Strategy Planning Analogy #562: Bowlers Vs. Golfers

THE STORY
Last fall, when Jordan Spieth won the FedEx Cup of golfing, he earned $10 million. That’s a lot of money for a golfing match. It’s not typical. The winner of a typical game during the golfing season makes only about $1.5 million. In the 2014-15 golf season, Jordan Spieth won $22 million from all his playing in golf tournaments.

By contrast, professional bowlers earn a lot less. The winner of a major bowling event earns about $25,000 (one-sixtieth of a major golf tournament). Top money makers on the pro bowling tour only earn about $250,000 for the whole year. In the first 56 years of the PBA (Professional Bowler’s Association) history—through 2013—only 40 bowlers made more than $1 million during their entire career. When you get past the top ten bowlers, the average yearly earnings from professional bowling is only about $6,500. And you have to pay all your own travel and living expenses. (You can read more about the plight of bowlers here and here.)

What’s going on here? Pro golfers and pro bowlers are both athletes; they both play as individuals on a tour; they both try to get a ball to roll to a desired target; they both have to practice thousands of hours to master their craft; they both play games also played by millions of average Americans. So why do golfers make so much more than bowlers?

I thought there was a movement to promote equal pay for equal work. It seems to me that the work of professional bowlers and golfers is roughly equal. Shouldn’t the pay be the same?

THE ANALOGY
The problem is that tournaments can only pay out a percentage of what they earn, and bowling tournaments earn a lot less than golf tournaments. Golf has the advantage of appealing to affluent men, a category difficult to target by marketers. As a result, companies are willing to pay a fortune to sponsor or advertise on golf tournaments. By contrast, bowling fans are not a coveted group by marketers. In fact, the PBA was so debt-ridden that it was purchased in 2000 for only $5 million, less than the cost of a minor league baseball team.

The economics don’t favor the bowler. There isn’t enough money available to pay them any more. As a result, poor professional golfers can earn a lot more than the best professional bowlers.

This problem is very similar to what happens in any business. The amount of money a company can earn is based largely on the pool of money available in the industry in which the company operates. If you are operating in a great place (like an athlete in golf), your chances for success are high. If you are operating in a poor place (like an athlete in bowling), your chances for success are practically non-existent, even if you work as hard at bowling as a golfer does at golf.

At one time, the recorded music industry was like golf, earning huge amounts of money. Mediocre bands could still make a decent living off recorded music. Now, the pool of money available for recorded music has shrunk dramatically. Only the top performers can earn a decent living off of recorded music.

Therefore, one comes to the conclusion that it is more important for businesses to determine where to play than to determine how to get better at playing their game. And determining where to play is a key role for strategic planning.

THE PRINCIPLE
The principle here is that hard work only has a huge payout if you are working in a space that can afford to make huge payouts. The problem is that I see so many businesses focus all their effort on trying to “get better” rather than trying to be in the “right place”.

Their strategists focus on things like:

•      How do I lower costs?
•           How do I improve the business process to make it more efficient?
•          How do I speed up my output (or get to market faster)?
•           How do I use R&D to improve the features of my output?

They end up focusing on things like Lean or TQM or other such process improvement disciplines. This is like a professional bowler spending all his time trying to figure out how to become a better bowler.

The problem is that no matter how much a bowler improves his ability to bowl, he will never be making the big money. He would have been better off spending those thousands of hours of practice on golfing.

Similarly, no matter how much a company focuses on operational improvements, the odds of getting a great reward on that effort are minimal if you are operating in a business space that is not profitable. Doing the wrong thing more effectively is still doing the wrong thing.

This is why Michael Porter, in his seminal article in the Harvard Business Review called “What is Strategy?” (Nov.-Dec. 1996) said that operational effectiveness is not a strategy.

If you really want to do strategy, you have to focus on something else.

Ask the Right Questions

The first place to start is by asking the right questions. The first question is this: What business should I be in? The second question is this: How can I win in this business?

As a young athletic boy, one should first ask a similar question: What sport should I be in? How one answers that question can have a major impact on lifetime earnings. In fact, there may be no other decision a young athlete can make that will have a greater impact on success. If a lot of professional bowlers had seriously pondered this question in a rational way when they were young, they might have decided to focus on golf rather than bowling.

Similarly, the choice of where a business decides to play is critical. Your answer to that question can have a greater impact on future success than anything else you ever do.

For example, Textron and Berkshire Hathaway both started out in the textile industry in the US. They could have just stayed in that industry and tried to do the best that they could at operating in the US textiles industry. Their strategy could have focused on how to be faster, cheaper, better at playing there.

But they did not. Both companies stopped to ask that critical question: What business should I be in? As it turns out, the US textile industry was a relatively awful place to play. It was sort of like the “bowling” of the business world. There just wasn’t a lot of money to be made in that space.

As a result, Textron and Berkshire Hathaway diversified and moved into better business areas. Their portfolios include businesses in the “golfing” areas of the business world, far removed from textiles. Stopping to take time to ask the critical question allowed them to become large, successful entities. Had they not stopped to ask the question, and stayed in US textiles, neither company would probably exist today.

In an earlier blog, I referenced a study by McKinsey which said that the largest factor in a company’s success is determined by the nature of the industry the company decides to play in. So companies should spend time deciding where to play.

As critical as this question is, I find a lot of companies don’t stop to do this. They are so focused on getting better at where they are, they never stop to ask if they should be operating somewhere else. This error can ruin a company more than almost anything else they do.

This is not a one-time decision. Industries change; prospects change (as we saw in recorded music). You have to periodically reassess if it is time to shift the business portfolio. GE has been so successful for so long because they continually ask this question and periodically shift accordingly.

A successful choice in the past will not protect you forever. Analog photography was great for Kodak for years, but eventually the time came to switch businesses. By not doing so, Kodak’s doom was inevitable. There was no amount of operational improvement that could save them in analog photography.

Focus on the Right Efforts

This leads to the second issue—what strategists should focus on once the right business is chosen. Although operational improvements have an impact, strategists can make a greater impact if they focus on something else. Rather than focusing on how to do things better, they should focus on how to do things differently.

If you do things just like everyone else, there is no reason for someone prefer your offering. They will see you as pretty much the same thing, so they will pick whatever is cheaper. However, if you are doing things differently, you can create a point of differentiation, a reason to be preferred. If you are preferred, you can often charge a premium price.

Moving from an environment of extreme discounting to premium pricing may do far more for the bottom line than all those operational improvements put together. I speak more about the need for differentiation in a prior blog.

SUMMARY
Operational improvement is not a strategy. Strategy is about finding the right place to play and about how to win in that space by doing things differently. If your strategic planning efforts overlook these two areas and only focus on operational improvements, you may end up perfecting the obsolete.

FINAL THOUGHTS

Ask yourself: Is my business space more like bowling or more like golf? If it’s more like bowling, it may be time to change sports.

Strategic Planning Analogy #339: Simple Solutions

THE STORY
Back in the 1960s, the United States and the USSR were in a race for dominance in outer space. Each country wanted to prove its superiority by achieving more in space than the other, like being the first to land a man on the moon.

There were a number of difficult challenges in getting a man to the moon and back. For example, in outer space there is no gravity, which makes it impossible to use a standard ball point pen. Things need to be written down while in outer space, so what do you do to solve this problem?

Well, the United States took a number of years and spent millions upon millions of taxpayer dollars to invent a pen that does not require gravity. It was quite an achievement. By contrast, the Russians found a different way to solve the problem. They decided to use a pencil (which costs practically nothing).

THE ANALOGY
Government waste is nothing new. That multi-million dollar pen is not the first time governments have taken the expensive route when a much cheaper answer is available…and I’m sure it won’t be the last.

This is not just a problem with governments, however. Businesses also face all kinds of difficult problems. Just as in the case of the multi-million dollar pen verses a cheap pencil, there can be a tendency for business people to believe that complicated problems require complicated solutions. Well, many times you can solve a complex problem with a simple and inexpensive solution.

Before embarking on a long and expensive strategic journey to design a complex solution (like a pen that works in zero gravity), take a moment to consider whether there is a quick and simple solution (like a pencil). After all, just like the space race, businesses are in the race to win the hearts and minds of their customers. And in the business race, being slower to market with a more expensive alternative can destroy a company’s chances for success.

THE PRINCIPLE
The principle here is that just because a problem may appear complex, that does not mean that the solution needs to be equally complex. Many times, there is a simple answer. In addition to the story of the multi-million dollar pen versus the pencil, here are some other examples of that principle in action.

Empty Boxes
There was a cosmetics company in Japan which had a problem. Occasionally, the assembly line where their soap was inserted into boxes failed. Customers could purchase a box of their soap at the store and then be disappointed when they got home and found that their box was empty. The soap never got inserted into the box on the assembly line.

The Japanese cosmetic company put its best engineers on the case to solve this problem. Their solution? They devised an X-ray machine to check every box going down the assembly line. The X-ray machine would take a picture of each box so that two technicians could see into the inside of the boxes to detect whether or not the box had soap in it. This was a complex and expensive solution which slowed down the assembly line, created a need for expensive equipment and the hiring of more people, and could create potential radiation problems in the factory.

By contrast, one of the rank and file people on the assembly line found a simple solution. He bought a strong industrial electric fan and pointed it at the assembly line. The wind from the fan blew against each box as it passed by the fan. If a box was empty, the fan blew it off the assembly line, leaving only the boxes with soap in them. This solution was cheap and did not slow down the assembly line.

Big Trucks
And you’ve probably at some time heard the story of the large truck which got stuck under a bridge. Apparently, the truck was taller (or the bridge lower) than expected, so there was not enough clearance. Engineers were looking at all sorts of complex solutions for getting the truck unstuck—including taking apart parts of the bridge or cutting off parts of the truck.

A little boy walked by, looked at the situation, and suggested that all they need to do is let some of the air out of the truck’s tires. This would lower the truck enough so that it could be simply driven out from under the bridge.

Rough Road
This past weekend I was on a long road trip. I was starting to get tired and was worried about getting so tired that I would accidentally swerve off the road into a ditch. I started thinking of ways to prevent this problem. My first thought would be to put laser beams along the side of the road. If a car swerved off the road, it would break the laser beam. This would then send a signal to a sensor that would activate a series of lights and horns to alert the driver that they had crossed over the edge of the road.

Of course, then I remembered that this problem had already been solved. The road crews had cut out narrow little strips of concrete from the edge of the road. When a car crosses over the edge, the tires will go over these places where the narrow strips were missing. This would shake the car a little and make a loud rumbling noise that would get the driver’s attention. It was a cheap and simple way to solve the problem…much better than my idea.

So what can we learn from these stories to help up avoid making poor choices in solving problems?

1. Look at how You Define the Problem
Before you start to solve a problem, make sure you have properly defined the problem you are trying to solve. With the space story, the US had defined the improperly defined the problem as “How do I design a pen to work in zero gravity.” The USSR had more properly defined the problem as “How can I take notes in outer space.” By pre-supposing that the answer required a better pen, the US ignored the possibility of a simple, non-pen solution.

In the story of the truck, the engineers were trying to solve the expensive problem of “how to untangle a tall truck from a low bridge” rather than using the boy’s approach of “how to eliminate the tangle altogether by changing the relative height of the truck to the bridge.” The engineer’s question caused them to look up for a solution, while the boy’s question caused him to look down (at the tires) for the solution.

In other words, how you frame the question will determine where you focus to find the solution. Poorly worded questions tend to look at process improvement (better pen) or cleaning up a mess (truck stuck on bridge). These questions almost by default tend to create complex and expensive solutions.

By focusing on improving a process, you are eliminating the option of seeking out different options, like eliminating a process or substituting a radically different process. By focusing on fixing a mess, you are missing out on options which eliminate the mess in the first place.

Better worded questions look at outcomes (ability to take notes) and solutions (truck no longer under bridge) rather than the immediate problem at hand (bad pen, stuck truck). Take time to phase your question properly, so that you are working on discovering solutions rather than fixing problems and processes.

2. Look at what You are Trying to Accomplish
In the case of the Japanese soap box engineers and my approach to solving drowsy driving, we both made a fatal mistake. We assumed that prior to solving the problem, there needed to be intermediary steps. We both added steps around detection and segregation.

The Japanese engineers wanted an expensive process to first detect which boxes needed special treatment (X-rays). Then they would segregate those empty boxes and treat them differently. My road process wanted expensive lasers to detect which drivers were driving poorly before segregating them for special treatments of noise and lights.

The simple solutions avoided the prior steps of detection and segregation. For the soap boxes and the fan, nobody needed to pre-determine which boxes had no soap in them and the boxes did not have to be separated for different treatment. Instead, every box was treated the same all the time. With the fan, empty boxes disappeared all on their own, without prior detection.

With the cut grooves in the road, there was no need for an expensive laser detection system. And you didn’t need a process to turn off and on warning sounds based on that detection. All cars were treated the same, and if a car was veering off the road, the system took care of itself.

In other words, before tackling a problem, make sure to examine what you are trying to accomplish. Sometimes we try to accomplish a series of steps which require events like gathering knowledge, detecting differences, and treating things differently based on these differences. Perhaps you do not need to accomplish all of those intermediate steps in order to solve the original problem. Again, one needs to focus on the solution rather than the process. By focusing on all the steps in a process, you may fail to see the benefit from eliminating steps or using a different process with simpler steps.

3. Look at who you ask to Solve the Problem
As we pointed out in an earlier blog, “to a hammer every problem looks like a nail.” In other words, we tend to create solutions based on our backgrounds and our strengths. An engineer will tend to look for solutions which require expensive engineering, because that is what they do for a living. In the case of the pen, the X-ray and the dismantling of a bridge, engineers were looking for engineering solutions. The idea for the fan came not from an engineer, but a worker on the line. The idea to let air out of the tires came from a little boy.

When you are looking for solutions, who do you have on the solution team? Consider having a diverse group, including people on the front line, customers, new employees, and people from diverse backgrounds and disciplines. It’s hard to find out-of-the box solutions if you keep turning to people in the same box to solve them. Make sure your team includes people whose occupation is not tied to expertise in creating complex solutions.

SUMMARY
Just because a problem is large does not mean that the solution automatically needs to be large, complex, expensive, and take a lot of time. Often, there can be a simple solution. To find the simple solution, a) frame questions around solutions rather than problems; b) don’t get hung up on solving a number of unnecessary intermediate steps; and c) have a diverse team working on the solution.

FINAL THOUGHTS
Sometimes problems do require complex solutions. To determine whether a more complex solution is required, ask your self these questions:

1. Am I just treating a symptom or the root cause of the problem? If you are just treating a symptom, then you need to broaden the solution.

2. Is this problem intertwined with lots of other issues in a system where actions in one area can ripple out into dozens of unintended consequences in other areas? If so, then you probably need a broader systemic approach.

Strategic Planning Analogy #270: Tool Time

THE STORY
A man walks into a home improvement store to purchase a ladder. He goes up to the salesman and asks for a recommendation on which ladder to buy.

The salesman replies, “That depends on what you plan on using the ladder for. We have tall ones, short ones, durable ones, inexpensive ones, and flexible ones. Tell me how you plan on using the ladder and I’ll recommend the right one.”

The man says, “I don’t know how it will be used. Heck, just get me a blue ladder. I like the color blue.”

THE ANALOGY
It’s hard to buy the right tool when you do not have a clue as to how it will be used. The same is true with financial models. You can design all sorts of different computer models for your strategic scenarios—simple ones, complex ones, flexible ones, and so on. Like the ladders in the story, each type of model has its place—and each type can be inappropriate under certain circumstances.

Models are like ladders; they are both tools to get a job done. The better you understand the job, the better choice of tool you will make. Choosing a ladder because you like its color makes as much sense as choosing a modeling technique because it is your favorite. Instead, get the tool based on the job to be done.

THE PRINCIPLE
The principle here is that the best type of model is the one that best answers the question at hand. Simple models have their place. They are quick and easy to build and easy to understand. However, they may not be adaptable to a wide variety of scenarios and they may not be sophisticated enough to provide a meaningful answer.

By contrast, a sophisticated and complex model can allow you to understand a situation more deeply. They can also be adaptable to more alternatives. Unfortunately, they can also be a time-consuming nightmare to build, debug, and input data. In addition, you may not have enough information to know how all the pieces in the model should interact.

Remember, a computer model is just a tool, not the end result. The end result is a strategic decision. Depending on what that decision is, different models may be more or less appropriate.

Keeping that in mind, here are my rules for designing models.

1) Start With the End
The first thing you need to do is ask what decision will be made based on the modeling. Knowing that (the end) will let you know where to start. I’ve seen cases where someone rushes off to build a model before fully understanding how the model will be used. They come back with something inappropriate. That is a waste of time for the modeler and the ones for whom the model was made.

If you are trying to decide between two different ways to operate your business (quality vs. low price; in-house vs. outsourced; mass vs. niche; automated vs. flexible; etc.) perhaps the best model is just a single look at each option in its mature state. There would be more complexity around the factors that are different in the scenarios and less complexity in the areas where they are the same.

If you are trying to decide whether to do an acquisition, then you probably want a model which spans several years—long enough to capture the value of the deal. You need enough detail to compute cash flow. Since a lot of the value of an acquisition is gained or lost during the transition process, you would want to model that as well.

If you are trying to choose between short-term tactics (like a pricing plan or an advertising plan), the model can probably be simplified to only looking at the areas of the business impacted by the tactic.

If you are in a crisis mode where a decision has to be made immediately, stick to the key issues and crank something out quickly.

2) Never Asssume You Will Get it Right In One Take
I worked with a guy who had an interesting take on model building. Once he got all the formulas right, he would run the model once and then freeze the results. In other words, he would erase all the formulas and links from the model and replace them with the actual numbers which came out of the first running of the model.

Then, he would present his results. Invariably, someone would want to adjust some of the assumptions in the model or try another scenario. This guy would then throw a fit because he had erased all of the formulas. He couldn’t run the model again because he had frozen each cell in the spreadsheet with the number from the prior scenario.

This is an extreme case, but the principle applies broadly. Assume that there will be future adjustments to the model. Build it with enough flexibility so that it can be adapted to the future changes.

3) The Questions You Ask Are More Important than the Model You Build
Models are built to provide more clarity around a business decision. Fuzzy notions are hard to quantify and even harder to properly evaluate. In the process of building a model, one has an opportunity to help your audience become less fuzzy by asking a lot of questions.

A computer spreadsheet model has a lot of cells which need to be filled. By working with your audience and asking the right questions, you can force them to become clearer about how each of those cells inter-relate. They may not have thought it all through. By asking the right questions, you can make them think about things that need to be thought through in order to fill in all the cells.

The value of getting them to think these things through may be a lot more valuable than the actual number which comes out of the model. For example, they may be looking at changing the price of a particular product/service. To make sure they fully understand the ramifications of the price change, you can ask questions like:

- How would that price change impact the price perception (and cannibalization) on the rest of the product portfolio?
- How will it impact your quality image?
- What happens if competition matches your price?
- How much price elasticity is there in the marketplace?
- If a lower price raises demand, what items are fixed and what items are variable in meeting that demand?

Just by asking those penetrating questions, you can create better decision-making, regardless of the model. I remember someone from McDonalds telling me about their test of a new product called McShrimp Cocktail. The original model looked pretty good until someone asked the question, “How much shrimp would it take to roll this thing out chain-wide?” Once it was determined that 100% of the known shrimp in the world would not be enough to cover annual sales projections, the project was scrapped. Simple questions can be very powerful. Use the tool of the model as an excuse to get in front of people to ask these questions.

4) Once You Have Enough Information to Make the Right Decision, Stop
The goal here is not the perfect model, but the right decision. Sometimes the choice is so obvious that it doesn’t take much of a model to show it. The gap between option A and option B at times can be so large that you don’t need to waste a lot of time fine-tuning the model. If no amount of fine-tuning could ever make option B better than A, then stop the fine tuning.

As you build and refine the model and the assumptions, continually ask yourself this question, “What is the likelihood that further refinement would lead me to a different conclusion?” At the point where you see little to no chance that further refinement would change your decision, then make the decision now and stop wasting time refining the model.

Sometimes the difference between option A and option B can be very slight. In those cases, it can be well worth the time to further refine your modeling in order to better understand which is the right decision. Focus on the key areas which are the least certain and the most influential.

SUMMARY
Financial modeling is just a tool. Its value comes from its ability to help you make better decisions. Depending on the decision, you made need a different model. So start by understanding exactly what that decision needs to be. Then bring clarity around that decision by asking the right questions. Finally, once you have enough clarity, stop fine-tuning the model.

FINAL THOUGHTS
Some strategic implications of a decision are hard to quantify, like the impact on corporate culture or the value of strategic flexibility. Just because they are hard to quantify does not mean they should be ignored. Often, the soft issues make or break a decision. A financial model is just one tool in the toolkit. Combine it with softer tools which take these other aspects into account.