#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.

Guidelines:

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!

#1/25: Information Rules

I want to try out a new format which you could call “book commentary”. I’ll quote some text passages and write a short comment about each passage.

Technology changes. Economic laws do not. If you are struggling to comprehend what the Internet means for you and your business, you can learn a great deal from the advent of the telephone system a hundred years ago.

This is a great advice and I can anybody recommend to read old books about business and economics especially case studies. I already covered some old business books myself here on the blog and I’m always receptive to recommendations.

We think that content owners tend to be too conservative with respect to the management of their intellectual property. The history of the video industry is a good example. Hollywood was petrified by the advent of videotape recorders. The TV industry filed suits to prevent home copying of TV programs, and Disney attempted to distinguish video sales and rentals through licensing arrangements. All of these attempts failed. Ironically, Hollywood now makes more from video than from theater presentations for most productions. The video sales and rental market, once so feared, has become a giant revenue source for Hollywood.

Interesting enough, Hollywood is still trying to fight against piracy. The next step would probably be to offer cheap versions as a stream (ala netflix). However, people don’t want to pay too much for a video stream.
I think it may be comparable with the automotive industry in the beginning of the 20th century. There were lots of car manufactures that produced really high quality cars which were really expensive. Most people couldn’t afford a car at this time. Then came the Ford Model T, which wasn’t as fancy at these other cars but it was cheap and good enough and people bought it.
Maybe Hollywood should think about producing movies which don’t cost $200m but instead only $20m.

In competing to become the standard, or at least to achieve critical mass, consumer expectations are critical. In a very real sense, the product that is expected to become the standard will become the standard. Self-fulfilling expectations are one manifestation of positive-feedback economics and bandwagon effects.

A very interesting observation with great effects. This makes PR much more important than I thought it would be. Especially, if you apply to to startups. Signals like founding and investors become stronger. And it isn’t so much about the product and more about connects, strategic networks, PR and marketing.

The dominant component of the fixed costs of producing information are sunk costs, costs that are not recoverable if production is halted. If you invest in a new office building and you decide you don’t need it, you can recover part of your costs by selling the building. But if your film flops, there isn’t much of a resale market for its script. […] Sunk costs generally have to be paid up front, before commencing production.

We’ve seen some movies which ran horribly in the cinema but great in DVD markets. So, there’s some recoverability. However, the movie can still suck. One method to cover the costs are upfront investments. Kickstarter is basically allowing this for a mass-market and some game studies took this approach to produce games which wouldn’t be backed by a publisher (see Doublefine Adventures).

The key to reducing average cost in information markets is to increase sales volume. Think of how a TV show is marketed. It’s sold once for prime time play in the United States. Then it’s sold again for reruns during the summer. If it is a hot product, it’s sold abroad and syndicated to local stations. The same good can be sold dozens of times.The most watched TV show in the world is Baywatch, which is available in 110 countries and has more than 1 billion viewers. […] The shows are cheap to produce, have universal appeal, and are highly reusable.

Basically the Hollywood argument I made above. Lower the production costs but produce more variety and stimulate more innovation.

With information you usually produce the high-quality version first, and then subtract value from it to get the low-quality version.

This is really important for the customer. You don’t want to feel that you paid the normal price for the inferior product. One example are some games which come with lesser content in the normal version but still costs $50-60. Don’t do that.

The coupons are worthwhile only if they segment the market. A coupon says “I’m a price-sensitive consumer. You know that’s true since I went to all this trouble to collect the coupons.” Economist say that a coupon is a credible signal of willingness to pay. […] What does this have to do with information pricing? Well, suppose that information technology lowers search costs so that everyone can “costlessly” find the lowest price. This means that sales are no longer a very good way to segment the market. Or suppose that software agents can costlessly search the net for cents-off coupons. In this case, the coupons serve no useful function.

I found this passage quite interesting. Basically sites like Groupon are too easy to use, so that people don’t segment themselves that good. Furthermore, there are lots of sites which offer coupon codes, so that today a sale for most online shops is probably more appropriate.

The rights management strategy is a twist on the versioning strategy described in Chapter 3. There we argued that you should offer a whole product line of information goods. The cheap versions (which can even be free) serve as advertisements for the high-priced versions.

Freemium described over 12 years ago. Interesting enough, McAfee used a freemium model since 1993 and before that they used a “pay what you think“-model, which was also quite revolutionary for that time.

Of course, a new brand can emerge that is easy to learn, thus reducing switching costs. Indeed, one strategy for breaking into a market with significant brand-specific customer training is to imitate existing brands or otherwise develop a product that is easy to learn. Borland tried this with Quattro Pro, aimed at Lotus 1-2-3 users, and Microsoft World has built-in, specially designed help for (former!) WordPerfect users.

We’ve seen this in the online market quite recently, e.g. with WordPress and tumblr. I wonder if you see a better word processor in the future.

What happens when perfect competition meets lock-in? […] Think about the extreme case in which you face fierce competition from equally capable rivals to attract customers in the first place. Both you and your rivals know that each customer will be locked into whatever vendor he or she selects. The result is that competition indeed wrings excess profits out of the market, but only on a life-cycle basis. The inescapable conclusion: firms will lose money (invest) in attracting customers, and (just) recoup these investments from profitable sales to locked-in customers.

Normally, you would assume that lock-in leads to excessive profit in a market but it doesn’t. You can talk about quasi-profits, i.e. the lock-in needs negative investment at the start and if you locked-in a customer, he will return the investment costs over his life-time. That is, if you want to make excessive profit, you have to still rely on product differentiation and/or cost leadership.

If you give your product away, anticipating juicy follow-on sales based on consumer loyalty/switching costs, you are in for a rude surprise if those switching costs turn out to be modest.

That’s when freemium goes wrong. If you are in a market with low or non-existing lock-in costs, i.e. trash mail provider or image uploading sites, freemium probably won’t work.

An other approach is to rely on versioning by offering long-standing customers enhanced services or functionality. Extra information makes a great gift: it is cheap to offer, and long-standing customers are likely to place a relatively high value on enhancements.

I really like this idea. Often you see introductory offers, like 20% off of the subscription but after this period, you either don’t care about the price, feel ripped-off, because you have to pay more or cancel your current account and get another introductory offer.
However, if you reward long-term customers with some useful addons, they have no incentive to do the latter and probably will appreciate the extra addon. Varian and Shapiro talk about this in the book in greater length.

The beautiful if frightening implication: success and failure are driven as much by consumer expectations and luck as by the underlying value of the product. A nudge in the right direction, at the right time, can make all the difference. Marketing strategy designed to influence consumer expectations is critical in network markets.

See the quote above. Early adopters are really important and early press coverage can greatly introduce the probability of success.

The revolution strategy involves brute force: offer a product so much better than what people are using that enough users will bear the pain of switching to it. […] The revolution strategy is inherently risky. It cannot work on a small scale and usually requires powerful allies.

The authors quote Grove’s 10X as a revolutionary metric. I got two nice examples which fit to this quote.
Firstly, Google+ which hasn’t offered a 10X and wasn’t a evolution of Facebook either. Furthermore, the group in the beta phase was too small.
An other example, are open source clones of proprietary products. More often than not, free source code or enhanced privacy aren’t 10X.

In addition to launching your product early, you need to be aggressive early on to build an installed base of customers. Find the “pioneers” who ware most keen to try new technology and sign them up swiftly.

Really important, see Crossing the Chasm.

All in all, I really liked this book. I think it’s probably a must-read for internet entrepreneurs. What I personally found really interesting to see which people endorsed this book and one of them is Eric Schmidt (Google’s ex CEO). And he said in an interview about Google+ and its chance to beat Facebook: “It’s very hard to beat a fast-moving incumbent in exactly same game in technology because it changes so quickly.”

If you are interested in more detail about the content of the book, its website offers free presentation material for college courses. Great book, great writing.

#26/111: Only the Paranoid Survive

What is it about?

How do you change a company? And how do you avoid being overrun by new forces? Andrew S. Grove explains how Intel changed from a semiconductor company to a microcomputing company.

Key points?

Strategic inflection point: Grove defines a strategic inflection point as a point or period in which a force (see Porter five forces analysis) gets 10X stronger. For example, in the 80s, the Japanese memory industry grew very fast and got very cheap. This was a gruesome experience for Intel because they were known as the memory company in the USA.

Always observe your environment: To avoid being overrun by such changes, you should always observe your environment, i.e. your customers, competition, new technology, your suppliers, etc. Only if you can see emerging 10Xs you can act fast enough.

Is it a 10X or isn’t it? There are a lot of changes but which are important? Ask your employees, customers or vendors. Often the CEO is the last one to see a change. If you think that one of these 10X forces isn’t really one, don’t discard it. Observe if it changes and then valuate it again.

Change from top and bottom: You can’t force a change from the top, but also can’t form a cooperate strategy from the bottom. It is important that you use both forces to carry out the essential modifications.

Conclusion

This is a excellent book on changing a company. Andrew Grove recounts his own experiences and don’t try to please everybody. Half of the managers quit the company because they don’t wanted to change. The other 50% had to reeducate themselves. Changing a company isn’t easy and there will be casualties but it’s better than completely vanishing. Clear recommendation!