Monthly Archives: March 2009

What Happens When We Can’t Keep Up with Information

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I ran into a former colleague the other day who, as it turns out, recently left his job and presently spends his days building options pricing models and trading from home on his own accounts. In turn, I described to him some of the recent work that we have done in revenue optimization and particularly the breakthroughs that we have engineered for processing data. His face scrunched up a bit, and his response was uncharacteristically blunt: “You can always process numbers quickly if you need to,” he smirked.

Not so, in fact. When you start asking extremely detailed questions that require combing through years of detailed historical data and then performing mathematical transformations on each of those figures, you will find out rather quickly the limits of processing speed when your results finish compiling in a week or so. The thing is that most of us never push up against the processing speed frontier. We can see that every year computers get faster, chips get smaller, and Excel seems to have more rows. Moore’s Law prevails. The trouble is that all the while the rate at which the data universe expands is screaming past advances in processing capabilities, and that rate does not fluctuate with the economic downturn. Consider the markets for microprocessors, which allow us to perform those calculations and manipulate data, and hard drives, which allow for storage of data. Microprocessor sales have been dealt a sharp blow by the global downturn as computer sales have slowed, but worldwide shipments of hard disk drives (HDDs) roughly maintained 2007 levels even in the worst quarters of the recession (and the drives themselves contain more memory). Solid state and flash memory shipments were down, but the evidence suggests that this is due to consumers substituting HDDs for other types of memory, rather than simply not storing more information. The demand for data storage, while not completely recession-proof, is nonetheless of the hardier variety.

Simply put, information of all kinds accumulates faster than we can analyze it. We are losing the race, and the gap is widening, not shrinking. As for what this ultimately means, I will now make a rather dour point. A fashionable explanation for the recession among both politicians and many “Main Street” types is that greed is what did us in. The greed of the bankers, the hedge funds, the fat cats, the small cats, whomever – greed is the culprit. But that doesn’t explain everything by a long shot. Even the greediest person doesn’t want the party to end and the money to stop coming in. Might it be possible that they weren’t able to ask the questions that might have led to certain debt instruments having never been created? Financial services employees have more information available to them than decision makers any other industry, and still here we find ourselves. Think about how many times each day similarly misinformed decisions are made inside corporations all across the world. The information is there, but we are more often than not letting it rot on the docks.

Globally, $50 Trillion of Wealth Disappeared in 2008; Will the Long Tail of Consumer Choices Survive?

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The global financial crisis wiped out $50 Trillion of wealth in 2008, and the global economy is likely to shrink in 2009 for the first time since World War II. The cumulative effects have left consumers without any excess household income – some losing their homes or jobs altogether – and therefore less likely to spend on frivolous products or services. As this trend continues through the predicted turnaround starting in 2010, I wonder if we’ll see an equally large contraction in the number of consumer choices that have exploded in the past 10 years?

In October 2004, Chris Anderson coined the term the “Long Tail,” referring to a new economic model where companies sell more of less. This was a direct result of the ubiquity of the Internet (along with increased processing power and cheap online data storage), where an unlimited selection exists for information, products and services 24/7/365. He argued that consumers were no longer confined to a narrow list of choices that emerge from large corporate entities in the form of “blockbuster” hits that are meant to satisfy the masses. Instead, consumers were wandering further from mainstream tastes and discovering that their preferences lie in the form of smaller niche movies, books, music, websites, services, etc. I found the theory intriguing back in 2004, but am now reconsidering it’s viability in the context of the global economic crisis: will the long tail survive?

To answer this, I can simply skim the news headlines to find companies scrambling to trim the fat off their product portfolios. No longer is cutting prices a viable strategy for dealing with declining consumer demand. Companies have turned to the ax to focus marketing dollars on their higher-margin, best-selling brands to help retain consumers, who are trading down in the recession. Auto companies have been hardest hit, where GM’s Hummer, Saturn and Saab brands will likely be lost if a buyer isn’t found. Chrysler management has already stated that the company has too many brands and too many dealers. Ford remains afloat, but for how long? Food companies from Sara Lee Food Corp. to H.J. Heinz Co. are trimming their offerings. In the airline industry, Aloha, ATA, MAXjet, Skybus, and Champion Air grounded their planes. Simply put, the long tail just got a little shorter. OK, a lot shorter. As shrinking payrolls, housing values and credit availability continue to push consumer demand down, I think it’s likely Chris Anderson will annotate the theory of the Long Tail to show its existence is more often a byproduct of exuberance in the markets rather than a permanent trend.

Is Pricing a Core Competency?

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I’m not sure whether or not Tom Peters actually coined the term “core competency”, but it certainly took firm root in the business world following publication of his Ur-management tome In Search of Excellence: Lessons from America’s Best Run Companies back in 1982.  The equity bull market that started that same year may have run its course, but core competencies are still with us.  A core competency is supposed to be a unique configuration of intelligence, skills, experience, processes, systems – the things that enable a company to do something really, really well, that are hard for others to replicate and therefore lead to an enduring competitive advantage.   In the business world “advantage” is achieved through profitability, and profitability is achieved through doing things that lead to higher revenues and lower costs.  And the strongest lever the company – any company – has at its disposal to shape its profit line is price.  Given this rather widely understood fact, would not it be logical to assume that a large number of our best-run companies manage pricing as a core competency? Logical, perhaps – but the evidence seems to indicate otherwise.

For all its impact on the bottom line pricing often seems to be more on the periphery of the activity flow than at the center – an outer rather than a core competency, and sometimes not much more than an afterthought – oh, yeah, we need to stick a price on that now… hmmm, let’s see.   There are three commonly-used methods for firms to price their goods and services, and none of them could be considered the basis for a core competency.   The Old Faithful of pricing methodologies is cost-plus: add up a bunch of direct and indirect costs, slap an arbitrary profit margin on top and voila – that’s the price. Then there’s competitor-based pricing, which many people seem to think is several evolutionary legs up from cost-plus but which Michael Douglas’ Wall Street character Gordon Gekko might have called “a dog with a different set of fleas”.  Why should either bean counters in the accounting department or your competitors be the metronome for what you charge your customers?

The third common pricing method perhaps comes closer to hitting the mark, but it still falls short of a core competency.  In fact it is not really a method per se but more an amalgam of several things – gut instinct, trial and error and maybe some back-of-the-envelope elasticity calculations .  Here the intention is right – try to figure out what motivates a customer to want to pay a certain price and then try to meet it – but the delivery is weak.  Even mid-sized companies in retail or distribution businesses face literally millions upon millions of pricing decisions every day – what product to what customer in what location via what marketing message and selling channel?  The permutations are too staggering to handle in any way other than with technology-aided, systematic rigor.  For a long time the tools did not exist to facilitate this – but as more companies become aware of the tools and best-in-class practices evolve (to borrow another one of those indispensable bons mots from Tom Peters) I expect that we’ll see some migration of the pricing discipline from the periphery to the core.

Wanted: Intelligence (Information Need Not Apply)

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Professionals in the foreign intelligence community take pains to distinguish between information and bona fide intelligence. Any piece of knowledge, no matter how trivial or irrelevant, is information. Intelligence, by contrast, is the subset of information valued for its relevance rather than simply its level of detail. That distinction is often lost in sector of the enterprise technology industry that is somewhat loosely referred to as Business Intelligence, or BI. This has become a bit of a catchall term for many different software applications and platforms that have widely different intended uses. I would argue that many BI tools that aggregate and organize a company’s information, such as transaction history or customer lists, more often provide information than intelligence. The lexicon is what it is, but calling something “intelligence” does not give it any more value. In order to sustainably outperform the competition, a company needs more than a meticulously organized and well-structured view of its history. Decision makers at all levels need a boost when making decisions amidst uncertainty and where many variables are exerting influence. They need what I would call predictive intelligence, or PI – the ability to narrow down the relevant variables for analysis and accurately measure their impact on the probability of a range of outcomes.

What makes the distinction between information and intelligence critical is that information is getting more accessible by the day. This democratization of BI is evidenced nowhere more so than at Microsoft. In 2008, Microsoft unveiled several projects in the late stages of development that it claims will put BI capabilities at the fingertips of more users than ever before. “Project Madison” will massively increase Microsoft’s information storage capabilities, while the “Kilimanjaro” and “Gemini” projects together will provide easy-to-use reporting and analysis tools designed to drastically reduce the complexity of using traditional BI tools – all at very low cost compared to large-scale ERP implementation. The possibilities abound. But I still ask the question: what are all of these newly empowered users going to do with all of this information once they can access it at the push of a button?

I am excited by the idea of so many more information workers being able to ask the questions that end up driving businesses to continuously reinvent and perfect themselves, but I worry about relevance. Will these capabilities actually increase the amount of intelligence available to decision makers? Any business decision can be thought of as a bet that some desired future state will materialize as a result of a present course of action. Business intelligence tools as we know them more often than not do not help us make more intelligent bets when it comes to the future. The problem is that we think they do. More data often makes the task of identifying the true predictors of business success and isolating their effects more difficult. In order for a company to get the most out of its data, it needs PI as well as BI capabilities at the fingertips of decision makers. For marketing and pricing to become a more fact-driven corporate discipline, we must recognize the need not for more data, but ways of evaluating the probability of outcomes based on only the factors that matter. This is not child’s play. Information alone, however well-groomed, is simply not sufficient to meet this need.

The Price You Pay for Not Changing Price

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The WSJ ran a story on 3/10/09 on the financial success of McDonald’s Corp. throughout the present recession. Since the company is one of only two DJIA members (the other being Wal-Mart Stores, Inc.) to have ended 2008 by posting a gain for the year, it is perhaps only fitting that the Journal devote a few inches to McDonald’s. The only student to pass a difficult exam rightly deserves a gold star. But amidst the discussion of McDonald’s zeal for succession planning, controlled expansion and keeping a lid on costs in the face of the last year’s commodity price swings, one item deserves more attention than it received: McDonald’s is encouraging individual locations to experiment with prices.

Restaurants sit at the crossroads of both cost and demand volatility. Much to their detriment, companies such as McDonald’s often buffer both their customers and their upstream suppliers from feeling the financial impact of this volatility. Now McDonald’s is at least hinting that it wants out of this arrangement, and our experiences working with multi-billion dollar partners in the food distribution industry points to this being a wise move. We have long observed significant daily fluctuations in food prices across all categories. Couple this with the effect that a strong dollar can have on McDonald’s overseas business, and it quickly becomes clear that understanding how much a customer is truly willing to pay for a menu item is of huge value for a company so proud of its billions and zillions served.

The real question is why don’t more restaurants (or any number of businesses for that matter) treat their price as the valuable asset that it is? It is not overly difficult for a restaurant to approximate a schedule of demand and create several different menus with prices tailored to different Cost of Goods Sold (COGS) environments. For a restaurant grossing $500,000 in revenues annually, every 1% increase in sales corresponds to a $5,000 improvement to the top line (subtracting the printing costs later). In our experiences in food distribution, a 1-2% increase in the organization’s top line can translate into a bottom line improvement of over 8% – an observation that we have seen replicated in numerous industries. Projecting forward a few years, I would be willing to bet that the majority of companies with the highest valuations among their industry peer groups will also be the ones that are trying to actively shape demand through their pricing strategies.