One of the basic skills required to invest in, buy, or sell businesses is the ability to correctly predict the size of a market for a product once it reaches maturity. Most professionals get these estimates wrong. At the core of under- or overestimation are a few common errors. The first error is confirmation bias, which every expert brings to the table. The second and third errors are “sins of omission,” which are both related to treating this as a purely theoretical math exercise. You must factor in consumer passion, otherwise the size of prize will be too low. But you must also factor in practical application (e.g., how would you actually realize the size of prize), otherwise the size of prize will be too high. Getting these estimates right is the key to consistently making money in a variety of industries.
Every executive has two types of investment war stories. The first type is underestimating an opportunity, which leads to a “fish that got away” story like how Blockbuster could have bought Netflix. The second type is overestimating an opportunity, like how Eddie Lampert bought Sears for $11 billion only to see it land in bankruptcy over a decade later.
One would think we would be better at conducting “size of prize” analyses. But we’re not. A private equity owned company turned to a well-known consulting firm to size the prize for one of their portfolio companies. That consulting firm concluded the business had already realized half of the total market opportunity. The business then proceeded to double in size within a year — with no signs of slowing down. This rattled the private equity firm, so they tried a different approach. By looking at the opportunity through the eyes of superconsumers — the most passionate, profitable, and prescient consumers — it was clear that this company was creating a new category, not just a new product. This showed the business had only captured a tiny fraction of the opportunity. The private equity firm realized they were at risk of selling the company too quickly and for too little.
At the core of under- or overestimation are a few common errors. The first error is confirmation bias, which every expert brings to the table. The second and third errors are “sins of omission,” which are both related to treating this as a purely theoretical math exercise. You must factor in consumer passion, otherwise the size of prize will be too low. But you must also factor in practical application (e.g., how would you actually realize the size of prize), otherwise the size of prize will be too high.
The first error of confirmation bias is unhelpfully baked into the most common approach to this task, which is finding a “comp” (or comparable example) as an analogy. Investment bankers like comps because they provide a quick shorthand at looking at potential EBITDA multiples of other transactions. This is similar to what a real estate agent does to help value your house before you sell it. But what happens when the comps chosen are single-family homes, and your property is a luxury condo that happen to be in the same zip code? When valuing businesses, this happens way more often than we realize — and it’s especially common when the company being evaluated is truly disruptive and is blurring the lines between multiple categories.
Confirmation bias also occurs with executives. Some have strong incentives to throw darts at a potential opportunity, not just because the vast majority of acquisitions fail to pay back, but also because they have to integrate and deliver the results being promised. Other executives overestimate the upside of a new category because they are trying to buy growth or trying to cement their legacy, which is how Jeff Immelt apparently viewed the troubled Alstom acquisition.
For the second error, misunderstanding consumer passion is typically the root cause of why a size of prize analysis is too low.
Consider this example: Rogaine would have been a much larger business had they not launched as a male hair loss treatment, but rather as a women’s hair thinning prevention. This is true for three reasons: Women have much more consumer passion about their hair, they are much more willing to tolerate a high-discipline, daily regimen, and hair thinning is a much more emotionally traumatic issue for women than balding is for men.
Another example is Pedialyte, a dehydration product for infants. Only recently has its producer begun finally accepting the fact that adults love it as a hangover salve. Pedialyte was launched in 1966, and adults have been using the product this way for decades, but only recently has the company begun marketing this way and correctly incorporating it into size estimates of its market.
The other way consumer passion gets miscalculated is to treat everything via a “problem-solution” approach, which is very rational but misses out on upside from emotion and passion. Take Insomnisolv, a startup with a wearable sleep device that is clinically proven to help consumers fall asleep 40% faster and stay asleep 30% longer. They realized their total addressable market (TAM) was much bigger if they flipped the script from a “problem-solution” approach (11% of U.S. consumers who say they are very dissatisfied with their sleep) to a broader “benefit-enhancement” approach (26% of consumers who completely agree that they love sleep and want even more).
The third error is not factoring in practical execution — in other words, what you need to do to successfully get to the number predicted. If you don’t know how you would practically realize the size of prize, it probably isn’t real. Instead of focusing just on the final number, recognize that what a size of prize analysis does is help you figure out the shape of your category “s-curve,” the growth and lifecycle of the category, and where your category is now on it. Every s-curve is different but has the same stages of (a) early slow growth, (b) steep growth (the “hockey stick”) and (c) later slow growth/decline. Over-investing is the biggest risk in the first stage, where you have to carefully fan the flame without putting out the fire, and the third stage, where no amount of marketing will help spark growth in a flat to declining category. Similarly, under-investing is the single biggest risk in the second stage.
The single biggest missed opportunity is not realizing the treasure trove of category ceiling and s-curve data that companies already have right under their nose. The reality is that there isn’t just one category ceiling and s-curve, but in truth many category ceilings and s-curves at a local market level. Do what we call a “Super-Geo” analysis to look at your best-performing and worst-performing local markets on a per capita basis. Take your company’s sales and shipment data at the most granular, local level and then convert it into a per capita metric.
We’ve seen extreme variance of both ceilings and s-curves at the zip-code or county level. Executives in categories that nationally have low single-digit penetration are shocked when they see that some local markets have rates in the high double digits. Others are spooked when they see that while national growth might be positive, key local markets have declining category growth. This is perhaps the least-utilized and the most robust fact-base because the category ceiling and s-curves are not projections, but actual reality.
Over-specialization is at the root cause of all three errors. Anyone with expertise has confirmation bias risk. Few people have expertise in multiple disciplines necessary. To correctly size the prize, you need three lenses — a microscope (e.g., consumer passion, activation, super-geos), a telescope (industry breadth and broad strategy expertise), and a mirror (confirmation bias). To avoid confirmation bias, you need a breadth of industry experience and likely strong strategy expertise. To solve for the second error, you need extensive expertise with consumers. To solve for the third error, you need expertise in execution and activation. Consultants and bankers, who spend a lot of time doing size the prize analysis, have relatively little experience with consumers or execution, which is why many of these analyses turn out wrong.
The key is to be self-aware of your own biases and skill gaps you are bringing to the table, and then build a team that compensates for the gaps.
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