Strategic Planning Analogy #276: Bias to Stop

THE STORY
Ford invented the minivan, but refused to build them. Why? First, Ford was #1 in the sales of station wagons at that time. Ford feared that the minivan would cannibalize the sales on all those station wagons. Second, the truck division thought it might cannibalize some truck sales, as well.

Ford saw little benefit in spending all the money needed to gear up production for a new vehicle (the minivan) if all it was going to is steal business from their other profitable lines. Therefore, they did not introduce the minivan. It seemed like a wise financial move at the time. Why needlessly spend all that extra capital to get sales you already had?

Of course, when Lee Iacocca left Ford and went to Chrysler, he found himself at a company that was weak in the station wagon and truck businesses. There wasn’t much at Chrysler to be cannibalized by the minivan. Therefore, Chrysler introduced the minivan. It was a huge success, mostly at Ford’s expense.

Ford was correct about one thing. The minivan did make the station wagon obsolete. Ford lost nearly all of its station wagon sales. Too bad Ford did not factor in a competitive introduction of the minivan into their business model. In the end, Ford did not save the expense of introducing a minivan (as they had hoped). They had to do it anyway, as a response to Chrysler. However, because they let Chrysler get a head start, Chrysler got most of the benefit of the minivan.

THE ANALOGY
As this story illustrates, one can create logical arguments to kill a new project, as Ford did with the minivan. One can even back up that argument with solid financials. However, that does not always mean that the new project should be killed.

Logical arguments and financial models can be flawed. A natural bias to kill new initiatives can exist in an organization. This bias can cloud one’s judgment, leading to a flawed analysis.

Due to a bias towards past success, Ford failed to take into account the inevitability that station wagons would eventually become passé. By not proactively planning for their replacement, they allowed Chrysler to replace them.

Businesses need to be aware of the potential for this bias, so that they do not fall into the trap which hurt Ford.

THE PRINCIPLE
In the last blog, we looked at how a bias to “go” on new ventures can hurt a company. In this blog, we will look at how a bias to “no go” on new ventures can also hurt a company. We will look at the causes of the “no go” bias, how it can distort our analysis, and questions to ask ourselves in order to keep the bias from causing us to make the wrong decision.

1. Bias Source #1: Avoiding the Hammer
The old saying is that the hammer hits the tallest nail. If you stand out too much, you are vulnerable to attack. New ventures have high visibility and stand out. If they fail, you run the risk of being attacked. Their failure becomes your failure. By contrast, it is easier to hide in the bureaucracy of the established businesses.

2. Bias Source #2: Avoiding Accountability
If you say “go” and the new business fails, there is hell to pay, because a highly visible loss is right there on the books for all to see. However, if you say “no go” and the business would have been great, there is less of a backlash, because it is all subjective. Hence, if you want to avoid accountability for your decision, it is easier if you say “no go.”

3. Bias Source #3: New Game Threatens My Game
You’ve been working yourself up the corporate ladder playing by the old rules associated with the old way of doing things. The new ways could make your “game” obsolete. To protect your personal future, you need to protect the ways of the past.

4. Bias Source #4: Short-Term Pressure
There tends to be more pressure on hitting the targets for the current month or current quarter than for long-term profits. Since new initiatives usually have a near-term drain on profits, there is a tendency to put them off, so they won’t hurt near-term earnings.

5. Bias Source #5: Not on My Watch
The remaining tenure for most CEOs is relatively short—shorter than the time for a new initiate to have a positive impact. The leaders may be retired or have moved on by the time the new initiative pans out. As one CEO put it, “Why should I invest in something that hurts earnings on my watch, but provides benefits for my successor that he will take credit for?”

6. Bias Source #5: Fear of Cannibalization
As we saw in the story, new projects often cannibalize older businesses. The thought is that by avoiding the new businesses, we can protect the old ones while at the same time avoiding all that new investment.

These factors can cause a bias to say “no go” to projects which should move forward. They can even distort the financial analysis, to make “no go” look better than it should.

1. Distortion #1: Old Cash Flow Will Go On Forever
New initiatives are often compared to the status quo. If you assume the status quo will always be strong and healthy, then it is hard to justify change. However, it is a fact of life that all strategic initiatives eventually fail. Customer desires change and innovations from others change demand. Product lifecycles eventually reach maturity and decline. The “next big thing” eventually becomes “that obsolete thing.” 8-Track players were once the rage in music. Now they are junk.

The cash flow on the old business will not go on forever. If you don’t replace it, another company will. Either way, there is inevitable decline. Make sure you put it in the model.

2. Distortion #2: Things Won’t Change if I Don’t Change
Ford thought that if they didn’t build the minivan, the minivan would not be built. This, as we saw, was not the case. Innovation is going on all over the place in your industry. If you can see the potential in the new venture, so can others. Just because you like the status quo and don’t want change (because right now you are the leader) does not mean that everyone likes the status quo (especially the non-leaders). Change is inevitable. Either you can take advantage of it (by action) or be hurt by it (through inaction). Therefore, your modeling should assume changing conditions caused by others.

3. Distortion #3: Old Beasts don’t Need to be Fed
To keep older businesses vital, one needs to reinvest in them. For example, I know of a retailer who built a lot of stores in the 1970s and 1980s and then hardly ever reinvested in those locations. Eventually the stores looked rather shabby. In addition, over the next 30 years, those neighborhoods changed and became less desirable locations for stores. People wanted to shop the newer, nicer stores of the competition which were closer to their new homes, rather than drive into the dangerous inner-city locations where these old shabby stores were. By not reinvesting in the old business and refusing to relocate those stores to better locations, the company eventually went bankrupt. In the near term, those relocations looked more expensive than staying in the older locations. Over time, however, the lack of reinvestment killed the old business. Does your business model include reinvestments in the status quo? The old beasts still need to be fed, or they will die.

So how can we avoid this bias and resulting distortions? If helps if we ask ourselves the following questions before making a decision.

1. If you were assured of a promotion regardless of the success or failure of the new initiative would you still want to kill it? (This unlinks your fate from the fate of the project, so that you can look at it more objectively)

2. If near-term pressures were eliminated, what would you do? (near-term is biased towards status quo) Keep in mind that astute investors value your firm based on future cash flow potential, not history. If you can convince them that these are good long term investments, they will support you. It helps if incentive programs de-emphasize near-term and also reward good long-term decisions.

3. What if another company says “go” to your “no go” decision? Can you survive the impact to your core businesses? (This provides a more realistic way to evaluate cannibalization)

4. In your model, are you adequately feeding the old beast to keep it relevant or are you choking it? Either the modeling needs to include lots of cash to invigorate the old, or the future prospects for the status quo need to be significantly reduced. Make sure the residual value on the status quo does not overstate its potential.

SUMMARY
Since all current initiatives will eventually fail, there is a need to continually reinvent a firm with new initiatives. Otherwise, your company will fail when all the current initiatives fail. Unfortunately, it is often difficult to justify the cost of reinvention while the old initiatives are still cranking out healthy cash flows. This creates a bias to kill off new initiatives. The more we are aware of this bias, the more we can avoid its disastrous consequences.

FINAL THOUGHTS
Just as the station wagon did not live forever, it appears that the minivan is now in decline. Crossover vehicles are starting to take its place. And guess what? Ford has aggressively gone after the crossover business, because they do not have many minivan sales to cannibalize. By contrast, Chrysler has been slow to get into the crossover business, in large part due to not wanting to cannibalize the most profitable piece of their portfolio (the minivan). Times may change, but the mistaken logic appears to live on.

Strategic Planning Analogy #275: Bias to Go

THE STORY
I worked with a company that desired to have a new corporate headquarters building. The company at the time was spread over several buildings around the city, making things inconvenient. Not only would a new headquarters get rid of that problem, but many thought it would be fun and a boost to the ego to work in a flashy new building.

The problem was that the new headquarters was difficult to justify based on financials. New headquarters can be very expensive, and the benefits to the rest of the business are difficult to quantify.

To make the financials more appealing, two assumptions for the model were changed. First, the resale value of the old headquarters was increased in the model. In other words, if we moved to a new building, it was assumed we would get more when we sold the old building (to help pay for the new building).

Second, it was assumed that the company’s unusually rapid growth rate would continue for awhile. This meant that the current infrastructure was even less adequate moving forward (we’d have to add more office space anyway). It also made the cost of the new headquarters more efficient, since a bigger headquarters costs less per square foot to build.

These changes gave the new headquarters scenario just enough of an edge so that it looked slightly better to build an impressive new corporate headquarters building than work with the current hodge-podge of buildings. So the decision was made to “go” with the flashy new headquarters.

Well it takes a year or two to get one of these headquarters built. Between the time of giving the go-ahead to build the new headquarters and the time it opened, two particular things occurred. First, the bottom dropped out of the real estate market, meaning that the old headquarters sold for a lot less than what was put in the model. Second, internal growth had stopped and the company had actually shrunk the headquarters staff. So the new headquarters opened up as about half empty. At this point, it would be difficult to justify that new building. But it was really cool and people still liked the prestige of being there.

THE ANALOGY
At the end of the day, that huge new headquarters was not built because it was the wisest financial move. It was built because people wanted a cool new building to work in. That bias of desire overtook common sense. The financial model had been unjustly modified to make it look like a wise move, but in the end, reality told a different story.

In the business world, there are all sorts of similar types of business decisions. They are some variation of this question: Do we stick with the old and familiar or go with the new and different? This applies not only to headquarters, but to potential new product offerings, brand extensions, acquisitions, diversifications, and the like. The sexiness of the new initiative lures people in like the songs of the Sirens.

People seem to forget that most new initiatives fail. They think that this one is the exception to the rule. So they push forward on the new initiative and create one more disaster, confirming one again that most new initiatives fail.

THE PRINCIPLE
The principle here is that when it comes to “go” vs. “no go” decisions on new initiatives, there are many internal biases towards “go.” If we don’t understand the impact of these biases, we can become blinded into making less than ideal decisions. Therefore, this blog will look at three things: Sources of a bias to “go,” distortions to the decision-making process which come from the biases, and the questions we need to ask ourselves to unmask the bias to “Go.”

In the next blog, we will do the same thing for the biases to “No Go.”

1. Bias Source #1: Fun Factor
Let’s face it. It’s fun to work on the “new” project. It sure beats working on the old routine stuff. You get to go to lots of committee meetings (and eat lots of yummy donuts). If you keep saying “yes” the fun continues. If you say “no” the fun ends.

2. Bias Source #2: Freedom
The old routine stuff has all sorts of tight budgets and short performance deadlines. There is a clear line of authority and accountability. It’s a hassle and you get yelled at a lot if budgets are missed. By contrast, the new stuff is usually more open-ended. There is a lot more freedom and less accountability as it is being set up.

3. Bias Source #3: Career Enrichment
The new stuff is highly visible. If you can make the new project a success, you can quickly become a hero in the organization. That can lead to all sorts of bigger titles, promotions, perks and money. Your rise in the organization tends to be faster if you work on the new stuff (and succeed), so you are biased to work on these projects and promote them so that you have a chance to succeed with them.

4. Bias Source #4: Linkage of Person and Project Image
Of course, this also works in the other direction. If the highly visible new project fails, your “failure” is very visible. The more a person sees their personal success as linked to the project success, the more likely they will push the project forward. After all, saying “no” to the project is viewed as being like saying “no” to the people working on it. This creates a strong bias to avoid shutting a project down, no matter how bad it looks.

5. Bias Source #5: The Panacea Phenomenon, or the Optimism of Ignorance
We all tend to know the shortcomings of our current businesses. However, when we venture into new territory, there are more unknowns. Given all of the other biases, we tend to take a more optimistic slant to those unknowns. After all, we have expectations to grow corporate sales and profitability. We know we cannot hit those aggressive goals with our current ventures. Therefore, the gap has to be filled by new ventures. If we say “yes” to the new ventures, we have a shot at filling the goal (our panacea for our problems). If we reject the new ventures, we run out of options for filling the gap.

So, given all of these biases, there is a tendency to create financial models which are biased towards moving the new ventures forward, even when they should be halted (whether we are aware of it or not). Some of the ways the models get distorted are as follows:

1. Distortion #1: Forget about Life Cycle Impacts
New ventures are often very profitable in the beginning. This is because it tends to be in an uncontested space with little competition. However, once we show that profits exist in the space, others will jump in. Competition will erupt and profits will go down. In addition, all that new business is probably coming to us at the expense of someone else’s old business. As soon as they see their business being attacked, they will fight back and get some of that business back. This is all part of the natural rhythm of the product life cycle. Eventually the industry matures and profitability drops to something near the cost of capital. If you only project the good, early times into your model, you will distort the model to be too optimistic. This is particularly true if there is a large residual value at the end of your model’s time frame.

2. Distortion #2: Forget about Transition Costs
The models for the new business often look at the venture once it is up and operating smoothly. That’s nice, but there is usually a costly transition to get there. For example, I’ve seen lots of people model out the benefits of an acquisition and only look at how the acquired company will perform once acquired. They leave out all sorts of very expensive costs associated with doing the acquisition, like investment banker fees, legal fees, PR fees, severance costs on the people let go, and so on. When you factor in all of these transition fees, a supposedly “good” deal can become a money loser. There are also substantial transition costs in new ventures. It takes a lot of time and money to get them up and running, which may not get into your model.

These two distortions can make your models biased more towards the new venture than they should be. They reinforce the bias to “go” which was already there, increasing the likelihood that you will vote to “go” when “no go” is the better response.

To help avoid the consequences of making poor decisions due to the bias to “go”, ask yourself these questions:

1. If it were your money, would you still do it? (We tend to have less of a bias to “go” when it is our own money at stake)

2. If you were assured of a promotion regardless of the success or failure of the project would you still want to go forward? (This unlinks your fate from the fate of the project, so that you can look at it more objectively)

3. Can the new venture overcome the competitive reactions and the copycats that will naturally occur as part of the life cycle? (Put it into the model and see)

4. Can the new venture absorb all of the transition costs and still work? (Put them in the model and see)

SUMMARY
Since most new initiatives fail, there is reason to be skeptical when new initiatives are proposed. Double check to see if the new initiative is truly worthy of a “go” vote. Don’t just assume the analysis is telling the whole story, since the bias to “go” can distort the analysis (even if you are unaware of it at the time). And check out your own biases to be sure you are choosing based on reality not some irrational emotion.

FINAL THOUGHTS
According to the book Parkinson’s Law, “During a period of exciting discovery or progress, there is no time to plan the perfect headquarters. The time for that comes later, what all the important work has been done. Perfection, as we know is finality; and finality is death.” So when people are pushing for a luxurious new headquarters, it may be time to get out of there, before it is too late.

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.

Analogy #202: Tax vs. Toll

THE STORY
Once there was a king, King Antaro, who wanted to use his power to make a lot of money. What he did was build only one road which went across the entire kingdom. King Antaro then put toll booths all along the road.

King Antaro was excited. He thought to himself, “I have a monopoly on all road traffic across my kingdom. Anyone who wants to get from one side of the kingdom to the other has to use my one road. I can charge whatever I want for people to use this road, since they have no other choice. I’ll be fabulously rich in no time.”

So King Antaro had his toll booths charge extremely high rates for passage on his road. Nobody wanted to pay that much to travel across the kingdom, so they avoided the kingdom altogether. They found alternative routes in neighboring kingdoms and did their business transactions with others.

As a result, not only did King Antaro not get the toll money he expected, the kingdom’s economy suffered from a lack of business. It was leading to the King’s financial ruin.

Suddenly, King Antaro had a new idea. He eliminated the tolls booths and allowed everyone to build any kind of transportation business they wanted in the kingdom. The only condition was that they would pay the king a very small tax from their profits. New transportation businesses flooded into the kingdom. This lead to a growth in the rest of the economy.

The small income tax on all of this growth added up, and in no time King Antar became fabulously wealthy.

THE ANALOGY
One of the long-standing principles of economics has been that monopolies create extraordinary wealth for the one who owns the monopoly. The thinking is that the monopoly allows the owner to charge excessive fees, which “unfairly” exploit everyone else (who have no alternative but to pay the fee). As a result, most governments try to limit monopoly practices by businesses.

The modern equivalent of the monopoly has been the platform wars. Companies try to create technology platforms that become the industry standard—a near monopoly for anyone wanted to do something in that space. Then, like King Antaro, they set up high tolls for anyone who wants to use that standard. Microsoft has made a fortune from owning the operating platform standard for PCs. Sony’s Playstation II made a fortune off the tolls it charged from anyone wanting to create a game which plays on their proprietary gaming standard.

Going back even further in time, Philips made a fortune off of owning the monopoly on the cassette tape standard, and extracted a toll off of every cassette made. It is no wonder that the companies behind Blu-Ray and HD DVD spent so much money trying to make their version the standard for next-generation DVDs.

King Antaro found out, however, that there are limits to this monopoly/toll booth phenomenon. First, if you charge too much, you will limit the usage of the standard. Second, there are always alternatives to your near-monopoly. King Antar eventually realized that if he gave up control and made the market open, he could make more money off fair taxation of the open market than he could off exploitive tolls from the closed market.

As we will see in this blog, the evolving business model seems to be abandoning the old platform wars and moving to something more akin to this taxation model. As you build your strategies into the future, you should keep this in mind as you develop your business model.

THE PRINCIPLE
The principle here is that monopoly-like business models appear to be drifting away from the exploitation model (extracting tolls on a tightly controlled path) to a taxation model, where you can do anything you want as long as you pay a tax.

The problems with the tightly controlled model are that:

1) Nobody likes to be tightly controlled. They will resist and seek out alternatives (or build a competing system).

2) Controlling the path usually results in restricting the commerce. Less total business will take place because you are not smart enough to dream up all of the potential possibilities.

3) Tight controls tend to restrict innovation and progress (standards must be maintained). As a result, instead of integrating incremental change, you get replaced by an innovative leap by someone else.

4) Tolls tend to be high fixed charges, no matter how successful the toll payer is. This tends to restrict risk and experimentation.

5) Tolls require people to behave in a particular way (tell you which road you have to take). This is not always the best way.

By contrast:

1) If the system is fully open, there is less incentive to avoid it or build an alternative (standard is more likely to thrive for a longer period).

2) As others add innovative applications for your platform, they make the standard ever better, ever stronger (the virtuous cycle). The market for your standard increases in size.

3) Taxes do not demand how someone makes their money…they just take a cut, regardless of what you do. This freedom can result in more money-making ideas…more things to tax.

For an example of this principle, compare how Facebook is trying to use more of a traditional “toll” approach for how people use its site, whereas Apple is using more of a tax approach for its mobile Apps Store. The “tax” for Apple, in this case, comes from retail markup on what Apps others build. I borrow this comparison from Umair Haque, Director of the Havas Media Lab, who is a Harvard Business blogger:

“Apple took something terminally closed - the mobile value chain -and pried it radically open. Facebook - still thinking in yesterday's terms - took something radically open - the www - and is trying to make it a little bit more closed.

“Apple took something radically evil - the mobile industry - and is making it a little bit more good: finally, now that it's usable, there's an incentive for you to get stuff that's actually useful on your phone, instead of just being a zombie whose head is getting ripped off by suits scheming up hidden charges in boardrooms.

“Facebook - still thinking in yesterday's terms - took something radically good - the self-organizing incentive for people to share knowledge with others on the www - and is making it a little bit more evil: exclude people from accessing it, trying to pollute it with ads, subvert it with pseudo-friends, silo it across mini-networks, dilute it to the point where low-quality apps proliferate like weeds.”

In other words, the near-monopolies of the future will not thrive based on control and exploitation. Today’s transparent and consumer-advocacy marketplace will not tolerate it. Instead, tomorrow’s near-monopolies with thrive by being the most consumer-friendly, with the gains coming from sharing in the success of those who choose to opt in.

The irony is that the monopoly-like models of the future will be a net result of avoiding all of the practices which created the near-monopolies of the past. Practices like control, exclusivity, ownership, and dictation of terms are giving way to openness, inclusion (even bringing competition and customers into the process), lack of ownership, and uninhibited free flow of ideas.

Or put another way, we used to think of “monopolies” as one large, single unit of power. Future “monopolies” will be nearly infinite units of independence who gain from working together. The “monopoly” comes from having one large, single unifying force which causes everyone in the value chain to be better off than if they were not in the value chain. And the one managing that force in the chain (and collecting the taxes) is the best off of all.

Umair Haque refers to this fragmentation as “atomization of the value chain.” But I think Umair may be missing the point that even if the value chain is atomized, they system only works if there is still a monopolistic type of force to enable the atoms work together. And no matter how open Apple’s App Store is, Apple still wants to be the sole owner of the store. This is not the end of monopolistic-like activities. It is just a different manifestation…a kinder, gentler force, but a force nevertheless.

Apple’s larger move into retail—Apple Stores, I-tunes and App Store—is no accident. It looks like the retailer may be in the best position to become the tax gatherer. Let everyone else make whatever they want to sell (the more the better), but if you are the best way to buy it (and store it and play it back), then you have the best chance of putting your tax-like markup on most of the transactions. And, by the way, it also makes it easier to sell your own stuff.

SUMMARY
In the past, it was concentration of power and exploitation (through tolls) which tended to lead to greatest profitability. In the future, it appears that dispersion of power will lead to the greatest profitability—provided that you manage the enough of the way everyone interacts so that you can “tax” it (get a cut of their success).

In building your future business models, you may want to look for strategies where you can “atomize” a market, yet still control the system enough to extract a “tax.”

FINAL THOUGHTS
EBAY used to try to exert more control over its system, but they are finding that if they open things up and act less like a toll-keeper and more like a taxman, they are better off. Now, some of that conversion was due to bowing to the demands of their partners, rather than a premeditated strategy. Why be like EBAY and wait until forced into it—be proactive.

It’s Better to Consistent Than To Be Accurate

THE STORY
Awhile back I was working on a project with one of the world’s largest investment banking firms. My task was to create a model to predict the detailed monthly financials of a retail chain going out about 5 years into the future.

My team created a very complex model which taxed the extremes of the capability of an excel spreadsheet. This model was so complex, and involved the interplay between so many different inputs, that the output in any given month fluctuated around a bit.

These little fluctuations bothered the people at the investment bank. They wanted nice, smooth progressions in the numbers over time. Therefore, they scrapped the huge, complex (but fairly accurate) model and built a simplistic little ditty based on extrapolating a couple of metrics. It didn’t exactly tie to all of our detailed assumptions, but all the numbers moved in smooth progressions over time and it roughly matched the macro trends of the complex model.

What these investment bankers told us was that it was better to be consistent than to be accurate. So the simple little model won out over the complex one.

THE ANALOGY
Businesses are made up of hundreds of thousands of little tasks which come together to create the financial outcomes. In addition, there are probably just as many activities in the external environment which also impact one’s financials. Depending on how all of these activities come together, one will get different results.

Trying to model all of these events and accurately guess how they will play out in the future is an extremely difficult task. It is easy to get lost in the minutiae and lose site of the big picture.

The task of strategy is not to accurately predict all of the events of the future in great detail. The task of strategy is to provide enough insight into the future so that whatever decisions you have to make “today” will have enough futuristic context so that you can properly choose a path that will improve your condition over time. Additional details do not always cause additional insight. Sometimes, they just cloud the issue and make it more difficult to see the big picture (for more on this topic, see the blog “Too Many Clocks”).

Therefore, instead of using up valuable time in building extremely complex (but potentially more accurate) models, that time could be better spent in understanding the strategic implications of the big picture (which can be derived with a simpler model) and developing the right strategic alternatives.

THE PRINCIPLE
Strategists are in the business of selling—selling visions, selling ideas and selling alternatives. For example, if you cannot sell a vision of the future, then you cannot get consensus on what to do to improve yourself in the future. Facts are a key element in the selling process, but it is not the only element. Beyond a certain point, additional facts do not increase your persuasive abilities. Other issues also come into play. That is why it can be more important to be consistent than to be accurate. In particular, there are three principles which cause this statement to be true.

1) The Principle of Focus
2) The Principle of Credibility
3) The Principle of Large Numbers

These are discussed in more detail below.

1) The Principle of Focus
It is difficult enough trying to reach strategic decisions when people are focused on the right issues. It is virtually impossible if people are focused on the wrong thing. In selling a vision of the future, what you want is to have people focused on the key assumptions which would cause you to reach a different conclusion, depending on how you think the assumption would turn out.

For example, if you were trying to create a strategy in the health care industry, you might come to a different conclusion depending upon your assumptions around how active the government will be in managing health care in the future. Therefore, discussions around expectations of government involvement in health care management would be very important in developing your strategy.

If you produce data which looked like the data that came from the complex model I referred to earlier, your added accuracy would cause your numbers to have little wiggles in them over time. Your audience could get fixated on the wiggles and start asking questions about all the nuances in you model which caused the wiggles. Then your conversation would be side-tracked into all sorts of minutiae. The big issues, like how much the government will get involved in health care, could get squeezed out of the discussion.

Models are only representations of assumptions. Their goal is to help roughly quantify the direction and magnitude of the impact of an assumption on your business model. That way, you can see the impact of the assumption on your business and then make decisions which optimize under that assumption’s scenario. If your model is so complex that it clouds the impact of the assumptions, then the model is no longer useful in helping you make decisions. Rather than focusing your audience on the key assumptions, it gets them focused on “wiggles.”

In general, most key assumptions revolve around how you think an issue will trend—for example, will it get stronger, weaker, or stay the same over time. Since we tend to think of these assumptions in terms of smooth trends, then the model is more effective in helping us understand these assumptions if it also reflects consistently smooth trends. Again, the goal is not accuracy, but usefulness in making decisions. Consistently smooth trends help keep us focused on the assumptions and their general impact. That is typically enough information to make the right decision for today.

2) The Principle of Credibility
One’s ability to be effective at selling is directly related to one’s level of credibility. If your audience believes you have credibility, then you can be more effective in selling your visions, ideas and alternatives. Conversely, if you have no credibility, it doesn’t matter what you say or do, because nobody will take you seriously.

We are conditioned to believe that life tends to move rather consistently through time. If we think of our assumptions in consistent terms (e.g., things getting gradually better or gradually worse over time), we would also expect their impact to be consistent over time on the model. If your model does not have this type of consistency, it makes people question the accuracy of the model. Complex models with wiggles in them are difficult for people to understand. If the model is so complex that they cannot assess its accuracy themselves, they are less at ease and have to trust even more in your credibility. But if the wiggles cause them to think that there must be something wrong with the model, because “it doesn’t look right,” then you have lost your credibility.

It is better for your credibility to be a little less accurate in your modeling and create models with smooth consistency over time, so that the model appears more “believable” to your audience. As long as the simpler model does not distort the facts enough to come to the wrong conclusion, the simpler model will be a more effective selling tool.

3) The Principle of Large Numbers
The law of large numbers says that it is often easier to forecast an aggregate outcome of many factors than to forecast the all of the individual outcomes of every factor. This is true because there is often more variability in the outcomes of the individual components than in the outcome of total integrated unit. When you aggregate many parts together, the variabilities of the individual parts tend to offset one another. Because the offset, they reduce the variability of the whole.

For example, if you were a retailer trying to forecast your gross margin, it may be easier to forecast the aggregate gross margin for the entire company than to forecast the gross margin on every single item you might sell and then add all of the items up. This is because the variability on the gross margin for each individual item sold is much higher than the variability over time in the aggregate for the entire company.

Therefore, building a simple model that creates smooth consistent trends on a few key aggregate outcomes might actually end up being more accurate than a model which tries to forecast all of the individual components. Hence, by concentrating on consistency over accuracy, you might end up with better consistency AND better accuracy.

SUMMARY
Effective strategy building requires effective salesmanship. Better salesmanship (and hence better strategy) usually comes from simple models that are easy to comprehend and follow smooth trends. That is why it is more important to be consistent than to be accurate.

FINAL THOUGHTS
One of the problems we can often run into is trying to make too many decisions too soon. Frequently, some of the more tactical issues are better handled when delayed until closer to the time when the tactic must be implemented. By keeping strategic models relatively simple, it keeps discussions on the strategic level (where more lead time is needed) rather than getting into the tactics too soon.