Chapter 14
Cheap and Cheerful Pricing Tools
Answer:…we put that question to the National Association of Insurance Salespersons Heavily Armed with Graphs.
Question: What was their answer?
Answer: They are surrounding your house right now.
—DAVE BARRY, MONEY SECRETS
Implementing a pricing tool into a company requires more than a smart algorithm. It must link to the market being addressed and to the specific price drivers relevant to the company. In short, some degree of customization is inevitably required for a tool to produce a useful pricing output. The customization requires that the tool be able to:
Match the workflow and expectations of the internal users.
Adapt to the market context.
Frequently, these goals can be accomplished more rapidly with a small stand-alone tool than with an enterprise-wide system. The simple tool won’t do all the same things as the complicated one, but it may well pay for itself thousands of times over.
Some small, relatively narrow-focus tools—running on a spreadsheet, highly customized, provide you with guidance on price level, discounting, value message, or bundle components. Such tools cost from low to mid six digits when you include initial analysis. Another choice is a highly sophisticated program, such as PROS and Vendavo, with refined and automated interfaces, deeper integration into the work flow, and industrial-strength security features. In some cases such tools offer algorithms that would be very difficult to reproduce in-house. They also provide other benefits, such as programs to help management audit and control sales pricing and to do detailed analytics on costs, margins, and discounting. Their breadth of features and seven-digit pricing tend to facilitate user acceptance, but they are relatively expensive.1
The two choices are typically seen as alternatives but need not be. The nice thing about cheap tools is that they are cheap! Cheap tools are disposable. The timeframe for implementation is also a fraction of the timeframe for larger systems. Your company can rapidly build an inexpensive tool and then move to the big system with more understanding and in less of a rush.
Even if less expensive tools are not in your company’s budget, there are even cheaper pricing tools now available on the Web that can serve some pricing purposes quite well—providing either a second opinion, an easy way to simulate what competitors might do, or an opportunity for training. One free site is called pricewitch.com. It uses a sophisticated algorithm to recommend product level pricing and bundle price points.2
Whether stopgap or permanent solutions, such tools can help your company reflect how context and price vary by market and by product. Where there is an appetite for better pricing by product or sales management, a simple spreadsheet-based tool can serve as an invaluable pricing engine. Best of all, they can be customized to the exact price context applicable to your products. Here are six popular and emerging categories of price tools and programs that serve often-unsupported pricing needs:
Product pricing, which helps product management set prices for specific products, taking into account different purchase contexts.
Product value, which turns a general value proposition into specific quantified benefits for a particular customer segment and context.
Discount scorecard, which tells sales, sales management, and product management what should be the right discount for a product given a particular set of circumstances.
Bundling model, which can take the various products and components in your company’s offer set and rigorously calculate a baseline price for the bundle (i.e., the right “bundle discount”).
Demand curve, which links segmentation and price targets.
Patent program, which can make it possible to defend price innovative pricing from imitation.
If any tool fills a void in your company’s pricing needs, it will tend to be a high-return addition to the managerial capability suite.
The high-return price tools are: (1) a discount management tool, (2) a product pricing tool, (3) a product-value calculator for quantifying customer benefit, (4) a bundle-price calculator, (5) a demand curve linked to segmentation, and (6) an improved patent process for protecting intellectual property.
Product Price Tool
A product price tool provides product management with a starting point for offer price and helps facilitate the transition to contextual pricing.
Typically, the first step in building a tool is to investigate what drives price, isolating those drivers and testing them against history. These may pertain to the product, to the buyer or the segment, to the competition, and to potential usage. Frequently half the drivers are contextual in nature, rather than absolute measures of the product. Finally, it’s important to get the results in usable form. Normally, there are no more than four or five drivers of product price.
In the case of a leading marketing information provider, for example, a four-part pricing mechanism captured more than 80 percent of the observed price variations. A disguised, but not simplified, example of such a tool is diagrammed in Figure 14-1:
Figure 14-1 Contextual baseline prices: illustration of how, in one case, contextual baseline prices were calculated.
Notice how only one of the four components referred to the product itself and the other three were all relative to the competition’s product? This simple structure, combined with weighting and coefficients, was much more sophisticated than the judgmental logic used previously for such decisions. The previous pricing practice focused on breadth but did not explicitly address the other factors—even though they were known to matter on some occasions.
The algorithm also made it easy for product management to adapt offers to different circumstances. For instance, for the “usage context” factor, the tool offered users three simple choices, so it was easy for product management to plug in one of three coefficients.
Since the tool quantified the net impact of these different contexts, it also proved it was worthwhile to bother pricing differentially for various contexts. Similarly, a contextual product pricing tool will frequently also quantify the benefits of branding and market communications, and what improvements in these areas would do to support pricing. In this case, the impact of reputation on price could be read directly from the tool. This was a useful input to the budgeting process.
A Product Value Tool
Frequently absent from value propositions is a quantification of product benefits, and the differential benefit compared with the competition’s product. But when it is provided, such a value tool can be powerful. Take, for example, a video promoting Mobil 1 motor oil, which describes in detail the benefits of lower wear and reduced deposits, and offers to share with automobile-fleet owners a financial estimate of what this means in terms of better reliability and engine life for their cars.3 Quantification of benefits has worked well to preserve the price of this superior-performing lube, especially in challenging price environments (contexts). Life insurance is another industry that takes showing value seriously. Hence, life insurance agents often have charts and models to show the consequences of purchase or nonpurchase of their policies.4
Yet many products are sold with no effort to actually quantify their benefits. Services, in particular, tend to be sold without adequate quantitative proof of differential value, when the facts may be much more cogent. The purchase decision consequences of a lack of support is described by a financial decision maker at the leading online automobile financing company: “I was asked to evaluate the two offers, and everyone agreed that [one vendor] had the better reputation, and was the class outfit. But while they said they were more reliable, they could not give me any numbers. So when it came to presenting a recommendation to management I had no choice but to choose [the lower priced vendor].”5
This should never happen! Every company must be able to construct an argument or a tool to give to the back-office staff, who can then rely on it if they choose. With no tool or argument in place, they would have to work very hard to justify a decision in your company’s favor—and if they don’t choose to do this, you may lose.
The heart of this sort of problem usually lies in either complexity or difficult analysis. Both can be addressed with a template or a tool-based calculation.
Comprehensive Services
For complex or comprehensive services, a spreadsheet template listing all the aspects of the service, and their value to the customer, will play a major role in achieving price goals.
The spreadsheet should include an estimate of the benefit for each element or attribute of the service. They may not be easy to estimate, but typically it’s worth more of the seller’s time to develop those estimates than of the buyer’s time. Don’t forget context here: choose circumstances that favor your product. If the potential buyer does not agree, they may modify your estimates or assumptions, but at least the burden of doing so is on them—lazy buyers can be your best friends.
For instance, a leading beverage company has a 12 single-spaced page document that lists product and service elements, and benefits offered to its largest customers. Such a comprehensive in-one-place list is already above-average practice, but when the company added a benefits calculator on a spreadsheet (which quantifies benefits proportioned to soda outlets and other scaling factors), the spreadsheet provided a powerful baseline for negotiation.
Difficult Analysis
When outcomes from the purchase are hard to predict and the differential between your offer and the competition’s offer (or nonuse) has not been quantified, there is a big payoff for developing a tool to convert those benefits into specific dollars.
Frequently, the purpose of customer tours and education is both to convince customers of capabilities and show that people should take the need for the services seriously. An example is SunGard Availability Services, which gives tours of its impressive disaster backup facilities. The tours do convince potential customers that in the event of disaster, SunGard is fully capable of rapidly doing all that is possible to keep call centers or computer support in operation. It’s a great sales procedure, but this company and its competitors do not supplement that impression by specifically quantifying the chances of such failure and what the differentials among different supplier choices mean in total dollars of impact. This is why often the driver of purchase here is regulation and legal pressures. However, that is a luxury that is not always available to sellers in other industries.
With statistical examination of history or modeling, or both, any difference can be modeled. Perhaps not to an academic level of confidence, but the modeling will certainly work better than guesswork and customer skepticism. For instance, extrapolating from infrequent events by using mathematical processes such as discriminate function analysis or Black-Scholes option pricing (not part of every buyer’s evaluative toolchests) can often provide a number when simple math cannot. And sometimes any number is better than no number when it comes to supporting prices. “In the land of the blind, a single number can be king,” to paraphrase an old expression.6 This is particularly valuable when selling high-uncertainty services (e.g., insurance, backup systems, new hires, new distribution methods, warranties, etc.).
All these approaches help cement your value. With the addition of context, each tool can be made powerfully applicable to a segment or customer. While this requires effort, the rewards can be substantial and repeatable. Note that there are usually multiple approaches for estimating benefits, and the first vendor to educate buyers in a market on the best approach to estimate or calculate benefi ts has an advantage. In some cases, you may even be able to patent that approach.
Additionally, the incidence of an event and its magnitude (failure patterns) should suggest different business models: Severe consequences occurring infrequently suggest that you include “insurance” with your offer. Severe consequences with high frequency generally means risk-sharing with the customer. Minor consequences in low frequency suggest a warranty, and minor consequences with high frequency suggest tiering of service and repair.7
The Discount Scorecard
Customer buying behavior can be incorporated into a relatively simple model. The model must calculate risk and reflect shifts in context, and examine what this means for pricing.
The logic can be expressed as a simple “if-then” table. The “if” asks questions about the account, and the “then” says what must happen to the offer. The “then” is usually about what level of discount is appropriate and necessary, but it may also include carve-outs. For instance, if a deep discount is required because it appears that the buyer is looking exclusively at the price tag, it would be appropriate to limit the warranty or spare-parts inventory, or eliminate free shipping.
The if-then table, otherwise known as the discount scorecard, may reflect up to a half-dozen factors that have an impact on discounting and risk of customer loss. Sound complicated? Not really: a good discount scorecard asks four to six questions of the sales rep (or other decision maker), and those should relate directly to the salesperson’s market experience. The rest of the calculation happens “behind the curtain.”
Whereas the product-pricing tool addresses factors that affect the product as a whole across all target customers, the discount scorecard addresses differences and factors that apply specifically at the account level. For instance, a product-pricing tool will consider overall product usage levels but cannot distinguish between accounts that makes purchase decisions though a committee and those that have a single decision maker.
The technique for building a discount tool has three steps. First: identify potential price drivers through sales force and customer interviews. Second: assign weights “points” to each price driver (factor) that has been identified, through statistical analysis. Third: test the weights so that when applied retroactively to history, the model predicts outcomes. This requires some iteration, but that is useful also because it makes the company more comfortable with changing the point allocations as the market evolves. Testing against history also gives you a “predictivity” number—how well your company would have identified past losses due to pricing—and it also allows an estimate of revenues lost for lack of the tool.
Rollout of the tool requires some thought. It will be helpful if the sales force is familiar with the logic behind it, and is looking for ways to improve pricing. That suggests you might tie the use of the scorecard to compensation. Too often account teams believe they know all there is needed to know about pricing and resent the imposition of pricing guidance. This is why pricing organizations are often treated as enemies of sales. Yet we find that this tool can outperform the sales force on questions such as whether an account is likely to defect.8
A Bundle Modeling Tool
For product development teams looking to create a suite of bundles and adapt them for use in the battle with competitors, a model-based tool has proven useful. This model needs a full underpinning of bundle pricing logic; however, once it is set up, it is simple to use.
In a nutshell, the goal here is to identify the core of the bundle and develop the correlations to the core. A spreadsheet model simply multiplies the stand-alone values of the components by the correlation to get the expected in-bundle value. Then the spreadsheet just adds up the core value plus the in-bundle component values. Simple enough?
However, if you have different segments with different cores (e.g., the video-centric core subscribers, and the telephony-centric subscribers) and the list of potential bundle components is long, a spreadsheet is the best way to evaluate a long menu of options rapidly. Plus, it ensures that managers who are afraid of correlation have some comfort.
While the model itself is simple and very cheap, the results can be material. Almost all bundles proposed by management will contain some low-correlation or negative-correlation (substitutes, not complements) bundle candidates. This model makes the consequences of that plain. Furthermore, if you have run through the full list of potential bundle elements, it allows managers to test potential bundles rapidly—and so save precious market research dollars for the most desirable tests.
A Demand Curve
When a demand curve is matched with a supply curve, the result predicts both the price and quantity of goods sold in a competitive market—two of the most important questions any company manager might ask on behalf of his company. Yet market analysis based on supply-and-demand curves is relatively infrequent.
Price and quantity sold. Basic question, right? Some of the most famous instances of marketing success have stemmed from the use of supply-and-demand (S&D) analysis. Recently McDonald’s appears to have used supply-and-demand analysis in moving to offer a suite of less expensive meals. This was a best-practice adaption to a change in the demand curve due to lower customer income. Other examples of successful S&D use come from electricity markets,9 and the use of demand curves by computer and cell phone manufacturers10—that combined this analysis with clever buying strategies where suppliers bid below cost to participate in eventual higher volumes.
Companies spend millions on consumer research, market scans, and cost accounting—functional bases for supply-and-demand analysis—but rarely are these combined together into a demand curve for the market. It is not because the concept is novel. Supply and demand is the oldest tool in pricing and demand forecast: the interaction was noted by Adam Smith in The Wealth of Nations in 1776 and described by David Ricardo in Principles of Political Economy and Taxation in 1817.
The Missing Strategy Tool
While supply and demand is not context itself, it certainly summarizes important contextual information about contextual pricing forces, i.e., relative abundance of buyers and sellers. However, many companies have never performed such analysis or even think it is possible. Some managers say obtaining any information outside the company is difficult, and putting them together for an integrated supply-and-demand view is not something they can do in their spare time.
In some cases these managers rely on elasticity curves for promotional and incremental pricing. Elasticity shows the short-term response of customers to price changes in highly communicative, near-commodity markets. Elasticity is good for tactical pricing (e.g., optimizing inventory turns). Some pricing systems augment their elasticity analysis with inference engines that are helpful in separating out prices for individual products in multiple-product selling situations (e.g., advertising bundles and retail). That is a good use for computers, but it does not produce demand curves.
Why bother with supply-and-demand curves when software systems conveniently offer elasticity-based analysis? If all you ever want is tactical advice focused on promotions and inventory turns, there’s no reason to bother. But if you are a senior manager asking how to grow revenues, enter new markets, and beat competition, you want a demand curve. Demand curves answer the big questions. Why don’t senior managers focus on demand curves more often? Perhaps the old adage that “Fish are unaware of the water in which they live” explains this lack of focus. Every company is subject to supply-and-demand curves, and demand is every manager’s concern. Every time someone says the word market, they really mean supply and demand—so to care about customers and markets is to care about your demand curve. Maybe it’s time to make that focus explicit.
Do not confuse elasticity figures with demand curves. Elasticity is deceptive in many ways, and the demand curve is much more strategic.
New-Product Development Tool
Two universally applicable examples of what a demand curve can do for your company relate to product development and competitive analysis. Starting with the former, product development often follows a process of relying on customer research to determine the point where most survey respondents indicate a willingness to buy. This simple yes-no inquiry leaves a lot to be desired. Typically it leaves money on the table, and does poorly at assessing how new products will change consumer price perceptions. This is why so many products are launched, only to be greeted with indifference, disappointing demand, or surprisingly overwhelming demand—or anything but the predicted demand.
With a demand curve, product developers can aim at multiple price points along the demand curve and clearly understand the likely uptake. Doing so beats building the product and then making guesses about how much to up-tier or down-tier the product to meet missed segments. A very typical example of a demand curve was in a mature equipment market where an elite segment was willing to pay more than 1,000 euros per year, and a huge, untapped, segment which was willing to buy if prices were pushed to less than 70 euros per year. This curve makes it clear that all segments cannot be addressed by one product targeted at the middle ground of 400 euros per year. Even more important, this curve prompted management to ask whether the low-priced segment might shift upward if enticed by introductory pricing and greater familiarity with the benefits.11
Demand Curves Address Sweeping Market Changes
Supply-and-demand curves often show critical interactions. Changes in supply curves seem to provoke changes in demand curves, and vice versa. For instance, when offered a new credible product or service at a new low price level, many buyers will take the time to learn about the value of such a product and buy despite no previous interest. This shift in demand is accelerated once there have been some sales and the concept has been proven. Examples include electronic navigation. As prices of GPS mapping devices have fallen, many consumers who formerly contented themselves with paper maps have now bought a GPS. In 2007, GPS volumes grew to 180 million, with an average selling price of $189 per unit. Such volumes—growing at 237 percent a year—would have been unthinkable in 2004, when the average price of a GPS was over $400. Elasticity analysis could not begin to suggest such results: short-term responses to price promotions for a $400 GPS would have fallen well short of today’s market volume.
Competitive Pricing Strategy
A second, equally important use of the demand curve, is harnessing its utility in thinking through strategic responses to competition. Here’s an example from the legal-publishing market. A premium publication called Moore’s Federal Practice was under attack by a lower-priced publication called Wright & Miller Federal Practice and Procedure. A typical management response might have been to cut the price for Moore’s, although this would either have run the risk of disappointing subscribers if it were accompanied by cutting content, or run the risk of alienating subscribers by attempting price discrimination across too large a gap. Instead, examination of the demand curve suggested to management that it ought to launch a lower-priced offer at a price where it attacked Wright & Miller from below. This version could be trimmed sufficiently so that there was little risk for defections from the principal Moore’s subscriber base.
This strategy proved a marketplace success. The new publication, Moore’s Abridged, attained the volume predicted by the demand curve (not immediately, but over time), and kept Wright & Miller from moving upmarket to attack the principal Moore’s version.
Do-It-Yourself Building of Demand Curves
How does one construct a demand curve? The key task is to identify proxies for market sensitivity. Proxies could include a good segmentation,12 or existing product tiers or different competitors that have staked out distinguishable market tiers. Begin with a chart showing prices and volumes as they are today. As an illustration: if there are 50 major brands of perfume, ranging from over $1,000 to $15 per ounce, and you can make rough estimates of volume, then a demand curve can be estimated based on this information. We would start with the highest priced perfume volume on the left, and then adding on additional, successively lower priced brands moving right. The fiftieth and least expensive perfume brand (or group of brands) should push the line to a market total. This will result in a very detailed “as is” demand curve from which management can apply judgment and market insights to estimate the maximum (“reserve”) prices potentially obtainable from each part of the curve.
Lifecycles
We find that demand curves change as industries mature. When a market is young and growing, the demand curve tends to be convex: many segments pay a high price as customers embrace a new product. We suspect this comes from lack of comparison points for sellers, and because in young markets there are often relatively fewer sellers. Visually, the demand line is pretty flat until it falls abruptly, as you get to segments who have not yet understood the need for the product. An example is coffeehouses, where the entry of a Starbucks into a neighborhood actually raised the demand for coffee and benefited existing coffeehouses—that, in addition, could charge higher prices. Frequently, new products galvanize markets.
As industries mature, the demand curve becomes concave. Alternatives have eaten into the middle section of the curve. For instance, as the idea of premium-priced coffee and “cool” surroundings matured, Starbucks trimmed its number of locations; Starbucks and its alternatives found themselves in an increasingly less complementary relationship. For demand curves, a mnemonic is to think of people’s faces: round when younger, gaunt when older.13
Knowing how your industry will evolve can be very useful. If you have, as we suggest, linked segments to different parts of the demand curve, you know which segments will likely show increasing price resistance—the middle of the curve. That will tell you which segment you need to begin to tier offers and launch new product architectures that can gracefully accommodate increasing price pressures. Given limited resources, better to be proactive in the right spot. For instance, in many markets we find it’s the segments with the highest reserve prices early in the lifecycle that remain the most price-indifferent customers.
Product Portfolios
Finally, demand curves are useful for defensive strategy. Estée Lauder is a good example of a smart incumbent with highly strategic responses to actual and potential competition. Lauder regularly launches or purchases brands that combat newer, less expensive entrants. To reinforce this focus, Lauder splits management of high-end cosmetics and those aimed at discount (e.g., Target) shoppers. This has resulted in effective management of traditional Lauder brands, such as Youth Dew, and launches of new products, such as Flirt, aimed at younger buyers. Lauder has used an understanding of demand curves to build a formidable thicket of brands with nonoverlapping demographics and price points.14
When it comes to demand curves, the real mystery is why they are not more widely employed. Decision makers within both companies and government have been exposed to the power of this tool in school, yet fail to ask for robust answers on price and quantity. Perhaps managers should begin to consciously request a direct analysis of supply, demand, and price. Top managers and policy makers should be made aware if they are missing fundamental documentation of demand curves and not be distracted by secondary tools such as elasticities.
A Patent Program
Suppose your pricing dreams come true? You have developed a price structure or a tool that stymies competition, delights customers, and raises margins—what would you do then?
Despite ignorant advice that nothing can be defended via patent, and equally ignorant claims that everything can be defended via patent, there is a good record of patents helping to fend off or delay imitators in pricing. One of the most famous examples is Priceline.com, a popular website where a reverse auction begins with a buyer bid, for example, on a hotel room or an airplane flight. Priceline patented this approach, sued an early imitator, and won. This helped preserve its distinctive (and, implicitly, contextual) pricing approach.
The defense of price structures, and indeed all aspects of products, is likely to become much more patent-intensive. Evidence of this is that former Microsoft CTO Nathan Myhrvold raised $5 billion to amass several thousand patents and is now beginning the process of suing companies that infringe on them.15 Many CEOs don’t like this change in the business rules, but ignoring this trend can be dangerous. Acquiring patents and other intellectual property plays a defensive role, as well as an offensive one. For instance, a leading software firm was sued for infringing patents but obtained a no-cost settlement when it emerged that it had some patents that would form the basis for a counterclaim. Without those offsetting patents as a “tradeable,” the results would have been much more expensive.
That’s why you might need a patent program. In the same way that product teams today make sure that tax and other legal requirements are fulfilled, they should also have ready patent-identification and filing systems. The ease of use of such a system is key because within a short period after commercialization or being made public, a patent is not an option.
The patent program has two elements: One is a process element, which defines how patent opportunities are identified and selected, how managers are rewarded for good patents, how the application should be promptly written, the use of “patent pending,” and how granted patents use should be pursued. The second element is more substantive: the patents are written initially for rapid approval, aggressively pursued, and even augmented through the purchase of licenses.16
What is contextual about pricing patents? Everything: by making your pricing patent specific to particular markets, segments, and situations, you are likely to arrive at a stronger patent and one which is more rapidly approved. The objective here is not to have patent applications gathering dust at the patent office (which many do) but to develop the portfolio of rights and then deploy them in your company’s market. If they happen to have further uses you can always make some money by licensing them, as IBM now does, earning almost $1 billion per year from its intellectual properties.
Summary
Sometimes a small investment is better than none. Often a small investment can make a big difference. For instance, the White Star Line economized by not equipping the lookouts on the Titanic with binoculars. Who knows if doing so would have avoided the collision with an iceberg; at least in retrospect it seems they overlooked a worthwhile modest investment.
In the same way, rather than make product management rely on simple intuition in complex markets to set prices, a tool can pay its way. Your sales channels today may guess at the risk of customer defection or make up puffy hyperbole as to economic benefits (instead of providing cold hard facts), but some relatively inexpensive tools may produce much better revenue results. For market managers, a demand-curve roadmap of the market may offer important strategic insights.
Often, the obstacle to these basic investments in infrastructure is not a few hundred thousand dollars; rather, it’s the fact that a pricing process which was long the province of intuition and experience is resistant to change.
Notes
1. Important to pricing, but not the focus of this book, is the role of incentives in instilling discipline. It can work wonders in getting sales to accept a new tool. See the excellent article by Marc Hodac: “Pay for Performance: Besting Best Practices,” ChiefExecutive.net, 2011.
2. There are many pricing engines on the Web oriented toward buyers; these compare prices, but few help product and sales management determine the best selling price. One Web site that helps sellers establish a price is www.pricewitch.com, which is free and fun to play with. The site can take your inputs and provide a suggested price target or a “second opinion” on internally developed prices. It also offers comments on price structure.
3. Mobill Las Vegas taxi field test video, at www.Mobil1.com. It has a video of people discussing oil and engine wear, if that topic interests you.
4. Interestingly, these same insurance companies and health care insurers are relatively price-tool poor in the B2B marketing and sales efforts. While they have admirable statistics on when a group’s policies will experience decaying economics because of adverse selection, they have not built tools that say at which point, and under what context, the company purchasing the policy is at risk to defect to another carrier because of price.
5. Senior director, www.carsdirect.com.
6. As adapted by O. Scott Rogers and Pat Clark, authorities on oil and gas law at Vinson & Elkins. Statistical wisdom is by Nassim Nicholas Taleb, The Black Swan, Ibid., pp. 88–92 and 138–139.
7. More on the structure implication of consequence and frequency can be found in Winning the Profit Game, Ibid., pp, 129–134. See also B. Shapiro, “What the Hell Is Market Oriented?” in J. Sviokla and B. Shapiro, Keeping Customers, Harvard Business Review Books, Cambridge, Mass., 1993.
8. For instance, one company which commissioned construction of a scorecard also had reps rate the “risk of loss” of different accounts. Midlevel sales management was not pleased with the study and was outraged when the draft model suggested that pharmaceutical client Novartis was in acute danger. Right before the meeting in which sales was going to protest, saying that the model was flawed, Novartis cancelled.
9. See A. Faruqui, et al., Pricing in Competitive Electricity Markets, Kluwer Academic Publishers, Boston, 2000.
10. Examples include Dell and Motorola. See “Winning the Profit Game. Smarter Pricing, Smarter Branding,” Rob Docters et. al. McGraw-Hill, 2004, Chapter 5.
11. Sources: ABI Research and NPD Group, December 2007 “Black Friday” Report.
12. Unfortunately, some segmentations do not distinguish among different price sensitivities, as they are geared to channel differences or product categories that can blend different price sensitivities. However, most segmentations do reflect price differences as this is primary requirement of segmentation. See R. Frank, W. Massey and Y. Wind, Economic Principles of Market Segmentation, Prentice-Hall, 1972.
13. See “At Starbucks, Too Many, Too Quick,” The Wall Street Journal, November 15, 2007, p. B1. Only five years earlier, the story was quite different. See “Despite the Jitters, Most Coffee Houses Survive Starbucks,” The Wall Street Journal, September 29, 2002, p. 1.
14. “Estée Lauder’s Dynasty: the Sweet Smell of Succession,” D. Roth, danielroth.net, September 19, 2005. See also Winning the Profit Game, Ibid., pp. 218–219.
15. “New Salvo in Tech Patent Wars,” The Wall Street Journal, December 9, 2010, p. B1. Another benefit of making patenting a program is that it makes taking out a patent cheaper and faster.
16. Taken from a March 16, 2011, address by Mark Nowotarski, president of Markets, Patent and Alliances, LLC, and patent agent.