In the first two articles about predicting the future, we looked at types of curves and leading indicators. These tools are useful ways to predict trends, but tends have limits, too. Today, we will look at the types of things that disrupt predictions made with the first two tools – or any other tool.
In general, if we understand how to find the type of system or curve shape we are dealing with, we can make much better predictions than most people. One of the biggest gotchas that can occur using this approach is that systems always have limits. Limits can be a powerful tool for understanding when trend and systems based predictions are most likely to fail.
Predicting Trends in the Dotcom Boom
Think back to the mid to late 1990s, as an example. The US economy was growing by leaps and bounds, and the early dotcom players were the darlings of the stock market. Stories of startups reaching huge market valuations in three or four years dominated business news. Many of these companies were bought and sold – privately or publicly – for multiples of earnings that astonished people with experience in finance.
In terms of curve shapes, this was clearly a period where we saw the effects of the geometric curve. The growth in adoption of internet-based behaviors was a clear driver of growth in dotcom businesses, and the numbers were staggering. The internet access service AOL grew from users in the tens of thousands in the beginning of the 1990s to 1 million users in 1995. By 1996, AOL had 5 million users, and the numbers seemed to be increasing more every year. Three years later, they had over 18 million users, and AOL was one of the biggest growth stocks of that period. Companies like Yahoo, Amazon and Priceline were seeing similar growth.
Along with this dizzying growth came astronomical market values. Stocks in internet businesses commonly traded at multiples exceeding 50 times earnings. This happened because many pricing models used the anticipated growth rate in earnings to value a company’s shares. Clearly, businesses that were seeing 500% growth in a single year must be worth much more than “normal” businesses that saw only 10 or 15% growth. In the early years of the dotcom boom, these investments payed out handsomely. Many investors who liked to predict trends clearly saw big growth in these markets, and invested accordingly.
Growth Plateaus
Unfortunately for many investors, between 2000 and 2005, most of these companies saw their growth plateau. As the growth rates slipped, market valuations began to tumble, and dotcom companies that had seemed poised to take off were often sold at a fraction of their earlier valuations. This was clearly a visible, understood and heavily analyzed market, by this point. So what happened to the predictions that led to such heavy investing?
To understand this, we need to think about what drove growth in these markets. In this case, the growth was massive adoption of a new technology – internet access. In 1989, only the most bleeding-edge tech-savvy households had modems, but by 1999, nearly 1 in ten households in the US were paying AOL for internet access. This suggests a very important factor for growth by technology adoption – the portion of the possible market that is currently using the technology. Unfortunately for AOL, there was a limit to this adoption. Simply put, the limit was the number of households in their market. As the technology got closer to this limit, the cost of converting non-users to users started to rise, and new forms of resistance to adoption emerged.
Predict Trends with Limits to Growth
This point – where growth tapers off and new growth is limited by the population, is where the technology of internet access nearly reaches its limit. Growth in the technology may still exist, but the markets are mostly saturated, and new growth will be driven by new products, services, and technologies. By 2010, AOL was no longer a growth stock, and its place was taken by players like Netflix, who was riding a new was of adoption fueled by broadband internet access. With the new technology, a new geometric growth curve revved up, and the tech market was once again dominated by the growth of user bases.
When we set out to predict trends, how do we see these limits before they happen? Simply put, we need to ask the question “What would stop this curve from going up this quickly forever?” In most cases, the population involved is a reasonably hard limit. The number of people in the world who have mobile phones cannot reach 20 billion because there aren’t that many people. Sales of high-end private jets are unlikely to exceed a few hundred because the number of billionaires is similarly limited. And right now, it’s safe to assume that the number of people infected with the COVID virus cannot exceed 100% of the population.
When looking for limits, ask what else might reduce the absolute maximum. For example, internet adoption can be limited by more than population. Luddites and other people who abstain from the use of technology, for example, are always there, as are people who can’t afford private jets, or are afraid to fly. Quick estimates of these proportions will help you estimate the level of any limit, especially in business forecasting.
When you predict trends in your future growth plans, it may help to work with experienced professionals who have a strong track record of planning for the future. If you’d like to learn a systematic approach to strategic planning that will help you incorporate some of these ideas, attend our day long online workshop on Simplified Strategic Planning, on August 10.