Counteracting Our Obsession with Speed

In the quest to get as close as possible to the speed of light for faster decision making, it appears some companies are moving too fast and thus making very costly mistakes. When windows of time are compressed to near zero, there’s no recovery time for critical errors.  In fact, for some decisions (especially those of a strategic nature) it’s much better to take it slow.

Courtesy of Flickr. By Theseanster93

In baseball, scouts love to find pitchers that can throw “the heater”.  Prospects that can throw near 100 mph a few times a game are coveted over those who can rarely top 90.  The mantra for pitchers now is; “throw it faster and see if hitters can keep up”.

However, there’s a renewed interest in knuckleballers, or those pitchers who throw a pitch with little to no spin. For these pitchers, the ball is supposed to “dance” on its way to home plate at speeds of 60-70 miles per hour. For baseball hitters, trying to track down a dancing knuckleball is extremely tough. It’s hard to track the lively movement of the knuckleball, much less adjust to low speed at which they’re thrown.

Why the revived interest in knuckleball pitchers? Sports Illustrated writer Phil Taylor says; “(In baseball) we need the knuckleball to help counteract the obsession with speed, to prove there still is a place for nuance and skill.”

Phil Taylor has it exactly right –in baseball and in the business world.

Our world is obsessed with speed. Faster food, hurry up offenses in football, faster computers, and even faster war-making. As I have detailed before, it’s everything—faster.

But conversely, sometimes moving too fast is dangerous. There are some decisions that should not be made too quickly, especially those that could benefit from more data collection, or decisions where there is ambiguity and complexity. United States President Barack Obama mentioned in a Vanity Fair article; “Nothing comes to my desk that is perfectly solvable…so you wind up dealing with probabilities, and any given decision you make you’ll wind up with 30-40% chance that it isn’t going to work.”

Even when speed is deemed a competitive advantage, sometimes faster isn’t better. For example, Knight Capital Group lost $440 million dollars when a “technology malfunction” launched erroneous trades on their behalf.  Trading at near the speed of light, there simply wasn’t enough time to recover from the initial errors leaving Knight with nearly a $500 million loss in the span of just 45 minutes.

The need for speed comes at a price of compressed decision making windows and non-recoverability for critical errors. Worse, when errors from a few players cascade through complex systems, the feedback effects can severely damage all participants in the ecosystem. It’s as if the butterfly flapping its wings really does bring about category four hurricanes.

Not every decision needs to be made faster. There will always be a place for decisions made with “skill and nuance”, where it’s important to slow down, see the bigger picture, and adjust our swing and timing for the occasional erratic knuckleball thrown our way

One Secret for Success in Cloud Computing – Fewer Choices

Retailers have long followed the mantra of “stack it high and watch it fly”. In fact, stores often pile goods to the ceiling, make shoppers navigate in-aisle displays, and price everything with bright and obnoxious signage.  However, some progressive retailers have discovered that reducing “choice” can actually boost sales.  And in terms of cloud computing, one successful vendor has taken a page from this retailing playbook by removing confusing computing choices.

Image courtesy of Flickr

In “Less is More in Consumer Choice”, I cited a 2007 study in which researchers conducted experiments in a shopping mall aimed at understanding mental fatigue associated with too much choice.  The studies concluded that when faced with too many buying options, study participants couldn’t stay on task in completing projects—in effect their brains were overwhelmed by choice overload.

The folks at Amazon Web Services (AWS) have figured this out.  Cloud computing can already be avery complex endeavor with behind the scenes infrastructure consisting of interconnections among servers, networks, applications, controllers and more.   So, by abstracting the complexity of cloud architectures via a simple user interface, AWS makes cloud computing easy to consume.

But AWS has taken simplicity a step further by actually reducing mental clutter and choice. Cloud Scaling CTO, Randy Bias notes AWS reduces choice by simply providing infrastructure as a servicewithout all the bells and whistles associated with offering the entire cloud stack.  AWS provides and maintains virtualized storage and compute resources, AWS users need to provide whatever else they require. AWS, Bias says, has “reduced choice by simplifying the network model, (and) pushing onto the customer responsibility for fault tolerance” as server instances are not persistent.

Bias also explains that AWS’ EC2 service requires developers to fit their applications to the infrastructure, not the other way around.   Amazon is effectively saying to developers, ‘Build your applications with our infrastructure in mind’ so they are cloud ready, instead of ‘build your application’ first, and then leave it to AWS to figure out how to scale it.

Going forward, there will be plenty of technology savvy buyers with the ability to sort through myriad complex cloud computing options. However, there will also be large segments of cloud buyers (i.e. those in lines of business) that will want to sign up for cloud computing with corporate credit cards. These buyers will appreciate simple user interfaces, easy to access resources, and less mental clutter and exhaustion for buying decisions.

When it comes to choice architecture for cloud computing, AWS shows us less really is more.

Unintended Consequences of Combining Speed with Technology

Technology is often hailed as innovation vehicle, productivity booster, and enabler of a higher standard of living for all global citizens. However, the field of finance provides an interesting backdrop for what happens when an industry is pushed to its technological limits in the pursuit of automation and speed.

Since advent of the telegraph, and all the way until early 1970s, stock prices were displayed on a ticker tape printed in near real time.  The ticker tape (via telegraph technology) was a drastic improvement in delivery of information, since brokers could gain stock prices with only a 15-20 minute delay from original quotation.

Setting the dial now to the year 2011, we now see super computers trading stocks—not with humans—but, with other super computers. Forget delays in minutes or seconds, today’s super computers trade in microseconds and are increasingly “co-located” near stock exchange servers to reduce the roundtrip time for electrons passing through networks. In fact, on most trading floors, human brokers are obsolete as algorithms are now programmed with decision logic to make financial instrument trades at near light speed.

We’ve come a long way since the decades of ticker tape, says Andrew Lo, professor at Massachusetts Institute of Technology (MIT). At a recent conference Professor Lo mentioned while technology has opened markets to the masses (i.e. day-trading platforms) and reduced price spreads, there are also downsides to automation and speed.

First, he says, there is the removal of the human element in decision making. As super computers trade with each other in near light speed, there are smaller and smaller windows of latency (between event and action) and therefore fewer opportunities for human intervention to correct activities of rogue algorithms or accidental “fat finger” trades.

Second, with fiber optic networks spanning ocean floors and super computers connecting global investors and markets, we’ve essentially taken a fragile system based on leverage and made it more complex. Automating and adding speed to an already “fragile” system generally isn’t a recipe for success (i.e. the May 6, 2010 Flash Crash).

Based on these trends, it’s easy to imagine a world where financial networks will intensify in complexity, capital will zip across the globe even faster, and relationships between market participants will increasingly grow more interconnected. Where loose correlations once existed between participants and events, markets will soon move in lockstep in a tightly coupled system.

To be sure, the confluence of technology and finance has been a boon to society in many respects. However, as Lo says, there are “unintended consequences” in the application of the most advanced and fastest technologies to an already fragile system.  Whereas the buffer of “time” to fix mistakes before or even as they occur once existed, now we’re left to clean up the mess after disaster strikes.

In addition, as markets become more tightly coupled and complex, the butterfly effect is more pronounced where the strangest and smallest event in a far away locale can potentially cause a global market meltdown.

The Zero Latency Future is Now

Today’s advanced technology brings us virtual broadband autobahns that move data across the globe with speed and precision. In an attempt to capitalize on fast-moving data, some companies are using sophisticated applications and compute power to make decisions faster than competitors. However, when machines move millions of times faster than humans, there are some implications for the decisions made by marketing professionals.

A previous column “Is the Speed of Decision Making Accelerating?” cited how a century ago, managers could take weeks or days to make important decisions. That’s because before the advent of the telephone, it would take a substantial amount of time for information to travel by courier. Fast forward to the 21st century, most executives now have a mobile device and can be reached at a moment’s notice.

Our global society is moving towards a zero latency world, where the time between decision and action is drastically reduced. And we need to look no further than Wall Street’s high frequency traders for evidence.

John Plender of the Financial Times recently defined high frequency trading (HFT) as a “type of computerized dealing (that) exploits the millisecond gap between news events and their impact on markets … such trading has expanded rapidly to the point where 60-70% of the trading volume is in U.S. equities. Much of this volume is conducted by a very small number of companies.”

So what’s wrong with HFT? Plender cites potential problems, such as the “ability (for high-frequency traders) to see orders before they are public” and the propensity for high-frequency traders to co-locate servers on the floor of stock exchanges for faster trading (something not available to the average investor). In addition, the race is on where the winner in high-frequency trading can close trades as fast as 250 microseconds—faster than you can blink your eye!

The speed of decision-making is accelerating. In HFT, the trend is unmistakable. Machines are trading with and against each other. They’re moving ahead of individual investors, leaving day traders in the dust. And as a Financial Times article notes, speed isn’t just confined to Wall Street:  “Technology has changed many other big markets around the world and tied them more closely together … Such changes has created winners and losers.”

For marketers, the implications of zero latency are clear. For example, did you know that “robots” are purported to perform text mining on press releases when they hit the wire? With analysis completed in microseconds, advanced algorithms then execute trades based on what they’ve learned. Your company’s equity price could go up or down in seconds, based on the words in your press release!

In a zero latency world, what marketers (and other employees) say, write, tweet, and announce can all be used as fodder by the machines to either raise equity prices or destroy shareholder value. Our ability to react and “fix” our mistakes before they are noticed is greatly diminished. All it takes is a bad press release, poorly written whitepaper or negative analyst report.

And it’s not just PR. To borrow a phrase from Thomas Davenport, companies are now “Competing on Analytics.” Marketers must understand that they are now engaged in an arms race with competitors mining their own (and third-party) data for insights—increasingly by the hour and minute, and then taking action to better connect with customers. Companies without these capabilities will increasingly face mammoth disadvantage.

Zero latency decision-making isn’t the future. It’s now. Are you ready?

Eight Things You Should Know About Statistics

Statistics have been called “an engine of knowledge” by one risk management expert. And while it’s true that some business managers don’t have a fundamental grasp of statistical concepts, we also know there is opportunity for misuse of mathematics. Is statistics the “new grammar” or are efforts to attach certainty to life’s events doing more harm than good?

In May 2010’s issue of Wired Magazine, author Clive Thompson laments the poor mathematical literacy of his fellow citizens. For example, he cites people laughing at the concept of global warming as they face some of the harsher winters on record, or the extra-vocal debate on vaccines and possible links to autism. Mr. Thompson would tell us that it’s the trend lines that matter, and we too often look at the trees and miss the forest.

The problem, he says, is that “statistics is hard” and an overall understanding of this important discipline is severely lacking. He says, “If you don’t understand statistics, you don’t know what’s going on, and you can’t tell when you’re being lied to.”

Thompson is correct that statistics are difficult for most of us, and that thinking by the numbers takes training and much effort. It’s also true that one must understand statistical concepts, especially when percentages, populations, and probabilities are bandied about in business and technical press. However, broader acceptance of the power of statistics should be tempered with limitations of this mathematical science.

Before accepting any statistic, study or experiment as gospel, the following should be considered (there may be more…):

1. Assumptions: What are the assumptions underpinning the research? As seen from recent debate on CBO numbers for the U.S. health reform package, assumptions matter tremendously.
2. History: How much historical data was used in the study? What was the time scale? As seen from the 2008 financial crisis, the models used by Wall Street mavens often only took into account 10 years of data in judging the volatility and probability of failure of complex financial instruments.
3. Samples: Are the samples selected randomly? From what populations? Is there enough data for statistical significance?
4. Data Quality: The output is only going to be as good as the quality of data feeding the analysis. Garbage in, garbage out.
5. Survivorship Bias: Author Nassim Taleb points out that “losers are often not in the sample.” Does the analysis include a population of survivors and those who also failed?
6. Falsification and Omission: Yes, in an era of IPCC’s Climate Gate, one needs to ascertain if data are hidden, missing or outliers ignored.
7. Association equals causation fallacy: Correlation does not equate to causation (a common mistake made by marketing and finance executives alike).
8. Proper Application of Statistics: The effective use of statistics by insurance actuaries, scientists, and even casino managers is well-documented. However, real danger results when mathematical concepts are used to denote certainty indecision-making and divining behavior of markets.

Now, please don’t get me wrong. Statistical analysis is very important for many industries (e.g., health care, transportation, and manufacturing). Statistics, however, can give us an illusion of control in a world that’s much more complex than our models suggest. Nassim Taleb, author of the Black Swan likes to remind us that “(real) life isn’t a casino.”

Statistical analysis is definitely a powerful gadget in the business manager’s decision-making toolkit. But one needs to understand the limitations of this science.

After all, Taleb points out that many of today’s statistical models work as though we have “full knowledge of the probability of future outcomes.” And this just isn’t so, especially when it comes to fat tails, or the “ten sigma” event. Indeed, sometimes those rare events have extremely large impacts. Were he alive today, the former captain of the Titanic, E.J. Smith would wholeheartedly agree.

Questions:
• Clive Thompson calls statistics “the language of data.” How important is it for marketers to understand and apply statistical concepts?
• “Lies, damned lies, and statistics” is a phrase popularized by Mark Twain in the context of using statistics to unduly persuade, obfuscate or even swindle. Can statistics get its reputation back? If so, how?

Marketing Lessons Learned: Riding Bubbles at Lehman Brothers

When “the next big thing” is identified—whether it is tulip bulbs, internet technologies, real estate or financial derivatives, market mania is not far behind. And while riding and making a mint from a bubble of “irrational exuberance” is possible, it’s also beneficial to know when to exit the moving train before it explodes. Just ask the former executives of Lehman Brothers.

It’s been said the phrase, “this time is different” is one of the most dangerous sentences in business. That’s because executives keep making the same mistakes again and again say economists Carmen Reinhart and Kenneth Rogoff; “We gullible humans (believe) that the laws of financial physics have been repealed for us.”

Why do humans keep making the same mistakes? Perhaps it’s because over optimism—and resulting speculation—is very much a part of the human psyche. We like to believe those who have previously failed just didn’t have the right information, or that a new paradigm has emerged. And sometimes changes are so fundamental and drastic that they do create new markets. But more often than not, we’ve exchanged our money, time and hope for worthless swamp land.

Now what does any of this have to do with marketing?

An important role for marketing executives is to provide direction to our business leaders regarding trends, white space, and best areas in which to compete or avoid. We do this via a thorough understanding of competitive, social, governmental, and economic forces within a market.

In adding a potential new product or service to our portfolios, we need to ask ourselves, is this market sustainable —or does it depend on unstable factors? How long will this market exist? At what stage of the lifecycle is the market? Does my company have the capabilities to compete? Can my company make a profitable impact?

And this is where diagnosis of a market bubble comes into place.

Now let’s be clear. Not everyone believes in economic market bubbles. Some economists are convinced that people have all the information they need and therefore always make rational decisions. Efficient and rational market theorists from the Chicago School of Business, in particular Eugene Fama, don’t believe in unstable and wild market inflations. “I don’t know what a bubble means,” Fama recently declared to writer John Cassidy.

However, since there’s an abundance of evidence for market euphoria, let’s assume economic bubbles do in fact exist. The next step is identifying whether the market in which you plan to participate is in fact prone to speculative behavior (even mania), and if so, should your company compete or walk away from the opportunity?

These are a few questions that could have been asked by senior management at Lehman Brothers as they jumped headfirst into frenzied markets.

In the book, “A Colossal Failure of Common Sense; the Inside Story of the Collapse of Lehman Brothers,” former Lehman Brothers vice president, Larry McDonald cites how then CEO Dick Fuld and his second in command Joe Gregory made bet after bet, first in derivatives such as collateralized debt obligations (CDOs) and credit default swaps (CDS) and then grandiose real estate purchases.

These purchases—with borrowed money—were made with the following inherent assumptions:

  1. the market would keep rising indefinitely,
  2. there would always be a market for securitized debt, and
  3. what’s profitable for competitors must also be the same for Lehman Brothers.

Sadly, we know how the story ends. McDonald relates, “When a high rolling market goes wrong, history tells us that it happens with lightning speed, as everyone stampedes for the door at the same time.”

Indeed, as the market for derivatives self destructed, Lehman was stuck with a bag full of product than nobody wanted, to the tune of sixty billion dollars. Senior management failed to ask themselves, “how long can this market sustain itself?” or even “what’s our current position and what happens if this bubble pops?”

It seems that it’s quite easy to get caught up in the euphoria of a new market, especially when everyone appears to be making boatloads of money. An ebullient market looks like it will never end.

However, it’s very possible to enter at the very top of the market and not know it, effectively joining the party just as the host removes the punchbowl. And this is where very careful analysis from the marketing function can come into play.

While a frothy market may be pretty easy to identify, it’s difficult to know when it’s going to end. Participating in a market bubble is a risky proposition and timing (getting in and out) is everything. And for those analytical types, even if deep market analysis is performed, it’s possible your timing may be off by just a bit, leaving you short or long. After all, as John Maynard Keynes once said, “The market can stay irrational longer than you can stay solvent.”

One thing is for certain, history repeats, or as others have said, it rhymes. Lehman Brothers stood for 158 years but participation in one of the largest asset bubbles in history brought this noteworthy firm to the steps of bankruptcy court. Lehman rode the bubble and didn’t “get out”. The musical chairs stopped with nary a seat.

It really wasn’t different this time.

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Marketers, do bubbles exist? Is it possible to discern a bubble? How can one discern when to “get out” of a frothy market before it implodes?

Thin Slicing Your Way to Lower Profits

hermes tieA hotel manager looks out in the lobby and notices a guest with a Hermes tie. Another is carrying a Prada handbag. In an instant and through “the power of the glance,” the hotelier decides these folks “look right” and are worth giving special attention. Unfortunately, this hotelier has probably just thin-sliced his or her way to lower profits.

No surprise to anyone, some upscale retailers and hotels are looking for visual cues to determine the service level they should provide to customers. According to a somewhat dated 2007 WSJ article, “The Gatekeeper: How Posh Hotel Sizes Up Guests”, some hotels are sizing up guests based on what car they park in valet, or what they’re wearing when they walk in the door.

In addition to keeping a record of the spending of hotel guests, the staff of the Peninsula Beverly Hills looks for signs of wealth and sophistication in guests. The article notes,

“The hotel’s managing director, Ali Kasikci, is something of an anthropologist of status signals. He is highly aware of the delicate hierarchy of fashion and symbols of influence, and he looks for small details to tell him what a pair of jeans and a T-shirt can’t.”

In the article, Mr. Kasicki spots a Hermes tie and a Charvet shirt among his wealthy guests and says, “It’s like a skunk. There’s enough scent being sprayed around that you can connect the dots.”

And while as of last year, Mr. Kasicki has recently moved on from the Peninsula Hotel to the Montage Beverly Hills, undoubtedly he’s still thin-slicing; segmenting and treating customer’s differently based on his seasoned observations and intuition.

It’s also a dangerous strategy.

Malcolm Gladwell, in his best seller, Blink, defines the concept of thin-slicing as, “the ability of our unconscious to find patterns in situations and behavior based on very narrow slices of experience.” Essentially, it’s the ability to see patterns based on extensive experience in a particular field or discipline. In the case of Mr. Kasicki, his years of hotel experience at the Peninsula and Four Seasons give him visual cues and “distinctive signatures” of which guests can afford his services.

Here’s the problem with intuition however. Solely relying on “at a glance” decision making, or decision making based on gut instinct can be very costly to our business and careers. For Mr. Kasicki to make better decisions on which guests should receive special attention, both observational data (visual cues) and hard numerical data are necessary.

It’s probably challenging in a service business like high-end hoteling, to not consciously or unconsciously segment and then treat customers differently based on how they dress or what they drive. However, even Mr. Kasicki admits that sometimes he gets it wrong when it comes to sizing up his guests. For example, the article notes a poorly dressed retired pharmaceutical executive is one of Mr. Kasicki’s wealthy guests!

It often makes sense to build loyalty programs, marketing campaigns and service/product offers to keep valuable customers spending money with your company. A good segmentation strategy, based on quantitative data, can help a company determine what customers to keep and which ones to let go to the competition.

For example, a data-driven customer profitability and life time value (LTV) analysis could show that while an individual is a frequent guest to a high end hotel, they also tend to bargain for the lowest rates, berate the service staff, tip poorly, steal towels and swipe hotel fixtures.

In an era of fierce competition, taking care of your most profitable and valuable customers has never been more important. Just don’t base your definition of a valuable customer on criteria such as he or she “looks the part.” Even Gladwell admits, “We are often careless with our powers of rapid cognition.”

Can you judge a book by its cover? Providing better levels of service to your top customers is a good strategy, but close your eyes for a moment and let your data speak to you for a comprehensive picture of who is “valuable.”