The authors use three “computerization bottlenecks” to address this and help determine which jobs would be hardest to computerize:

  • Perception and Manipulation – Jobs requiring high dexterity or the ability to work in cramped or awkward positions will be harder to computerize. Surgeons should be safe.
  • Creative Intelligence – Computers will have difficulty doing work requiring the ability to come up with unusual or clever ideas. Artists and engineers should be safe.
  • Social Intelligence – If negotiation, persuasion, or assisting and caring for others are required, computers will be out of their element. Teachers should be safe.

Using these bottlenecks, the O*NET job description database, some hand coded training data, and predictive analytics they ranked 702 jobs by “Probability to be Computerized”.  You can see the results summarized in the figure below.

The Future of Employment - Figure III

(figure from the study)

So if you’re in Engineering, Art, Science, Business, Education, Legal, or Healthcare, you’re pretty safe. But those in Sales, Service, Admin, Production, and Transportation should probably be looking over their shoulders. Also, it’s interesting to note that employment has moved to the edges. Most jobs are either high risk (low physical, social, and creativity demands) or low risk (high physical, social, and creativity demands).  There is not much in-between.

The following chart is one I created to investigate the relationship between average income and computerization risk. It combines the paper’s probability ranking data with BLS income data, using Python and the matlibplot module. You’ll want to click to enlarge the image so you can read the text. It shows 685 jobs plotted based on their probability of computerization and average income. The linear regression trend (green line) shows a definite trend: jobs that are more likely to be computerized are already paying less, on average. The jobs with more than 1 Million people currently employed are labeled to give an idea of who is and is not at risk.

Mean Income vs Probability

It will be fascinating to watch as the job market transforms (as long as you’re not getting the Friday afternoon talks). Of course, we don’t know the timeframe for changes this big. It’s not all going to happen in a just year or two. And, you never know what sort of societal or regulatory hurdles could be put in the way to slow it down. The Oxford paper quotes someone named Mokyr on this point: “Unless all individuals accept the “verdict” of the market outcome, the decision whether to adopt an innovation is likely to be resisted by losers through non-market mechanism and political activism.”  This is understandable, those in high risk jobs aren’t going to want to give them up. If that is your livelihood, it is a scary place to be. Even if  you’re not threatened by these trends, it is strange to think about a software algorithm writing articles, doing legal research, or medical diagnosis

If you are in the position to invest in, start, or work for companies doing the computerization of these jobs, there is a lot of opportunity. To see how much, I looked at all the high risk jobs (greater than 70% probability of computerization) and multiplied the number of people currently employed by the mean income. The results is the “Opportunity” represented by the labor cost savings of automating all current positions. The top twenty opportunities are in the table below. Naturally, not all the jobs will actually be at risk, and it may not make sense to automate many of them. But, if you look through the list, you’ll probably think of companies that are already taking advantage of these opportunities. For example, retailers have been installing self-checkout to automate the retail sales / cashiers jobs and Kiva Systems is replacing hand material movers in warehouses.

Probability Description Opportunity
92% Retail Salespersons $109.8 B
85% Sales Representatives, Wholesale andManufacturing, Except Technical and Scientific Products $90.9 B
96% Office Clerks, General $82.2 B
94% Accountants and Auditors $80.2 B
96% Secretaries and Administrative Assistants, Except Legal, Medical, and Executive $70.0 B
97% Cashiers $67.5 B
79% Heavy and Tractor-Trailer Truck Drivers $62.8 B
98% Bookkeeping, Accounting, and Auditing Clerks $58.9 B
85% Laborers and Freight, Stock, and Material Movers, Hand $56.6 B
92% Combined Food Preparation and Serving Workers, Including Fast Food $55.1 B
94% Waiters and Waitresses $48.3 B
86% Executive Secretaries and Executive Administrative Assistants $40.3 B
97% Team Assemblers $30.1 B
84% Security Guards $28.5 B
88% Construction Laborers $28.1 B
96% Receptionists and Information Clerks $26.1 B
72% Carpenters $25.3 B
73% Administrative Services Managers $23.4 B
96% Cooks, Restaurant $23.3 B
95% Landscaping and Groundskeeping Workers $21.5 B

To sum up, this study holds a warning, an opportunity, and a vision. A warning to those with jobs in the high risk areas, an opportunity for those who can help make it happen, and a vision of what the world could end up looking like in ten to thirty years.

I’m not commenting on whether or not I think these things will make life, on average, better. A lot depends on what other opportunities are created for and by people during this time. But I do think it’s going to happen and there’s not much we would be able to do to stop it. Might as well get on board.

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Amateur Economics

I’ve been looking at a lot of historical economic data recently, trying to get a broader perspective on our current situation. I have two drivers:

  1. Desire to preserve my retirement capital over the long-term.
  2. Curiosity. Our economy is a complex system that I would like to better understand.

This post is mainly a data dump of charts with my brief observations.

Side note: Since I’m a marketer, not an economist or an investment adviser, please don’t take anything here as investment advice. The “real” economists don’t agree on what’s going to happen.
Side side note: Though, it appears that the more economists disagree the lower the stock market returns in subsequent periods (link).

Real GDP and Real GDP per Capita (shaded areas mark recessions)

Amateur Economics - chart 1

The US economy has had a incredible run over the last 80+ years, obviously there was a major dip in 2008 with the financial crisis, but the overall trend has been very strong. On average we’ve had a recession once every 6 years (since WW2), they’ve lasted an average of one year, and have caused an average of a -2.28% GDP decline. My takeaway from this chart is that I should expect to go through a number of recessions in my investing lifetime (average 7.5 recessions for the 45 years from age 20-65), it would be helpful to know how to preserve my capital through the troughs.

GDP per employed person and Number Employed

Amateur Economics - chart 2

The dip in GDP after 2008 has not been due to decreased productivity. GDP per employed person is very strong and has been increasing at a rate above the linear trend since ~2000. This seems to indicate that businesses are able and willing to quickly lay-off employees and keep their production in line with demand. It also indicates that employment is very important, you’re not going to be able to increase GDP without new jobs, it’s only slower processes like technological advancement that changes the GDP/employee relationship. The possible exception seems to be war (see below).

GDP per employed person and Number Employed (Red Bands are major shooting wars, Blue band is the Cold War)

Amateur Economics - chart 2w

GDP/employee seems to increase during war time, potentially due to defense spending.

% of Total Population Employed and Unemployment Rate

Amateur Economics - chart 3

From 1960 through 1990/200 there has been a consistent increase in %  employed. I expect this is mainly due to women’s lib. Since ~2000 this upward trend has stagnated and with the 2008 crisis has dropped back to 1985 levels. This could mean we have have a lot of upside once more jobs are created, however, our private industry has been shifting away from manufacturing towards services (see below).

%GDP from Goods Producing Industries and % GDP from Service Industries

Amateur Economics - chart 7

Our economy has very consistently been shifting from Good’s Producing (from 40% in 1940’s to 19% in 2013) to Service Providing (from 47% in 1940’s to 69% in 2013)

Real GDP and Debt as % of GDP

Amateur Economics - chart 4

This is a worrying chart. It looks like the recovery we’ve seen since 2011 has been fueled by debt. Naturally, it’s been in the news, we’ve been hearing about QE3 and we’re wondering what will happen when the Fed stops pumping in billions of $. I have no way to predict, but I’m worried that if fundamental measures like # of people employed don’t increase, we’re in for a very hard landing.

I’m going to be working on another one of these soon, there are some interesting stats buried in employment by industry and I want to look at Gov’t contribution to GDP vs. actual Gov’t spending.

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Context Specific Shortcut Reminder for MS Office (or any other program)

Much of my work day is spent in Excel and Powerpoint. I’m employed by a marketing consulting firm and part of my job is turning CPG data into clear, persuasive insights to guide clients and help them persuade their customers. To speed up the tedious parts of this task I have turned to AutoHotKey scripting and learning more keyboard shortcuts.

Remembering simple shortcuts is easy, but there are many out there that I’ve never used, or have forgotten. So I created a script that can remind me of the shortcuts available in any active program. If I’m in Excel, it will remind me of  Excel shortcuts. If I’m in Powerpoint, it will remind me of Powerpoint shortcuts. You get the idea.

Here’s video of it in action:

Let me know if you like the idea or have other ideas or improvements. If you want a copy, I’d be happy to send you one. This concept can be adapted for just about any program.

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What is Social Media?

A followup to my “What is Marketing?” video, this has been up on YouTube for a bit but I’ve neglected to post it here. This is a very quick (<2minute) introduction to what social media is, at its most core/basic.

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What is Marketing? – a succinct and insightful orientation to marketing and branding

I’ve been meaning to create videos on Marketing basics for a while now. My marketing workshop experiences have proved the importance of communicating the basics again, and I noticed a LOT of people are out there searching for “What is Marketing” and “What is SEO” etc. The video is 2 minutes long, covering marketing, customer lifetime value, and branding. Even if you are an expert, you’ll probably still learn something (or at least, enjoy the refresh).

Stay tuned for my next video, which will be on Social Media, I promise you that you won’t want to miss it.


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How to measure anything – book review and summary

I recently finished How to Measure Anything: finding the value of intanglible in business. It’s a great book, I highly recommend it. Especially if you have a engineering/science background and find yourself in management.

Hubbard claims that there are no intangibles that you can’t measure. Central to this claim is his definition of measurement as “uncertainty reduction.” This means that the point of measuring is not to find THE answer, but to reduce our uncertainty about what the answer is. He suggests starting with the estimate of” calibrated” experts who can consistently estimate within a 90% confidence interval. From this initial range you calculate the value of more information. I.e. how much is it worth to further reduce your uncertainty. If the benefit of more measurement is worth the cost you should perform that measurement. He then provides some helpful measurement techniques.

The most helpful part of the book, at least for me, was his definition of measurement and his value of information calculations. Defining measurement as uncertainty reduction helped to break me out of an engineering mode of measurement. In engineering you can usually measure very accurately. In either case you then use “the answer” in the rest of your calculations. The problem with this is assuming that a measurement can or needs to give you “the answer” to be helpful. It doesn’t keep in mind what the measurement is for in most cases.

His calculations of information is a great way to examine current or potential measurements and ask if it’s worthwhile. I’m certain that many businesses are wasting lots  of money measuring things that are easy to measure with high certainty, but are mostly worthless in terms of information value. It would be much more valuable to gather a few important metrics, even if there is a big range at a 90% confidence interval.

The basic approach is:

  1. Build a model of the problem. If you don’t understand how a variable affects the outcome, you won’t know what’s important and what you need to measure to what precision.
  2. Gather what you already know about the problem. Use current estimates and “calibrated experts”
  3. Calculate the value of gathering more information. You may already have enough information to come to your conclusion or make your decision. If not, you need to figure out if more information is worth the cost.
  4. Take measurements of the high value variables.
  5. Make your decision

My notes from the book:

Section 1 – No “intangible” is unmeasurable.

Chapter 1 – Hubbard suggests going through the book with some tough measurement problem in mind, you’ll find a way to measure it.

Also, he states there are three important propositions that define an approach to measurement in business:

  1. Measurements inform uncertain decisions
  2. There are many things to measure and many ways. Perfect certainty is rarely a realistic option.
  3. Therefore management needs ways to reduce uncertainly about decisions.

Chapter 2 – Hubbard provides examples and strategies for  measurement: Fermi decompositions (like what MBAs use for case questions). He states, “The concept of measurement as ‘uncertainty reduction’ and not necessarily the elimination of uncertainty is a central theme of this book.”

Chapter 3 – People don’t think some things are measurable because they have misconceptions about three aspects of measurement:

  • Concept of measurement (they      have an incorrect definition of measurement)
  • Object of measurement (they      are not clear on what they need to measure)
  • Methods of measurement (they      don’t have enough ways to measure)

Def of measurement: a quantitatively expressed reduction if uncertainty based on one or more observations.

Even a small reduction in uncertainty can be worth millions.

Clarification Chain:

  1. If it matters at all, it is detectable/observable
  2. If it is detectible, it can be detected as an amount (or range of possible amounts)
  3. If it can be detected as a range of possible amounts, it can be measured.

Helpful way to think of what you need to measure: thought experiments. Ask “What if?” what you wanted to measure was happening in one group and not in another, what would be different?

It’s very important to know why you want to measure something and what decision will it effect.

“If you don’t know what to measure, measure anyway. You’ll learn what to measure.” – David Moore

Four useful measurement assumptions:

  1. Your problem is not as unique as you think
  1. You have more data than you think
  1. You need less data than you think
  1. An adequate amount of new data is more accessible than you think.

Rule of five: there is a 93.75% chance that the median of a population is between the smallest and largest  values in a random sample of five from that population.

His five step process for measuring

  1. Define a decision problem and the relevant uncertainties
  2. Determine what you know now
  3. Compute the value of additional information
  4. Apply the relevant measurement instrument(s) to high-value measurements
  5. Make a decision and act on it

Section 2 – Before you measure

Chapter 4 – Clarify the measurement problem. What decision are you supporting? What will more information do for you? how will it change your decisions?

You need to understand the difference between uncertainty and risk. Uncertainty is simply the lack of certainty. You do not know the “true” outcome/state. The measurement of uncertainty is a set of probabilities assigned to a set of possibilities, e.g. there is a %50 chance of X, %30 chance of Y, and 20% chance of Z occurring. Risk is just a state of uncertainty where one or more of the possible outcome is negative.

Chapter 5 – Calibrating experts

You need to have a good grasp of probabilities and calibrate your intuitive understanding if a 90% confidence interval (add Wikipedia link).

It is possible to “calibrate” people so they are capable of repeatedly making guesses in a 90% CI (confidence interval). Calibration requires several exercises the most important if which is the equivalent bet exercise where you pretend to your 90% CI estimate is a bet versus an actual true 90% probability. If you are indifferent, then you are at a 90% confidence interval.

Chapter 6 – measuring risk through modeling

Monte Carlo simulations.

Chapter 7 – Measuring the value of information

Expected Value of information (EVI) = Reduction in expected opportunity loss (EOL)
EVI = EOLbefore info – EOLafter info

EOL = Change of making a sub-optimal decision x Cost of making a sub-optimal decision.

Expected Value of Perfect Information (EVPI) = EOLbefore info

(EOL after is zero if information is perfect, you’d make the perfectly optimal decision)

A common measurement myth: When you have a lot of uncertainty, you need a lot of data to tell you something useful. Actually, if you have a lot of uncertainty now, you don’t need much data to reduce uncertainty significantly. When you have a lot of certainty already, then you need a lot of data to reduce uncertainty significantly.

Section 3 – Measurement methods

Decompose the problem into its parts.

Perform secondary research

Use basic methods of observation:

  1. Does it leave a trail of any      kind?
  1. If the trail doesn’t already      exist, can you observe it directly or at least a sample of it?
  2. If it doesn’t appear to leave      behind a detectible trail can you devise a way to begin tracking it?
  3. If tracking the existing      conditions doesn’t suffice, can the phenomenon be “forced” to      occur under conditions that allow for easier observation? (i.e. an      experiment)

Measure Just enough

Consider the Error

He then goes into more detailed statistical techniques


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