Friday, May 22, 2009

Strategic Planning Analogy #259: Quant Jocks

The term “Quant Jock” refers to people who earn their living by being excessively good at developing complex analytics via the computer. Quant Jocks have been an integral part of the Wall Street financial community for years. However, in the past year or two, their reputation for financial wizardry has become a bit tarnished.

First, it was the quant jocks who helped develop all of those sophisticated repackaged mortgage bundles, which nobody really understood and which were a major factor behind the recent housing crisis. Second, it was the quant jocks who developed sophisticated computerized stock trading programs. These sophisticated stock trading programs helped to increase the negative impact of stock melt-down in the fall of 2008.

The irony is that the quant jocks had claimed that all of their sophistication would help to reduce downside risk. Instead, it now appears that their models actually increase risk, particularly if events fall outside the programmer’s narrow assumption parameters (which inevitably will happen).

There are many similarities in the goals of Wall Street and of business strategists. Both are trying to find a way to optimize the blend between profitability and risk. The goal is to create as much profitability as possible within a particular risk tolerance.

For years, Wall Street has used a lot of quant jocks in the attempt to achieve that goal. Now, we are seeing more and more of that complex analytical approach being applied to strategic planning’s goals. Planning techniques like Scenario Planning and Real Options seem to be falling under the influence of quant jocks.

At its worst, overly-quantified scenario planning can become similar to those mortgage-backed securities which helped bring down the housing market (if the quant jocks are allowed to go wild). You bundle up all these scenarios into one massive computer program and come out with some sort of bundled scenario risk formula that doesn’t quite match any particular scenario. This makes it hard to know what to do.

The same can happen to a Real Options approach to minimizing risk—lots of math leading to conclusions that tend to mask what is actually going on in a strategy. If the parameters are in the assumptions are off by too much, they whole thing can collapse.

It’s not that math or analytics are bad per se. Scenario planning and real options can be valuable tools. The knowledge and insight coming from rigorous analysis can be useful—on Wall Street and in strategy. But taken too far, analytics can obscure your view. Instead of knowledge and insight, there are incomprehensibles and too much blind faith in the cold, unthinking calculations of formulas inside a black box. And, as we saw in the story, rather than reducing risk, it can lead to increasing risk—and creating melt-downs. Is this what you want for your strategy?

The principle here is that strategies need to be far more than just numbers and formulas. In fact, an excess of “quant jock” thinking can actually increase the risk of failure for your strategy. The logic behind this point of view are as follows:

1. Continuity Vs. Discontinuity
Quant jocks tend to live in a world of continuity. The idea is that the world operates by a set of rules. The role of the quant jock is to model those rules and optimize the nuances for the benefit of the company.

By contrast, good strategists tend to focus on discontinuities. Nearly all great strategic moves are done in a way that totally destroys the rules of the status quo. For example, the great success of the Ipod comes from more than just the creation of a device. It was a reinvention of the rules for the entire industry—of how music was sold (itunes), organized and listened to.

The same was true for the iphone—creating an entirely new apps-based business model. Amazon was not just another outlet for selling books—it was a new way to think about shopping, with lots of new tools and information to create a wholly different shopping experience.

As we saw with the housing crisis and the stock market melt-down, the quant jock systems failed miserably when there was great discontinuity. They weren’t built for such rapid change. I fear the same is true in the strategic world.

Rules-based models don’t help you discover a new set of rules, nor do they tell you how to react when the old rules no longer apply. Rather than overanalyzing the world of today, strategists should be dreaming of how to destroy the world of today for their benefit.

Instead of modeling the world as it is, we should model potential new realities. Yes, there is still some analytics involved, but the analytics are only as good as the dreams being analyzed. Great dreams are more important than great analytics, because the dreams are what create the new business models to be analyzed. Analytics without these dreams is just random noise.

2. Beating the System Vs. Being the System
In the stock market, the goal of the quant jock is to find little holes in the rules, so as to beat the system on very narrow deviations. Unfortunately, what happened was that a large number of firms adopted these models and started trying to beat the system in pretty much the same way.

When that happens, you are no longer beating the system. Instead, you become the system. The herd mentality caused so much trading to be done in this similar analytical manner, it became harder to make the models work (too many people chasing too few holes). Then, when the market fell apart in the fall of 2008, the models all worked in unison to force prices down further and faster.

A similar situation occurs in strategy. “Me Too” strategies, where you try to copy the leader, rarely lead to great riches. The leader usually stays the leader and you fight over the few crumbs left behind.

If the herd mentality takes over and everyone tries to win in the same way, it tends to commoditize the business. This usually leads to price wars, where the prices drop just like those stocks did (and so will your profits).

Analytics by nature tend to mimic others, because they are trying to model the world that exists. Quant jocks may come up with original ways to push around the math, but they rarely come up with original new strategic options. If you truly want to beat the system, you need to create a new position, working under a new set of rules—a place where you can be the leader and the rules work in your favor. Brainstorming, not whirring computers and complex spreadsheets, are the priority.

3. Creating Vs. Measuring
Quant jocks like to measure things. The problem is that when you are creating a brand new business model in a brand new space operating under a brand new set of rules, there isn’t much to measure beforehand.

If you wait until the market is fully developed, so that you have more to measure, it is usually too late. The market leaders have already established themselves and the rules are already working in their favor. The game is over.

Apple succeeds by blazing new trails, doing new things—be it the Ipod/Itunes model, the Iphone/Apps model or the Apple Store model. It doesn’t wait until the market is fully developed and measurable. It takes calculated risks. Sure, calculated risks are still calculated, but the strategy is not put on hold until perfect information is available.

Sometimes, a little consumer research can help. But even here, if the concept is too radical, the customers may not at first be able to internalize how they would behave in that new world, thereby making the results unreliable. Small beta tests may be better than analytical modeling.

Well, if the new world is not yet available to measure, we can always still measure the current model, right? Unfortunately, overemphasis on measuring the business model you are trying to destroy usually will not tell you the best way to destroy it. That’s a lot of effort for questionable reward.

Strategy is primarily a creative act—building a new business model that did not exist before. A “quant jock” mentality/focus typically is not the path to get there. Instead, it tends to keep one mired in the past. Sure, a little analytics can help fine-tune the creative idea, but it won’t develop it. Therefore, rather than obsessing on analytics, obsess on creative model building and then use analytics as a secondary support mechanism.

Sir Isaac Newton did not discover gravity through analytics. It was a creative burst prompted by watching apples fall from trees. The analytics came later. Strategists should probably spend more time pondering things like falling apples and less time pouring over computer printouts.

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