#8/25: Megamistakes

Megamistakes was published in 1989 – so, it’s especially interesting to look at a book about forecasting which is over 20 years old. Most of this article will be quotes of the book. Enjoy!

The most prominent reason why technological forecasts have failed is that the people who made them have been seduced by technological wonder.

The forecasters who construct them are blinded by their emotions and lose perspective of commonsense economic considerations.

A key premise of forecasting is that a passionate focus on technology for its own sake spells disaster. No doubt, many of the same errors are being repeated today.

How much I love these three sentences. Year and year again we see some people praising some new technology to be the “next big thing”, “bigger than the internet” or whatever and a few years later nobody remembers about that technology.

As one expert noted: “We can build all kinds of mass-transit vehicles, but no one has yet found what’s going to make people want to get out of their cars and ride them”

A common technological trap. I thought for the most part of my life that technology can solve practically every problem. But if you look deeper, the problem is often psychological or cultural. Today, we see that lots of young people don’t necessarily need a car – forty years ago this was unthinkable.

Computer forecasts are exceptional in this regard. IT is one of very few industries where optimism was warranted.

This is really exceptional. Even the overoptimistic forecasts became true.

The method did not matter. Asking the right questions did. Nuclear ships had no effect on the industry. Lower-cost international competitors did.

This was a common theme in the last decades. Especially, in the US people thought about technology but neglected international developments.
Look at the flight industry. Flights didn’t really got cheaper because Boeing developed an innovate air plane. It got cheaper because of deregulation and lower barriers to entry.

It is good advice to be skeptical of any forecast that calls for a new age.

The lesson to be learned here is that although we read so much today about how things will change rapidly, the home of the future will probably look pretty much like the home of today

If you exclude computers then not so much has changed in the last 10 – 20 years. We pretty much life in the same houses, shower in the same shower, eat practically the same food (also people buy much more organic today). Cars haven’t really changed – the most change came through computers. Things like notebooks, smart phones, car navigation systems, etc.

In 1987 Tyzoon Tyebjee conducted some experiments on biases in new product forecasting. […] He found that the very act of participating in the new product planning process led to overly optimistic forecasts.

This is an interesting result for especially for technology startups, or more so for investors.

He [Nigel Calder] is particularly impressed with the performance of Barbara Wooten, a social scientist who made prediction in the 1964 study. She made “forecasts that seemed damp and depressing at the time.” In hindsight, however, they turned out to be remarkably accurate. Why? According to Calder, because she presumed “that the pattern of social life would not be remarkably different”

Later there’s more about demographic forecasting and it works pretty well.

There is absolutely no evidence that complicated mathematical models provide more accurate forecasts than much simpler models that incorporate intuitively pleasing rules of thumb. In growth market forecasting it seems less important whether the model is fancy or not than whether the model incorporates the right assumptions.

In the excellent book Forecasting (a newer version is freely available) they basically came to the same result. Often simple methods like exponential smoothing work better than highly complicated models.

In a 1985 article in Management Science, Everette Gardner and Ed McKenzie proposed a simple mathematical model that incorporates a “damped” trend. […] They and others, including myself, have tested their model in many different types of applications. Almost universally, it has been found to be more accurate.

And dampend trends work even better. You can read more about them int he same book as above for free: 7/4 Damped trend methods.

A central thesis of diffusion of innovation is that some initial group of customers purchase new products. They are called the innovators. […] After a while, a second group of customers, opinion leaders, enters the market. Rapid market growth ensues as many other consumers imitate the purchases of these respected members of society. […] Then growth slows, as nearly everyone is using the product.

The problem with using research arising out of the diffusion of innovations, the product life cycle, and market growth curves is that they ignore the fact that market growth is not guaranteed, or even likely.

Like the quote says the big problem is that the diffusion of innovation models only explains successful products, so it’s not of much use pre-success.

The Zeitgeist concept is used to explain the fact that inventions and discoveries tend to be made simultaneously by researchers working independently.

Consequently, the Zeitgeist means that inventions and discoveries are due less to the power of individual genius than to the spirit of the times.

The Zeitgeist also casts doubts on the merits of consensus forecasts. It implies that a consensus forecast is not difficult to obtain, but that the consensus may be more indicative of present beliefs than of actual future outcomes.

We’ve seen a lot of inventions happen simultaneously. Schnaars also notes that not only inventions are covered under the Zeitgeist concept but also forecasts. It’s interesting that each decade had its own Zeigeist and the forecasts mirrored it dramatically. Even Schnaars himself contributes to this by emphasizing the industrial strength of Japan and other non-US countries which was a big topic in the mid-late 80’s.
Here are some excerpts from Megamistakes:

Cars would also move into the jet age. All three American automakers spent heavily on turbine cars using jet technology. The transfer of technology from airplanes to autos failed to generate a growth market. It was an impractical idea.

All in a setting where children wanted to grow up to be astronauts rather than computer wizards. People were fascinated by space travel.

Imagine yourself in the late 1970s forecasting that energy prices would decline throughout the 1980s. For one, it never would have happened. Such a forecast would have been preposterous at the time. All the indicators of the day pointed to ever higher oil prices. Some even talked of the end of the petroleum age. Even if you had made the forecast, who would have believed it? It was inconsistent with the beliefs of the day.

The last two sentences are insightful. It’s hard to argue against the general belief. In science, there’s the observation that beliefs don’t die, but the people having them. It’s probably the same for normal people. In 10 or 20 years we will look back and saw some stupid belief we have today.

What to do?
Start with a simple price-performance analysis:

* What additional benefit does this product offer over existing entries?
* Will consumers have to, and be willing to, pay extra for it?
* Does the product offer a benefit over existing products that justifies a higher price?

This will filter out easily half of the promising technologies. In this book, Schnaars applies these three questions to a lot of different technologies, like moving side walks and video conferencing.

Predicting social trends is one of the most difficult forecasts to make. Social trends involve people, who, unlike physical quantities, do not behave according to physical laws.

In the permissive 1960s, for example, who would have predicated that a conservative President would be overwhelmingly elected in 1980? Radical college students in the 1960s saw a revolution in this country as a real possibility. In the 1980s, those same persons flocked to business schools and coveted highly paid careers in investment banking.

Demographic forecasts that sought to predict birthrates and other events that had not yet occurred often proved mistaken.

There are two different things here. Forecast of social trends and demographic forecasts. Social trends are extremely hard and probably more random than most of the things. Demographic forecasts, however, can be rather accurate if they people are actually born, yet.

For example, in 1960 Business Week analyzed census data and concluded: “During the next twenty years the number of Americans over 75 will increase to 9 million.” This forecast of the elderly segment of the population proved close to perfect. The 1980 Census counted 8.94 million persons over the age of seventy-five.

However, a problem is if you assume demographic and cultural forecasts behave identically.

Forecasts are particularly vulnerable when they assume that a growth market will result when a large group of consumers enters the primary age for heavy demand of a product category. Such forecasts assume that the younger group will follow the pattern set by its parents.

Like Elster said, either the young will do the same as the parents or something different. Don’t assume that younger people will do the same as their parents.

Are forecasts possible?

Yet a Business Week editorial foresaw the oil crisis about two years before the actual event. It stated that “the stage is being set for an energy crisis in the U.S. by the end of this decade. […] In a time of international crisis, oil supplies could be cut off.”

This is one example. There were a people who foresaw the 2008 financial crises and there were a ton of people who questioned the sustainability of Groupon.

Surprisingly, firms holding a commanding share of their market are often among the last to foresee potential threats to their bread-and-butter products. As a result, market leaders often miss the opportunities that they themselves should have created.

Remarkably, time and time again, in industry after industry, market opportunities have been more apparent to outsiders than to those with a dominant position in the industry.

We see this time and time, again. Schnaars describes one of the slide rule manufacturers. A giant – then came the micro chip and they though that this doesn’t really influence their market. A decade later they shrunk into a small business.
A similar thing happened to IBM where they declined the possibility of creating (enterprise) software, etc. etc.


  • Avoid technological wonders
  • Ask fundamental questions about markets:
    • Who are the customers?
    • How large is the market?
    • Will the new technology offer them a real benefit over existing and subsequent substitutes?
    • Is the technology cost-effective relative to those substitutes?
    • Is the derived benefit worth the price you will have to charge?
    • Are cost efficiencies probable?
    • Are social trends moving toward or away from this market?
    • Does the innovation require users to do things differently?
    • Does the innovation go against customs, culture, or established business practices?
  • Be suspicious of trend projections
  • Avoid extrapolating the issues of the day
  • Challenge Assumptions

There are some alternatives to “traditional” forecasting:

  • Scenario Analysis – I would recommend Solving Though Problems as a nice book on scenario analysis
  • Follow rather than lead – be innovator
  • Perpetual innovation: always innovate -> start small, try, then scale
  • Assume that the future will be similar to the present

Such an awesome book. I really loved reading Megamistakes. It’s full of insights. Schnaars shows different forecasts from different time periods. It’s astonishing how strong the Zeigeist influenced their forecasts. Furthermore, a lot of time he quotes other forecasters and their experiences. And you can get a used copy for under $5. It’s a fantastic book and should be read by nearly everyone! Recommendation!

Reading Kaushik (Part 1): Digital Marketing

I read Occam’s Razor for quite a while now and I really like Avinash’s style and insights. I thought it would be nice to reread most of his stuff and as a nice extra, I will post my notes on here.

I oriented each section by the section defined in his overview of all articles.


The 10 / 90 Rule for Magnificent Web Analytics Success

  • There is lots of data but no insights
  • Rule: 10% in tools and 90% people/analysts
    • may seem over the top but
    • med-large websites are complex
    • reports aren’t meaningful by default
    • tools have to be understood
    • there is more than clickstream to analytics
  • If you don’t follow the 10 / 90 Rule
    • Get GA account
    • Track parallel to expensive solution
    • Find a metrics multiplier, so you can compare GA to old data
    • Cancel your contract and hire an smart analyst which will probably deliver more insights for less money

Trinity: A Mindset & Strategic Approach

  • The goal is to generate actionable insights
  • Components:
    • Behavior analysis: clickstream data analysis
    • Outcomes analysis: Revenue, conversion rates, Why does your website exist?
    • Experience: Customer satisfaction, testing, usability, voice of customer
  • Helps you understand what customer experience on your site, so that you can help influence their behavior

The Promise & Challenge of Behavior Targeting (& Two Prerequisites)

  • We have so much behavior data but you get the same content regardless whether you are here to buy or get support
  • There are BT systems but you have still think about the input
  • You have to first understand your customers good enough to create suitable content
  • Test content ideas first to learn what works and as evidence for HiPPOs

Six Rules For Creating A Data Driven Boss!

  • Paradox: The bigger the organization the less likely it is data driven in spite of spending lots of money on tools
  • It is possible to achieve this but you have to actually want to do and fight for it
  • 1. Get over yourself: Learn how to communicate with your boss and try to solve his problems
  • 2. Embrace incompleteness: Data is messy, web data is really messy but still better than completely faith based initiatives.
  • 3. Give 10% extra: Don’t just report data, look at it. Give him insights he didn’t asked for. Make recommendations and explain what’s broken.
  • 4. Become a marketer: Great analysts are customer people. Marketer as internal customer (like account plannner)
  • 5. Don’t business in the service of data: Data should provide insights not just more data. Ask: how many decision have been made based on data that have added value to the revenue?
  • 6. Adapt a Web Analytics 2.0 mindset:

Lack Management Support or Buy-in? Embarrass Them!

  • HiPPOs may be don’t listen to you but they better listen to customers & competitors
  • 1. Start testing
  • 2. Capture Voice of Customer: Surveys, Usability tests, etc.: Let the customer do the talk
  • 3. Benchmark against the competition, e.g. use Fireflick
  • 4. Use Competitive Intelligence
  • 5. Start with a small website
  • 6. Ask outsiders for help

How To Excite People About Web Analytics: Five Tips.

  • 1. Give them answers
  • 2. Talk in outcomes / measure impact
  • 3. Find people with low hanging fruit and make them a hero
  • 4. Use customers & competitors
  • 5. Make Web Analytics fun: Hold contests, hold internal conferences, hold office hours

Redefining Innovation: Incremental, w/ Side Effects & Transformational

  • 1. Incremental innovation, e.g. Kaizen
  • 2. Incremental innovation with side effect, e.g. iPod or Adsense
  • 3. Transformational innovation, e.g. invention of the wheel
  • Web analytics can’t probably create 3
  • Clickstream alone is also not enough for 1.
  • generally the more the better (Web analytics 2.0)

Six Tips For Improving High Bounce Rate / Low Conversion Web Pages

  • Purpose gap between customer intent and page
  • 1. Learn about traffic sources / keywords(!)
  • 2. Do you push your customers against their intent? Identify jobs of each page and focus on your call to actions.
  • 3. Ask your customer what they are looking for
  • 4. Get insights from site overlays
  • 5. Testing!
  • 6. Get first impressions from people, e.g. fivesecondtest

Online Marketing Still A Faith Based Initiative. Why? What’s The Fix?

  • Faith based initiatives like TV, magazines, etc.
  • Online marketing gives us useable data
  • and allows us to test easily
  • The web is quite old yet it is not in the blood of executives
  • Old mental: shout marketing, instead of new inbound marketing
  • Lousy standards for accountability
  • Let the customers speak
  • Benchmark against competition

Win With Web Metrics: Ensure A Clear Line Of Sight To Net Income!

  • Focus on the bottom line, i.e. profits
  • 1. Identify your Macro Conversion
  • 2. Report revenue
  • 3. Identify your Micro Conversions
  • 4. Compute the economic value
  • Net income = Unit Margins * Unit Volumes
    • Unit Margins = Price – Cost
    • Unit Volumes = Market Share * Market Size
  • Because Net Income is the goal, you have to measure Price, Cost, Market Share or Market Size
  • Which metrics help doing that? And if not, why do you track/report this metric?
  • They also depend on the strategies or more general goals of the organization
  • — let your “boss” decide what matters most to him/organization
  • identify clear metrics / KPIs for each used strategy
  • use the web analytics measurement framework as a reporting foundation (more to this later)
  • find actionable insights with segmented analysis

Digital Marketing and Measurement Model

  • Marketing with measuring helps you to identify success and failure
  • Digital Marketing & Measurment Model
    1. Set business objectives (should be DUMB)
      • Doable
      • Understandable
      • Manageable
      • Beneficial
    2. Identify goals for each objective
    3. Get KPIs for each goal
    4. Set targets for each KPI
    5. Identify segments of people / outcomes / behavior to understand why things succeeded or failed
  • What scope has the model to cover?
    1. Acquisition: How do people come on your site? Why? How should it be?
    2. Behavior: What should people do on your site? What are the actions they should take? How do you influence their behavior?
    3. Outcomes: What are the goals? (see previous summary)

11 Digital Marketing “Crimes Against Humanity”

  1. Not spending 15% of your marketing budget on new stuff
  2. Not having a fast, functional, mobile-friendly website
  3. Use of Flash
  4. Campaigns that lead to nowhere
  5. Not having a vibrant, engaging blog
  6. “Shouting” on Twitter / Facebook
  7. Buying links is your SEO strategy
  8. Not following the 10/90 rule
  9. Not using the Web Analytics Measurement Model (previous summary)
  10. Using lame metrics: Impressions, Page Views, etc.
  11. Not centering your digital existence on Economic Value

Economics of Angel Investing

After writing the last post I thought a bit about the further development of innovation. What would be if we could predict successful companies (ideas) with high probability? That would allow to reallocate human capital faster and thus leading to more successes.
And one idea is a prediction market for startups. You may think that sites like AngelList go in this direction. I’m not entirely sure about this. I will first take a view on Angel Investing from an economic standpoint.

Angle Investor and Entrepreneur
This is a typical Principal-agent problem. The Entrepreneur has more information than the Investor. Often happens exactly what you will expect and that is that the Investor will look for commitments of the Entrepreneur which reveal information about his private information. Examples for these commitments are quitting one’s job, using one’s own money for funding or buying an expensive domain. Furthermore, of course, they try to grasp personal attributes of the Entrepreneur and his team.

Angel Investor and other Angel Investors
This stage is more interesting. Let’s say that our Entrepreneur got his first funding from one Angel Investor. Depending on the status of the first Angel Investor there are two different scenarios.
Firstly, assume the first Angel Investor isn’t famous. The next Angel Investor will probably see that this investment could possible be profitable but he will with a high probability go to stage one again and use his own judgment.
Secondly, now assume that the first Angel Investor is famous, a top notch one. The second Angel Investor will probably trust the judgment of the first Angel Investor so much that he will skip the first stage or neglect some flaws that he found. This is herding and leads to incorrect pricing and maybe a bubble.

We could either try to make these investments anonymously but this would be impractical. However, we could at least correct the pricing allowing short selling.
This all sounds like a stock exchange and they have a similar function, i.e. funding companies.

However, I think there’s one problem of stock exchanges for Angel Investment and that is that the expectations of the participants are different. Some Investors want 2x exists, some 5x exists, some want it in the next two years other in the next five. This is totally OK if we use these mechanisms for allocating capital.

Yet, the goal is to predict future successes and here I think prediction markets are more suitable because there are clear goals. E.g. “Company X will reach 5m in sales by 31 December, 2016.” Prediction markets do these things really good. One of the biggest problems will be liquidity which can be partially solved using aggregation or even better attracting more people to the market.

Want Innovation? Lower the initial investment.

I read an article a few months ago where the author complained that there isn’t much innovation outside of web and mobile applications. I thought about what makes them special and came to the conclusion that their initial investment is extremely low. I’m part economists therefore investment shouldn’t just be viewed as monetary investment. There are different costs and factors. I will take web application development as an example for this reasoning.

Educational Cost These include the costs of learning the techniques of your trade. Today, you can build simple web or mobile apps in less than a year without previous knowledge of programming. For programmers it’s even faster, maybe two or one month.

Capital requirements This is was a business major understands under initial investment. In the case of a web app it’s probably a hosting space and a domain. Maybe $5 per Month.

Administrative Cost Do you need any special certificates or are they any regulations? For web apps there aren’t any special restrictions.

Social Cost of Failure I think this is an important factor in more risk averse cultures, like in Europe. Let’s say you build a web app for two months, launch it and it fails. OK, happens often, no big thing.

Let’s compare this to some other, less innovative, industries like mechanical engine design. The educational costs are high, often you need some sort of advanced degree. The capital requirements are tremendous, you’ll need a work shop with very expensive equipment and so are the administrative costs with insurances, worker safety. The social cost of failure is increased because of the high capital requirements.

So how can these factors be reduced? One thing are definitely hacker spaces or shared work spaces. BioCurious provides the required equipment for the biotech endeavors (capital requirements). Furthermore, they provide classes to learn how to use this equipment (educational cost). This will also lower the social cost of failure.

I can imagine that this concept will be transfered to other industries like mechanical engineering, chemical processing, etc.