Author Archives: Christian Bonilla

About Christian Bonilla

Christian Bonilla is a Solutions Architect at Sentrana, responsible for product management and adapting quantitative marketing to different industries. Prior to joining Sentrana, he spent several years working with corporate Chief Financial Officers providing research and best practices on capital structure policy and other corporate finance issues. In college, he interned in a research role for the White House Council of Economic Advisors under then-chairman N. Gregory Mankiw. Christian holds a Bachelor’s degree in Economics and Public Policy from Duke University.

Why Pricing Must Be a Continuous Process (Part 1)

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At some point, every homeowner learns an important lesson about how to save money on air conditioning during the hottest part of the summer. Generally speaking, it costs less to keep your house at a relatively even, tolerable temperature, then to turn off the unit entirely during the day and blast the A/C in the evening when you are home. The process of re-cooling the entire house each time wastes a lot of energy to get to a comfortable temperature again.

Multiple optimal prices can exist for a product, even in transparent markets. Note that all of the prices in this image apply to the exact same HP printer.

Multiple optimal prices can exist for a product, even in transparent markets. Note that all of the prices in this image apply to the exact same HP printer.

The lessons of efficiently cooling a home can be applied to many scenarios. In business, having a system in place for tweaking procedures continuously is easier to manage over time than are prolonged periods of stasis followed by dramatic transformations. Transformations are complicated. They are often expensive. If too much time passes between transformations, the organization’s inertia coefficient (a 100% made-up term) passes a critical threshold. After that point, two outcomes are the most likely, with a few shades of gray in between: (1) transformation projects mushroom from merely “expensive” to “expensive and painful”, or (2) the company is too lethargic to change, effectively dooming the business to eventual defeat or absorption by more innovative rivals. For the sake of comprehensiveness, I have to acknowledge that for a fortunate few, “federal bailout” must now be added to this list as a third possible outcome. However, in a few years we will see if my suspicion that outcome three eventually finds its way back to outcome two turns out to be correct. Continue reading →

Missing the Ocean for the Stream: What We Can and Cannot Learn from IBM’s New Breakthrough

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As part of its perpetual quest to reinvent and perfect its business model, IBM has made an aggressive push into the analytics market in the last half-dozen or so years. The company’s slick, though occasionally confusing ad campaigns (remember those ads with the mysterious red box being unveiled?) often announce its new initiatives, though it is not always clear that a new announcement is indeed a major one. In the analytics space, however, Big Blue does mean business. The announcement of its sizable new business analytics and optimization division is clearly intended to prove as much. Shortly after its announcement, IBM also unveiled a new stream computing platform called “System S” to much fanfare. The breathless enthusiasm of business journalists, technology bloggers and investment analysts has been palpable. But what exactly does this technological advancement do, and what does it mean for your business?

To answer this question, let’s begin briefly by dissecting what IBM has introduced. Imagine that you are receiving a continuous stream of data, such as stock prices on the Nasdaq. These figures must be quickly analyzed so that the proper buy and sell orders can be placed. Suppose that you also need to base your decisions not just on the Nasdaq prices but also the numbers figures coming in from dozens of other exchanges. Continue reading →

Cheating Your Way into Business Visibility

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Several weeks ago, I wrote a post about how the pace at which the world is accumulating information exceeds our ability to critically evaluate it. For companies that make thousands or millions of marketing decisions every day in the form of price offerings, advertising placements and so on, this translates into making decisions that perpetually involve a greater amount of uncertainty relative to the amount of information we have. The root cause of this problem is a technological one: we do not have the computing power to slice and dice massive datasets in order to glean insight in time to support decisions. An even deeper explanation is that the gap between the rate of information accumulation in businesses and the pace of information transfer improvements will continue to widen at an increasing rate. This poses serious challenges to the capabilities offered by Business Intelligence, not to mention our ability to determine optimal prices. Continue reading →

The Micro-Monopoly Phenomenon

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Here’s an interesting market experiment that you can try without leaving your desk. Go to www.pricegrabber.com, choose a merchandise category, and then select a product that has more than a half-dozen or so different sellers. Sort the list by price, and compare the highest price to the lowest. Having just performed this for the HP Laser Jet 1022n laser printer, I see that I have the option to pay as much as $290.00 or as little as $115.00, plus a range of prices in between. That’s a lot of variance for the exact same product. The highest price is almost three times as high as the lowest. Yet all sales have not been captured by the lowest-price seller, nor has the most expensive retailer (which happens to be HP itself) gone out of business. Intuitively, you may already be rationalizing this phenomenon to yourself. People are willing to pay for things like the seller’s brand strength, return policy, warranty, service packages, availability, and so on, which is why different prices are charged. I didn’t bat an eyelash when I saw the price range on the screen, even though it seems to contradict the premise of market-clearing prices in perfectly competitive, transparent markets. We understand the reasons for these differences, but there is a deeper insight to be gleaned from this apparent oddity.

Let’s say hypothetically that this printer has 10 different attributes like the ones mentioned above on which every buyer places a value, even it happens to be zero. There is a segment of the printer-buying population that wants all 10 attributes, including the HP brand name of the seller, and that segment is willing to pay a higher price. No other seller can satisfy all 10 attributes, giving HP a monopoly on that attribute set. But as a seller, HP operates within constraints since other sellers offer the same exact printer at a lower price in return for providing fewer attributes. Thus, HP cannot set its prices as a pure monopolist, because an excessively high price will drive too much of the market to the next lowest price tier. HP’s competitive position is what I call a micro-monopoly (or “Micropoly” if you prefer the conflation, as I do). The explanation for this price dispersion is that every seller of this printer satisfies a unique mix of attributes demanded by a particular segment of the market. For that segment, the seller has a limited amount of micro-monopoly pricing power.

When viewed from this angle, it becomes easy to see why it makes more sense to set prices based on what your customers value rather than what your competitors charge. The reason is that one firm may compete only tangentially with another firm that sells the same products. The obvious question then is what happens when two firms fulfill the same exact mix of attributes. At this point, firms would then compete on price, but I think this logical extension can be somewhat misleading. In the real world, no two firms ever truly occupy the same attribute space. There will always be at least some differences in the total experience and feel that the customer gets from making the purchase, and thus the potential for price differentiation exists. Multiple optimum prices for the same product can exist in the marketplace. A profit maximizing firm’s objective should not be to race to the bottom in a low price battle with competitors, but rather to understand very clearly what its price ceiling is.

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.

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.