Center for Simplified Strategic Planning

Category: Making predictions

  • What people get wrong in strategic planning – part 3: data sources and estimates

    In my last two posts, I discussed two of the three things that people get wrong in strategic planning:  information and buy-in.  Today, I’ll be discussing the third thing:  sources and estimates.

    Data Sources

    As I mentioned in a previous post, people tend to want to find definitive sources of data for their strategic planning.  These sources rarely exist.  This is because the cost of aggregating the data exceeds the value available from selling the data.  I would take any published statistics on a very small market with a grain of salt, but they can be a good starting point.

    This leaves you with

    • your own data

    • proxy data, or

    • estimates.

    Each ot these may challenge you, but used properly they will give you a way to make unknowable data useful for strategic planning.

    1. Your own data

    Your own data may be sufficient for assessing markets if your share is large enough.  If it isn’t, you may need to “triangulate” your data with other sources to confirm its accuracy.  Even if it’s not enough, it is a good data point to start with.

    2. Proxy data

    Proxy data is simply data that is  indirectly representative of the data you need.  For example, if you wanted to establish the size of the market for mental health services for teenagers in a state, you could use other services as a proxy.

    Elective dental procedures might not seem like a good proxy, but they involve a choice to spend considerable money on a child’s health and well-being by parents – and this spending is not often covered by the most popular insurance plans.  This means that the known data – dental procedures – may help us understand how much money parents are willing to spend on their teenagers health.

    There are definitely flaws with any proxy you choose, and this one is no exception.  Mental health treatment still carries some shame for parents, but is sometimes considered an urgent need.  Even with these limitations, the proxy gives us a general picture of spending on teenage health.

    A more useful proxy may be available in some markets by looking at the market for some input, like steel for machinery or oil for certain chemicals.  Again, this isn’t perfect, but it will get you close to the real answer.

    If you can look at multiple proxies, you start to get the real “triangulation”.  This is because understanding the relationship between those numbers and the number you see will illuminate your own answer.  It is also far less likely you will make a bad decision if you consider the data from multiple sources.

    3. Estimates

    Estimates may involve the first two items – your own data and proxy data, but include some reasoning and judgment about the final answer. Fortunately, you don’t have to be perfectly accurate in your estimates (though it helps).

    In my experience, many are intimidated by the process of making good estimates from a skeleton of reliable data.  One of the key pitfalls here is spending too much time and money trying to get perfect data, when it is largely impossible and usually unnecessary.  The key to a usable estimate is simply to show your work, so that the reasoning can be considered in your strategic planning meetings.

    For example, with the dental/mental health proxy, we can make a decent estimate.  If we know that parents in a state spend an average of $3,000 per child on dental care, we know that the mental health number is unlikely to be twice that number.  It’s also unlikely to be less than half that number.  Your challenge in estimating the number you use will require you to reason about why it is higher or lower.

    Once you have an initial estimate, you can check it against other data sources.  One good check is to estimate the sales of everyone in the market, and compare it to your estimate.  Another is to get information from suppliers about how big they think the market it, and compare that.  It’s unusual that these two additional data points match an estimate exactly.  Regardless, if they are within 20% of your initial estimate, you are can probably use it in your strategic planning.  As I often tell clients, our goal the first year is to hit the broad side of a barn.  Naturally, in later years we should refine our understanding of the market.

    Your experience – and an offer

    What is your experience with the data you use in your strategic planning?  Have you practiced making good estimates in the past, or do you shy away from it?

    If you are using Simplified Strategic Planning, you may want to schedule a homework assistance day with Robert Bradford.  Robert makes the entire day available to your team to schedule 1:1 or team Zoom calls to go over strategic planning homework and tune it up for use in your strategic planning.  There is a limited number of slots available, and there is a special price offered until March 6

    Sign up at https://strategicplanning.clickfunnels.com/order-strategy-homework-help

  • Where are you on the Technology Curve?

    Where are you on the Technology Curve?

    technology life cycle curve
    technology cycle curve (source: Wikipefia)

    New technologies follow a fairly well-understood “curve”.  We can think of these curves in terms of profitability, but this is oversimplifying things.  Much of the thinking about technology curves in the business world comes from the work of Nikolai Kondratiev in the 1920s.

    The Kondratiev Curve

    In his work, Kondratiev suggested that there are long-term cycles of economic development and growth, characterized by periods of prosperity and innovation followed by periods of stagnation and decline.  These periods are driven by technological innovation.

    During the periods of prosperity, known as Kondratiev upswings, there are rapid advances in technology and infrastructure, as well as increased productivity and economic growth. These upswings are typically accompanied by new inventions and innovations that have a significant impact on society and the economy. For example, the Industrial Revolution, which began in the late 18th century, is considered a Kondratiev upswing.

    In contrast, the periods of stagnation and decline, known as Kondratiev downswings, are characterized by slower economic growth, decreased innovation, and declining productivity. These downswings are typically marked by economic crises, such as recessions and depressions, and may be accompanied by social and political instability.

    Technology Curves in Strategy

    While the application of this theory to economic thinking may have drawbacks, it is quite useful in understanding the path of a specific technology in an industry.  Technological adoption and improvement in the early years of a technology tends to lead to rapid growth and profitability for the key players in an industry, while the downswings can indeed lead to crises an instability.

    In the business world, most of the darling companies of the past 50 years have been companies in the upswing part of the cycle.  This make sense, since the way most analysts value stocks relies heavily on the Black-Sholes model and puts a premium on growth.

    Strategically, if you are in the upswing phase of your technology, congratulations.  Your business is likely to be viewed favorably by investors and your main focus should be market share over anything else, including profit.

    When your technology reached the top of the curve and begins the downswing, your strategic priorities should shift.  At this point, market share is still a priority, but profit becomes much more important as your industry shifts from building the technology to exploiting the technology.  Efficiency, execution and branding become much more important in this phase, and growth may be best achieved through identifying and exploiting niches or consolidation of the industry.  If your business has geographic elements, you may also want to seek growth through geographic expansion.

    Understanding where you are on the technology curve can be a critical element of setting good strategy.  Simplified Strategic Planning offers a tremendous advantage for setting and executing such strategies.  If you’d like to book a workshop on the process and its application in your organization, contact us at www.cssp.com/inhouse-workshop/

  • How to Predict Trends by Finding Limits

    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.predict the end of a trend by finding the limit

    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.