EXHIBIT 2-1 Economic Value Estimation (EVE®)Model
The Guiding Force of Pricing Strategy
Customers will not pay literally a penny more than the true value of the product.
Ron Johnson1
An estimate of value is to pricing what an estimate of location is to navigation. Just as it is necessary, when lacking a GPS, to measure angles of the moon, the sun, and stars relative to the horizon to determine one’s location and the direction of a desired destination, it is necessary to understand what benefits drive value for customers in order to triangulate the way to a cost-effective value proposition. Understanding the values that different combinations of features and services could create for customers, and how the best combination might differ for different segments of customers, is essential for creating offers that will enable a company to grow profitably. This is true even when the goal is not to maximize value capture but to rely instead on low “value pricing” to attract customers. One must still understand which sources of value one can deliver cost effectively, and which are better left unsatisfied because cost would exceed value. Any value proposition that is both compelling and profitable is based on offering a better perceived value, regardless of whether it is priced high or low relative to the competition.
Of course, customers’ perceptions of value can change as they get new information or consider a purchase within a different context. Value is not determined by the unchanging physical aspects of a product but by the ever-changing circumstances of the customer, just as the location of celestial points of navigation, although unchanging in the heavens, appear in different places relative to the boat at different times of the day or when sailing in different directions. When they do not appear as expected, a navigator needs to convince the pilot to change direction. Creating a profitable marketing strategy likewise requires understanding not only the value created, but also what causes customers’ perceptions of that value to be aligned or misaligned with reality. Marketing can influence customers brand choices not only by changing the actual value offered relative to price, but also by changing the context from which customers perceive the value created and the fairness of the price.
Each element of the Value Cascade introduced in Chapter 1 plays a critical role in managing some aspect of the relationship between value and price. Strategic pricing involves creating differentiated offers that represent “good value” to customers, raising willingness-to-pay via value communication, creating price structures that reflect the differences in value to different users, and adopting pricing policies that lead customers to make purchase choices based upon value. In short, strategic pricing is largely about managing the relationship between value and price. In effect, what we call strategic pricing could as accurately be labeled strategic value management, since strategic pricing involves much more than optimizing prices.
At the time of this writing, Apple has enjoyed more than a decade of exceptional profitability and growth, not only because it created a stream of differentiated smartphones and priced them higher than competitors’ phones, but also because of decisions to manage both perception and delivery of value. Apple’s advertising to its target segments to communicate why its phones are worth a premium price, and its high level of customer support, especially in its dedicated Apple Stores®, is designed to give customers confidence that they too can achieve the benefits that the phone’s features promise to deliver. Apple avoids short-term promotional discounts that would lead people to wait for a deal before buying. And Apple offers different models designed to reflect value and ability to pay across different segments of customers. Apple’s financial success is not driven purely by a drive to create more and better technology, but by a sophisticated effort to manage perception and realization of the value that its technology can create for its customers.
In contrast, many firms fail to capture their fair share of value delivered because they do not understand how their products and services create benefits for their customers. They erroneously assume that merely adding features or improving performance will lead to profitable gains in price, volume, or both. But more and better features will not lead to greater profitability unless those features translate into more value, actual and perceived, for customers. To do that, a pricing strategist must first learn techniques for understanding and estimating value.
Among the most important techniques for pricing are those that help managers determine the value of their products and services. Marketers have long admonished companies to set prices that reflect value. Unfortunately, because value is often poorly defined, value-based pricing is sometimes rejected as impractical. Consequently, let’s begin by explaining what we mean by value when we refer to it as the guiding force that keeps the various elements of pricing aligned and moving in the same direction.
In common usage, the term value refers to the total savings or satisfaction that the customer receives from the product. Economists refer to this as use value or the utility gained from the product. On a hot summer day at the beach, the use value of something cold to drink is extremely high for many people— perhaps as high as $10 for your favorite frosty beverage. That information is of little help, however, to a vendor trying to price cold drinks for thirsty sun worshippers, since few potential customers would be willing to pay such a price.
Why not? Because potential customers know that, except in rare situations, they don’t have to pay a seller all that a product is really worth to them. They know that there are likely competing sellers who will give them a better deal, leaving them with extra money in their pockets, or what economists call a “consumer surplus.” Perhaps customers believe from prior experience that, if they wait long enough another vendor will come along selling cold beverages at prices closer to what they have come to expect from their past experience. Or they may know that about half a mile up the beach there is a snack shop where frosty beverages cost only $2. As a last resort, they could drive to a convenience store where they could buy an entire six-pack for only $6. Consequently, thirsty sun worshippers will usually reject an unusually high price as “outrageous,” even though the product is worth so much more to them than that price.
So what do marketers mean when they propose pricing to reflect value? The value that is key to developing effective pricing strategy is not use value, but is what economists call exchange value and we call economic value. This value is determined first and foremost by what the customers’ alternatives are. Few people will pay $5 for a cola, even when they value it at $10, if they think the market offers alternatives at substantially lower prices.
Still, only a small segment of customers insists on always buying the lowest priced alternative. It is quite likely that many people would buy a cola at the beach for $5, despite the availability of the same product for less at a snack shop or convenience store. By offering the product at the beach the vendor is pricing a differentiated product offering worth more than the available alternatives to some segments of the market. How much more depends on the value customers place on not having to drive to the convenience store. For some, that value is very high. They are willing to pay a lot for the convenience of not having to exert themselves. For more athletic types who wouldn’t mind an excuse to jog to the store, the premium for convenience may be much less. To ensure the patronage of that segment, the beach vendor would be wise to differentiate his or her offering in some other way that this segment values highly.
Economic value comes in two forms: Monetary and psychological. Both of these are instrumental in shaping a customer’s willingness-to-pay but require very different approaches to estimate them.
Monetary value represents the total cost savings or income enhancements that a customer accrues as a result of purchasing a product. Monetary value is the most important element for most business-to-business purchases. When a manufacturer buys high-speed switching equipment for its production line from ABB, a global electrical equipment manufacturer, it gets products with superior reliability that minimize power disruptions. For many of ABB’s customers, the benefit of fewer power disruptions has high monetary value because it translates into tangible cost savings associated with avoiding plant shutdowns.
Psychological value refers to the many ways that a product creates innate satisfaction for the customer. A Rolex watch may not create any tangible monetary benefits for most customers, but a certain segment of watch wearers derives deep psychological benefit from the prestige and beauty associated with ownership to which they will ascribe some economic worth. As the Rolex example illustrates, consumer products often create more psychological than monetary value because they focus on creating satisfaction and pleasure. However, some consumer products, such as a hybrid car, create both types of value, and it can be challenging to discern which is more important to the purchase decision. A consumer in the market for a new car might focus on the monetary value derived from the potential fuel savings by switching to a hybrid. Other customers will be motivated more by the psychological value derived from knowing that the hybrid is less damaging to the environment. Still others will gain satisfaction from the status associated with driving a trendy car. Regardless of the source of value, many buyers of hybrid cars are willing to pay a price premium over similar conventionally powered cars.
More formally, a product’s total economic value is calculated as the price of the customer’s best alternative (the reference value) plus the worth of whatever differentiates the offering from the alternative (the differentiation value). Differentiation value may have both positive and negative elements. We call this the Economic Value Estimation (EVE®) Model as illustrated in Exhibit 2-1. Total economic value is the maximum price that a smart shopper, fully informed about the market and seeking the best value, would pay. Not every buyer is a smart shopper, however. Often product and service users, and particularly purchasing agents buying on the users’ behalf, may not recognize the actual economic value they receive from an offering. That is, the offering’s perceived value to a buyer may fall short of the economic value if the buyer is uninformed. Therefore, it’s critical that a company’s sales presentations and marketing communications ensure that features likely to be important to the buyer—particularly competitively superior features—come to the buyer’s attention. The need to communicate value is why the Toyota website contains easy-to-use calculators comparing fuel and emissions savings of the Prius hybrid car relative to other brands.2
EXHIBIT 2-1 Economic Value Estimation (EVE®)Model
One of the most critical factors driving customer choice and willingness-to-pay is the set of alternative products under consideration for purchase. From the marketer’s perspective, these products represent the next-best competitive alternatives or NBCA. Given the centrality of competitors’ pricing in the purchase decision, economic value estimation begins by determining the price the competitor charges (not necessarily the NBCA’s use value), which becomes the reference value in our model. For example, the reference value of a hotel room on a business trip is the price charged for the next-best hotel choice in town given the minimum lodging service level the traveler will accept. In the case of a new iPhone®, the reference value would be the price of the comparable Android or other 4G phone under consideration.
In some cases, the reference product or service is not necessarily a specific competitive offering, but a self-designed solution that buyers might use to achieve their objectives. For years, most accounting software suppliers assumed that buyers would compare their wares to traditional double-entry bookkeeping methods. Software vendors designed products to automate double-entry accounting and its rigorous debit and credit data entry requirements. Intuit, however, learned that double-entry methods were the wrong reference process for the two-thirds of small-business bookkeepers who used their own simpler cash-based accounting solutions. Working closely with those customers to understand their need for simplicity, Intuit created QuickBooks, which outsold competitors in the small-business market because it automated those simpler approaches.3
Differentiation value is the net benefits that your product or service delivers to customers over and above those provided by the competitive reference product. Our soft drink vendor strolling right up to the customer’s beach blanket provides convenience compared to a distant refreshment stand. The traveler’s hotel of choice provides a free breakfast and free cocktail hour not available at the next-best hotel. Competing products in a category likely provide many sources of differentiation value. It’s important that an effective value estimation concentrate on those value sources having the most differentiation “bang for the buck” for a customer or customer market segment. Whereas a free breakfast may not be an important value driver for a business executive on an expense account, it could be a crucial factor for a traveler booking a hotel for a family vacation. The degree to which a supplier differentiates its offer in terms of those needs will have the greatest impact on the price the marketer can successfully charge above the reference value.
Marketers have historically invested considerable effort to develop effective value propositions to represent their company and products. And few would argue that an effective value proposition, a concise statement of customer benefits, is an essential input to brand building and sales conversations. But a general statement of value is insufficient input for pricing decisions because it lacks the detail and quantification needed to shape strategy. In this section, we describe techniques that can be used to develop quantified estimates of customer value that, in turn, can be used to help set more profitable prices. We start with a discussion of how to collect and analyze competitive reference prices. Then we describe two approaches for quantifying monetary and psychological value and illustrate them with detailed examples.
Conceptually, identifying the next-best competitive alternative is simply the answer to, “ If I, the seller, did not exist, what would my customer do?” Yet the work of identifying the next-best competitive alternative and gathering accurate reference prices offers a number of challenges that often trip up pricing strategists.
In some cases, marketers may have a myopic view of their markets. One of the authors recalls the first consulting project he served on where the client, a manufacturer of uniforms, thought they had an 85 percent market share. However, rudimentary market research quickly revealed that customers considered competitive offerings not just from other uniform manufacturers, but anyone who sold clothing such as department stores and discounters. Consequently, when viewed against this larger backdrop of potential uniform vendors, we found that the client only had a 35 percent share of the market and they were shocked to learn that they were missing many more sales opportunities than they had originally thought.
In other cases, there may not be just one competing product; instead, customers might construct a basket of products and services as a viable alternative. The “triple play” offered by communications companies such as Comcast and Verizon gives price allowances to consumers who choose one vendor for phone, internet connection and cable television service. Satellite TV companies can’t offer this same bundle because of technical and regulatory limitations. Determining the reference price for these customers requires some analysis to estimate an aggregate price for a comparable basket of goods.
Finally, in some cases the biggest source of competition may simply be customer apathy or doing nothing. For a seller of additives that allows concrete to cure more effectively in cold conditions, for example, the biggest source of competition might not be an alternate additive manufacturer. Rather, the biggest competitive challenge may simply be getting the builder to realize that he needs to use an additive in cold weather applications.
Once the competitive alternative has been identified, the next challenge is to establish the competitive reference price, which requires gathering accurate price data and ensuring that it is comparable to the pricing for your product. You must ensure that competitive prices are measured in terms familiar to customers in the segment (for example, price per pound, price per hour) and are stated in the same units as your product. In some product markets such as CPG automotive and apparel, competitive prices as well as discount and incentive data are readily available through data services such as IRI, Urban Science, or Nielsen. In addition, there are many price comparison tools available today from vendors like Google Shopping, Nextag, Price Grabber, among others, that allow users to quickly scan for competitive price points. In other categories, however, competitive prices are more difficult to obtain because of industry-wide practices of unpublished prices or because prices are negotiated individually with customers. In these instances, marketers must be creative in finding secondary sources of information by using techniques like polling the sales organization or interviewing customers. Secondary price data of this sort will invariably contain some bias and be less reliable than primary data obtained directly at that point of sale. Generally, though, it is possible to take imperfect competitive price data and treat it so that it becomes useful to a value estimation exercise.
Customers are a valuable information resource. One of the more effective ways to get competitive price information is to take advantage of the customers who call your customer service support line asking whether you are willing to match a competitive price. It is very easy for the customer service representative to ask a few simple questions—what is the competitive brand or product and its price—and record that information. While the data will not be entirely accurate due to gamesmanship by customers, over time enough data accumulates to offer a perspective on competitive price positions. Alternatively, favored customers can be good sources of competitive price information. Those who are loyal to the company, perhaps because of its quality or good service, do not want their competitors to get lower prices from anyone else. Consequently they will warn the company when a competitor issues new price sheets or when they hear that someone is discounting.
EXHIBIT 2-2 Non-Normalized Reference Price Data
Exhibits 2-2 and 2-3 provide an illustration of how secondary price data can be normalized for use in a value estimation. In this example, a technology manufacturer in North America collected data as part of a competitive strategy assessment. When the data in Exhibit 2-2 was examined for use in a value estimation exercise, it seemed there was little coherence to how competitors were setting prices. After seeing the raw data, one of the product managers noted that his suspicions were confirmed: Competitors were completely irrational in their pricing! Closer examination, however, revealed that much of the variation was due not to irrational pricing, but to differences in volume and service levels. After the pricing data was normalized for these factors, Exhibit 2-3 revealed much more consistent pricing behaviors that could be used as an input to the value estimation.
EXHIBIT 2-3 Normalized Reference Data
As this example illustrates, collecting reference prices is often more than just a data collection exercise. It requires some judgment and analysis to ensure that the data is ready to be incorporated into a value estimation calculation.
After determining the competitive reference prices, the next step in value estimation is to gain a detailed understanding of customer value drivers and translate that understanding into quantified estimates that can be used to support pricing decisions. The distinct characteristics of monetary and psychological value drivers require different approaches to quantify. As we noted earlier, monetary value drivers are tied to the customer’s financial outcomes via tangible cost reductions or revenue increases. Since monetary value drivers are already quantitative, monetary value can be estimated using qualitative research techniques that allow for a rich understanding of the customer’s business model or personal finances. In contrast, the intangible nature of psychological value drivers such as satisfaction, security, or brand value are not inherently quantifiable. Therefore, marketers often rely on sophisticated quantitative techniques such as conjoint analysis to quantify the worth of the various elements of a product offering. (See Chapter 8 for a complete discussion of price experiments and other pricing research techniques.)
The first step in quantifying monetary value drivers is to understand how the product category affects the customer’s costs and revenues. In consumer markets, this is a relatively straightforward exercise because end consumers usually have fewer monetary value drivers for a given product category. A hybrid car, for example, provides monetary benefits such as lower fuel and maintenance costs, as well as psychological benefits, such as conveying a concern for the environment, yet these do not affect customer finances. Typical of most end consumer monetary value drivers, fuel and maintenance costs can be quantified using readily available data; Toyota’s online calculator (referenced earlier in this chapter) is a tool to help consumers estimate the financial benefits of purchasing one of their cars. Quantifying monetary value drivers in business markets is more challenging because of the complexity of most business operations and the need to understand fully how a product affects a customer’s profitability. This complexity is why we start with a detailed assessment of the customer’s business model to understand how our product contributes to the business customer’s ability to create value for its own customers and to reduce its operating costs.
To illustrate this point, consider the example of Distributor Co., a technology distributor selling in a two-tier distribution system. Distributor Co. buys technology products such as servers, software and network components and resells them downstream to value-added resellers (hence the two-tier nature of the channel). The management team believed that all customers valued its technical service and support highly—a belief supported by high service usage across all segments. But an examination of its customers’ business models revealed that this was not the case.
One large segment of customers operated under a “systems integrator” business model that involved sourcing components from Distributor Co. and then installing and maintaining those components as an integrated system in their customer’s businesses. For these customers, high-quality technical support was essential to enable them to ensure proper installation and maintenance. In contrast, another segment operated with a “box-pusher” business model in which they might buy the exact same components purchased by a systems integrator, box them up, and resell them as a packaged solution for the customer to install. For the box-pushers, technical support was not essential to their business success because they relied on low prices, minimal inventory costs, and quick turnaround to make their business successful. Interestingly, the box-pushers consumed significant amounts of technical service even though it was not integral to their business model because Distributor Co. included it for free as part of its customer value proposition. When Distributor Co. started charging for technical support, usage by the box-pushers dropped dramatically because of the low monetary value in their business model. This pricing move improved profits in two ways: It reduced cost to serve for the box-pushers that didn’t value technical support and increased margins earned from the systems integrators, for whom technical support was integral to their business model.
Exhibit 2-4 provides an example of value driver algorithms created for an equipment manufacturer. One note about these algorithms: They do not need to be complicated. In most cases, the algorithm will consist of simple formulas that approximately describe how value is created.
Once the mechanisms for value creation are understood in terms of the customer’s business model, the next step is to collect specific data to develop quantified estimates. In-depth customer interviews are the best source of information. Very different from survey or even focus group methods, in-depth interviews probe the underlying economics of the customer’s business model and your product’s prospective role in it. The goal is to develop value driver algorithms, the formulas and calculations that estimate the differentiated monetary worth of each unit of product performance.
In-depth interviews require a different skill set than many qualitative research methods. Rather than striving for statistical precision, validity, and reliability, the price researcher seeks approximations about complex customer processes that might defy accurate, to-the-decimal-point calculations. It’s critical, as a wise adage goes, to accept being approximately right lest you be precisely wrong in disregarding an important driver of value that seems too difficult to quantify. Therefore, the in-depth interview provides a foundation for developing value algorithms and collecting some initial data points to turn those algorithms into quantified estimates of customers’ monetary value drivers.
It is also important to remember that not all points of differentiation are positive. There may be instances where a competitor does some things better—perhaps they are able to deliver more quickly or have a better warranty. These are important differentiators, the value of which should be calculated in the same manner as described above. When netting out the total value delivered, it is important to subtract the worth of these sources of negative differentiation.
EXHIBIT 2-4 Examples of Value Driver Algorithms for Equipment Manufacturer
Once the differential value algorithms have been determined, the final step is to sum the reference value and the differentiation value to determine the total monetary value. There are several guidelines for estimating monetary value that will enable you to simplify the process and avoid common errors. First, consider only the value of the difference between your product and the next-best competitive alternative (NBCA) product. The value of any benefits that are the same as those delivered by the NBCA is already determined by competition and incorporated into the reference value. You can charge no more for it than the price of the NBCA product, regardless of its use value to the customer. Second, measure the differentiation value either as costs saved to achieve a particular level of benefit or as extra benefits achieved for an identical cost. Don’t add both; that’s double counting. Finally, do not assume that the percentage increase in value is simply proportional to the percentage increase in the effectiveness of your product. Although your part might last twice as long as a competitor, it does not follow that your value is only twice as large. An essential part, for which the competitor charges only $10, might save tens or hundreds of thousands of dollars if it requires shutting down a customer’s production line half as frequently to replace it. Would you charge only $15 (a 50 percent premium) for such performance? Of course not!
GenetiCorp (a fictitious company) creates innovative products that accelerate the process of genetic testing. Monetary value estimation determines the financial impact that these breakthroughs actually deliver to different types of institutional customers.
One GenetiCorp product, Dyna-Test, synthesizes a complementary DNA strand from an existing DNA sample significantly reducing DNA molecule degradation and enhancing the precision of a DNA analysis. Dyna-Test preserves sample integrity much longer than does its primary competitor, EnSyn, thus improving DNA test yields and accuracy in a variety of applications. For example, criminal investigators use DNA to match hair, blood, or other human samples. Hospitals and medical professionals use DNA to diagnose diseases. Pharmaceutical manufacturers use DNA analyses to target genes susceptible to new drug treatments. In all applications, test failures can be costly. For criminal investigators, getting a “fuzzy picture” in a criminal investigation may produce a false-negative result, requiring a retest that might take several weeks. Retests for investigators are problematic because tissue sample sizes in criminal cases are very limited, often precluding repeated tests. Similarly, for a pharmaceutical company, getting a fuzzy picture when analyzing a DNA strand may cause drug researchers to miss their true target, the genetic portion of the DNA suspected of triggering a disease.
Unfortunately, when it first marketed Dyna-Test, GenetiCorp did not have a clue about its product’s monetary value. It set prices based on a high markup over costs and then discounted those prices under pressure from purchasing organizations that could buy large volumes. To improve its profits, GenetiCorp decided to learn what its product is really worth to customers: Dyna-Test’s reference value (the price of what the customer considers the best alternative product) plus its positive and negative differentiated values (the customer use value of the attributes that distinguish Dyna-Test from the next best alternative). Buyers will pay no more than the reference value for features and benefits that are the same as the competing products. When multiple competitors offer customers the same benefits, those benefits are table stakes; a customer need not pay anything close to a product’s worth because it can get the product elsewhere. A product earns a price premium over the reference value only for the extra performance—the differentiated value—it alone delivers. The sum total of reference and differentiated values is the monetary value estimate.
Dyna-Test has more than one monetary value driver because different types of users have different reference alternatives and receive different use value from Dyna-Test’s distinguishing features. Let’s examine the value estimation components in two different market segments, commercial researchers and non-industrial markets.
Commercial researchers in pharmaceutical and biotech firms most often consider EnSyn the best alternative to Dyna-Test. EnSyn sells for $30 per test kit; that’s the product category’s reference value for such users. To determine Dyna-Test’s differentiation value, GenetiCorp studied the five primary drivers of Dyna-Test’s positive differentiation value among commercial researchers.
VALUE DRIVER 1—YIELD OPPORTUNITY COSTS Dyna-Test provides a greater yield of full-length cDNA, the compound DNA structures used for analysis, which is extremely valuable. With more full-length cDNA to work with, drug researchers can reduce the number of experiments needed to find the relevant portions of DNA, saving an average of a week’s valuable research time, according to GenetiCorp’s customer interviews.
GenetiCorp studied its pharmaceutical industry customers’ business models and found the annual revenue from a successful commercial drug ranges from $250 million to $1 billion. GenetiCorp used a conservative estimate of $400 million in revenue for one drug, which, with a 75 percent contribution margin, generated $300 million in annual profit contribution. The cost of developing a typical drug was approximately $590 million. These contribution and cost estimates yield an average net present value of $41 million a year profit for a successful drug over a 17-year patent life. But it takes 500 target tests on average to finally identify the gene sequence leading to a successful new drug, so each target test eventually is worth $82,000. With a 260-day work-year (approximately 2,100 hours), the value of a target test is $39 per hour. If using Dyna-Test saves the researcher an additional week that can be devoted to another new drug, the value of those additional 40 hours is $1,560.
VALUE DRIVER 2—YIELD LABOR SAVINGS Dyna-Test’s cDNA yield superiority over EnSyn also produces more efficient laboratory staff work. Customer interviews indicated that using Dyna-Test saved 16 hours of processing labor compared to using EnSyn. Because laboratory personnel receive an average of $24 per hour, labor savings from Dyna-Test are about $384.
VALUE DRIVER 3—QUALITY CONTROL LABOR SAVINGS Prior to Dyna-Test, researchers frequently checked test-chemical batches for quality, sterility, and reproducibility, adding two hours to a test. However, Dyna-Test maintained uniform quality and performance over several years, assuring researchers that they could eliminate these quality-control checks. In interviews, customers said “I am confident with Dyna-Test because it is a quality and tested product” or “Dyna-Test has been around long enough; you know it works. If someone says they ran the experiment with Dyna-Test it must be right.” High quality produced two hours of customer cost savings, totaling $48.
VALUE DRIVER 4—SAMPLE SIZE OPPORTUNITY COSTS Using traditional methods, analyzing a DNA sample usually requires using some “starter” sample material at the outset. Often, the amount of original sample material is very small; gathering more on an emergency basis might take about three weeks of lost research time. But the Dyna-Test kit has a two-step system that reduces the need for starter samples, making available more testable original sample material and freeing researchers from the search for more. Using the value per week of Dyna-Test usage, GenetiCorp estimated the opportunity cost of searching for new material at $4,680 (3 × $1,560) per project. But because such emergency searches happened only about 10 percent of the time, the likely opportunity cost averages to $468.
VALUE DRIVER 5—SAMPLE SIZE LABOR SAVINGS Similar to Driver 4, gathering additional emergency starter material requires researchers to repeat the entire analytical test—an extra 16 hours of research labor time—about 10 percent of the time. But with the Dyna-Test kit yielding more usable material and with labor costing $24 per hour, the value of using Dyna-Test on this dimension is $38 ($24 × 16 × 0.1).
EXHIBIT 2-5 Monetary Value Estimation for Dyna-Test Industrial Buye
In sum, for pharmaceutical and commercial biotechnology firms, the estimated total economic value of Dyna-Test is calculated by adding together the reference value of $30, plus the estimates of differentiated value associated with each value driver, yielding a total estimated economic value of $2,528. In other words, purchasing the new Dyna-Test kit instead of the EnSyn kit would produce $2,528 in cost reductions and new product profit gains for a commercial researcher. Exhibit 2-5 illustrates the monetary value estimation for that industrial buying segment.
Non-industrial markets such as academic institutions and government laboratories estimate economic value in a similar fashion. Their reference value is also the $30 price of the EnSyn test kit; however, the most price sensitive among them simply have lab assistants—essentially free student labor— who make DNA test products from scratch. Their differentiating value drivers are similar to those of industrial customers, but modified to reflect the business model in this market, which has a different research environment and economic reward structure.
Value Driver 1—Yield Opportunity Costs: The yield opportunity cost avoided by using Dyna-Test is $1,055, somewhat less than for commercial researchers because of the lower economic rewards from breakthroughs in primary research.
Value Drivers 2, 3, 4, and 5: The yield labor savings of $231, quality control savings of $29, sample size opportunity cost avoided of $317, and sample size labor savings are also less because of the reduced cost of labor within university systems.
Thus, the estimated total economic value of Dyna-Test for academic laboratories is calculated by adding the reference value of $30 plus the estimates for each value driver, yielding a total monetary value estimate of $1,685.
Exhibit 2-6 illustrates the relationships.
EXHIBIT 2-6 Monetary Value Estimation for Dyna-Test Academic and Government Buyers
Remember that the economic value derived from monetary value estimation is not necessarily the perceived value that a buyer might actually place on the product. A customer might not know about a reference product and won’t be influenced by its price. A buyer might be unsure of a product’s differentiating attributes and may be unwilling to invest the time and expense to learn about them. If the product’s price is small, the buyer may make an impulse purchase without really thinking about its economic value. Similarly, brand image and equally unquantifiable factors can influence price sensitivity, reducing the impact of economic value on the purchase decision, as in the case of Rolex watches. Ultimately, a product’s market value is determined not only by the product’s economic value, but also by the accuracy with which buyers perceive that value and by the importance they place on getting the most for their money.
EXHIBIT 2-7 Monetary Value Profile for Dyna-Test
This limitation of monetary value estimation is both a weakness and a strength. It is a weakness because economic value does not indicate the appropriate price to charge. It only estimates the maximum price a segment of buyers would pay if they fully recognized the product’s value to them and were motivated to purchase. It is a strength, however, in that it indicates whether a poorly selling product is overpriced relative to its true value or is under-promoted and unappreciated by the market. The only solution to the overpricing problem is to cut price. A better solution to the perception problem often is maintaining or even increasing price while aggressively educating the market. That is what GenetiCorp did with Dyna-Test. After previously cutting price to meet the demands of its apparently price-sensitive buyers, GenetiCorp raised prices two- to fivefold, at the same time launching an aggressive marketing campaign. While customer purchasing agents expressed dismay, sales continued growing because even the new prices represented but a small fraction of the value delivered. Profits increased significantly in the following year as purchasers learned about Dyna-Test’s superior economic value to their institutions and accepted, sometimes grudgingly, the need to pay for that value.
GenetiCorp’s experience also shows how value can vary among market segments. To determine a pricing strategy and policy for a product, you must determine the economic value delivered to all segments and the market size of each. With that information, you can develop an economic value profile of the entire market and determine which segments you can serve most profitably at which prices. Exhibit 2-7 profiles the economic value and market potential for each Dyna-Tech market segment.
Monetary value estimation is an especially effective sales tool when buyers facing extreme cost pressures are very price sensitive. For example, since health-care reimbursement systems began giving hospitals and doctors financial incentives to practice cost-effective medicine, pharmaceutical companies have been forced to add cost and performance evidence to their traditional claims about a drug’s clinical effectiveness.4
Psychological value drivers such as satisfaction and security, by virtue of their subjective nature, do not lend themselves to estimation via qualitative research techniques like in-depth interviewing. Instead, pricing researchers must rely on a variety of quantitative techniques to estimate the worth of a product’s differentiated features. The most widely used of these techniques is conjoint analysis—a technique developed in the late 1970s and early 1980s that can discern the hidden values that customers place on product features. The basic approach is to decompose a product into groups of features and then provide customers with a series of choices among various feature sets to understand which they prefer. In recent years, marketing researchers have extended the basic conjoint techniques so that virtually any type of consumer choice can be tested including choices involving different brands, budget constraints, and even purchasing environments.
Using conjoint analysis makes it possible to estimate the value of different feature sets in driving willingness-to-pay and, ultimately, the purchase decision. For example, a flat screen TV can be described in terms of attributes such as size of screen, number of pixels, and brightness. In a conjoint study, each of these attributes is divided into levels that can be tested. For instance, screen size might be broken into 36 inches, 42 inches, and 52 inches, as a means to estimate the relative value placed on greater screen size. Similarly, conjoint is a common approach to estimating brand value because it enables brand to be treated as any other attribute. Treating brand as another attribute in the choice decision allows us to understand how customers might value a 36-inch Sony TV relative to a 42-inch Samsung model. Regardless of the attributes tested, the value estimates derived from a conjoint study can then be used as an input to a variety of pricing decisions.
Sport Co. (a fictitious company), a leading sporting goods manufacturer, has developed a revolutionary golf club named the “Big Drive.” The new design has led to significant increases in distance and accuracy for both beginning and advanced players. The question facing the management team was how to set prices given that there were many different types of golfers who would be potential customers. Beginning players found the club appealing because it was much more forgiving of poorly hit balls compared to traditional clubs. However, the management team believed that beginners would be relatively price sensitive and unwilling to pay a premium price for the technology.
More advanced players concerned about improving performance found the added distance of the Big Drive very appealing, and qualitative research indicated they would be willing to pay a substantial premium for the club. Knowing that there were multiple segments with different value drivers and willingness-to-pay created a quandary for Sport Co. management—how should they set prices to maximize profits?
The approach involved several steps. The first was to identify the different segments that might be interested in the new club and profile them based on actionable descriptors. This segmentation work uncovered four unique segments, as follows:
Having identified the key segments, the next step was to identify the attributes of the club that each segment found appealing so that they could be tested in the conjoint study. Of the extensive list of attributes that were tested, three were noted most commonly by all segments: Distance, straightness, and consistency. These attributes were then tested along with some other features of the offering such as warranty in a conjoint survey of 670 golfers.
The results of the conjoint study provided the needed inputs to develop a segmented pricing strategy. For example, the study provided actionable data on consumer willingness-to-pay for various attributes such as a warranty, as shown in Exhibit 2-8. The initial hypothesis was that a warranty was not a key driver in the purchase decision; potential purchasers were more focused on the performance attributes of the club. The data revealed that the initial hypothesis was not correct, because consumers across all segments were willing to pay a premium for a one-year warranty. Interestingly, extending the warranty from one to two years did not lead to a similar increase in willingness-to-pay.
EXHIBIT 2-8 Impact of Warranty Length on Willingness-to-Pay
The results also provided key insights into the value derived by different market segments that, in turn, informed the channel pricing strategy. The data in Exhibit 2-9 shows the differences in willingness-to-pay between the Innovator and Budget Shopper segments based on the conjoint results. The profit-maximizing price for the innovators was $425, which would lead to approximately 40,000 unit sales. As expected, the optimal price point for budget shoppers was considerably less at $275. This difference in the value (and hence willingness-to-pay) created a dilemma for Sport Co.: If they set the optimal price for the innovator segment, they would lose many of the budget consumers, who represent nearly 30 percent of the market. This challenge of setting prices when value differs widely across segments is a common one that we will address in detail in Chapter 3. In this instance, the quantified value estimates of the different segments combined with a detailed understanding of segment buying patterns and value drivers enabled the team to make a solid business case for a two-tier pricing strategy. With some minor modifications to the club design, aesthetics, and brand, Sport Co. was able to introduce a lower-performance model aimed at budget shoppers and sold through discount retailers. At the same time, they introduced the premium model aimed at Innovators and Value Buyers to be sold at a higher price in pro shops and high-end sporting goods outlets.
It was possible to generate reliable estimates of psychological value for the “Big Drive” because the key benefits of distance and accuracy are ones with which golfers have prior experience. They know what it feels like to hit the best ball off the tee in their foursome and can imagine what they might pay for that feeling.
EXHIBIT 2-9 Difference in Willingness-to-Pay and Market Potential by Segment
Where conjoint and other similar survey research techniques can fall short is when the differentiating benefits are innovative. The research subject in that case must guess what the benefits are and how valuable they might be. Most people, even those deeply familiar with the technology, are not good at inferring the benefits of innovation. In 1977, the founder and CEO of the world’s second-largest computer company at the time asserted publicly, “There is no reason anyone would ever want a computer in their home.”5 But Steve Jobs did imagine the benefits and set prices for the Apple computer that sparked the growth of the home computer industry.
As the GenetiCorp and Sport Co. examples illustrate, the approach and data used to estimate monetary and psychological values differ substantially, with each having some advantages over the other. While both approaches yield quantified value estimates that are essential to effective pricing strategy, the qualitative approach used for monetary value enables the price-setter to make an explicit linkage between a product’s differentiated features, the benefits those features create for customers and the value associated with each benefit. The importance of this feature–benefit–value linkage will become clear in later chapters where we discuss bundling and value communication choices. Quantitative approaches such as conjoint analysis are appealing because they enable the pricing researcher to perform a wide variety of statistical analyses that can be readily used to test different offering designs and competitive scenarios. In each case, however, they provide the pricing manager with a solid fact base from which to make more profitable pricing choices.
An additional note about building EVE® models: In some cases, the focal product may have net negative differentiation versus the next-best competing alternative. For example, consider the case of a manufacturer that is beginning to supply a generic equivalent of a food sweetener that is just coming off of patent protection. While the generic is equivalent on a molecular level, the branded product still has some significant advantages. It is produced locally which means that lead times are much faster and thus customers do not need to maintain as much buffer stock; it has an established brand name that enables customers to earn a small premium from consumers who recognize and value the brand; and the branded product has a better consistency that allows it to mix more easily in a production process. Relative to the branded incumbent, the generic has no positive differentiating attributes, only negative differentiators. The EVE® model works just the same as illustrated previously, except in this case the model will indicate the minimum discount needed to entice a buyer of the branded product to consider the generic alternative. The useful thing about the model is that in many cases, sellers, in the hopes of quickly overcoming a sales hurdle, will often discount their prices too much; the EVE® model provides useful guidance on how much of a discount is actually needed.
In addition, the EVE® model will often highlight deficiencies that are sometimes better addressed using non-price means. In the example of the generic supplier of a food sweetener mentioned above, long lead times due to foreign production may be more efficiently countered by stockpiling product in a local warehouse to enable response times that are equivalent to the domestic incumbent supplier.
When setting prices, there are no shortcuts for understanding the economic value received by the customer. Many companies, nonetheless, shortchange themselves by assuming that if their differentiated product is “x” percent more effective than the competition, then the product will be worth only “x” percent more in price. While that relationship makes sense superficially, closer examination reveals how wrong it is. If you had cancer and knew of a drug that was 50 percent more effective than the competition’s in curing your disease, would you refuse to pay more than a 50 percent higher price? Of course not. Suppose you were planning to paint your house and discovered a paint sprayer that lets you finish the job in half the usual time—a doubling of your productivity. Would you pay no more than twice the price of a brush? Obviously not, unless you’re some rare individual who can paint twice as fast with two brushes simultaneously. Otherwise, the value to a busy person of the painting time saved by the sprayer is much greater than the price of a second brush.
As these examples show, the value-based price premium one can charge is often much greater than the percentage increase in an offering’s technical efficiency. The total economic value of a differentiated product is proportional to its technical efficiency only when a buyer can receive the benefits associated with a superior product simply by buying more of the reference product. In our example, that would be the case only if using 50 percent more of the competitive cancer drug or painting with two brushes at the same time would produce the same increase in efficiency as using the superior products. Because of this misunderstanding, many companies committed to value-based pricing have been misled into believing that they cannot price to capture their value if their ratio of price-to-use value would exceed that of their competitors.
The Problem with Customer Value Modeling
At the center of marketers’ misconceptions about pricing and value is the popular concept of customer value modeling (CVM), which emerged from the total quality management movement when companies tried to measure and deliver superior quality at a competitive price. Marketers and a variety of value consultants have applied CVM in many contexts, including early criteria for the Malcolm Baldrige National Quality Award, largely because it is easy to implement. CVM relies on customers’ subjective judgments about price and product attribute performances. It assumes that customers seek to purchase the products that give them the greatest perceived benefit—which might be quantified in monetary terms, but need not be—per unit price. Avoiding the translation of relative attribute performance into hard-dollar estimates, CVM is analytically simpler than economic value estimation, particularly for pricing consumer products with their heavily psychological values.
The fact is, however, that CVM underestimates the value of the more differentiated products in a market and overestimates the value of the less differentiated products. CVM methods define value differently than economic value estimation. CVM rates each competitive supplier’s relative strength on each product attribute, weighing each attribute by customer estimates of importance, according to customer and prospect surveys. Then CVM calculates the average relationship between perceived quality and price, creating what is variously called a “fair-value line,” a “value equivalence line,” “indifference line,” or other term for the presumed linear relationship between price and perceived quality. A point on the line putatively indicates a “fair” balance of price for quality. For a given price, a product with less than fair perceived quality is disadvantaged and stands to lose market share, say CVM theorists, while a product offering more than fair quality will gain share. There are flaws in this thinking. First, customers don’t pay for average differential benefit estimates; they pay for the worth of the benefits they receive. That is, they mentally convert benefits into monetary terms so that they can judge how much more they should pay for the extra value received from a more expensive product. If it’s worth more than the price premium charged, they buy it.
Second, CVM fails to distinguish between the value of common benefits that are priced as commodities and the value of the unique benefits associated with a differentiated offering. Total economic value—what the customer really gets from the offering in monetary and psychological terms—does not have a single linear relationship to price. One of the two components of economic value, the reference value, usually is much less than the use value of the benefits delivered by the reference product. The reference value is the price a customer pays for the next-best alternative offering—like the price of the second paintbrush, the price of the EnSyn DNA test kit, or the price of a soda at the refreshment stand. Benefits offered by more than one supplier become commodity benefits; customers can get them from more than one source. Competition among suppliers drives the price for those benefits below their use value, making the price-to-use value ratio of the reference product lower than one-to-one.
In contrast, differentiation value, the second component of total economic value in the EVE® model, is the extra use value a product delivers compared to the reference product. The differentiation value, expressed in monetary terms, is equivalent to the price premium the differentiated supplier could charge as a fair price. It’s fair because the customer gets just what she’s paying for in additional value, no more and no less. The price premium-to-differentiation value ratio is one-to-one. In other words, the relationship between price and economic value is a function of two different price-to-quality ratios, not the single average ratio hypothesized in a CVM model.
This difference is significant because the larger the proportion of differentiation value in a product’s total economic value delivered, the more the truly fair price to the customer—the economic value estimation price—can exceed the CVM-hypothesized “fair-value line” price.6 Pricing your highly differentiated product at the supposed “fair-value line” level will be hazardous to your bottom line!
Market segmentation is one of the most important tasks in marketing. Identifying and describing market subgroups in a way that guides marketing and sales decision-making makes the marketing and pricing process much more efficient and effective. For example, customers who are relatively price insensitive, costly to serve, and poorly served by competitors can be charged more than customers who are price sensitive, less costly to serve, and are served well by competitors. At many companies, however, segmentation strategy focuses on customer attributes that are not useful for pricing decisions, creating customer groupings that do not adequately describe differences in purchase motivations among customers and prospects, or classify them in a way that is meaningful for making pricing decisions.
Consultants and market researchers who offer segmentation-modeling methodologies abound. Often those plans are driven by statistical differences in personal demographics or company firmographics (customer size, standard industrial classification, and so forth). While the results seem clear and sometimes coincidentally differentiate buying motivations, those segmentations seldom assist pricing decisions, especially for setting different prices that maximize profit from different segments. More useful are value-based segmentation models that facilitate pricing commensurate with actual value perceived and delivered to customers. Only then can a marketer ensure that each different customer subgroup is paying the most profitable price that the marketer can charge. Charging the entire market a single price risks undercharging some segments, causing foregone profit to you, and overcharging others, costing you additional foregone profit since those customers buy from other suppliers.
Significant differences between value-based segmentation and other methods are especially critical for pricing. First, most segmentation criteria correlate poorly with different buyers’ motivations to pay higher or lower prices. Both plumbers and personal-injury lawyers consider online advertising to be very important, for example. They advertise to attract customers who have an immediate, unexpected, and high-value need. Google could charge both groups the same advertising rates, but the lawyer can afford to pay more than the plumber because of the greater value of each legal client. Simply raising ad prices across the board would eventually price plumbers out of the market and into less expensive media leading to lower profits. But Google has developed an ingenious bidding mechanism that allows customers to pay whatever price reflects the value to them. The trade-off, of course, is that the lower you bid the less prominent your ad or webpage will be displayed. By enabling the customer to make price and value trade-offs via the bidding mechanism, Google has successfully aligned prices with value and improved the profitability of their advertising business.
Second, even needs-based segmentations give priority only to those differences that are important to the customer. They miss the other half of the story, those customer needs that have the greatest operational impact on the seller’s costs to serve those needs. The seller’s costs and constraints are also important to pricing decisions, as we will see below, because our goal is not just sales and market share, but profitability. Finally, the customer in-depth interviews required for value-based segmentations also uncover why customers find certain product benefits appealing—or would find them appealing were they sufficiently informed. Such knowledge reveals opportunities to develop new products and services and can reveal flawed strategies based on less comprehensive research.
That is a lesson International Harvester Company (IH), in a classic example, learned the hard way. For years, IH classified farmers according to surveys of farmer “benefit perceptions,” particularly IH’s equipment reliability compared to that of arch rival John Deere. Farmers consistently rated Deere equipment as more reliable, so IH invested heavily to ensure that an IH tractor could not possibly break down more frequently than a Deere tractor. Still, Deere kept leading the reliability rankings by a wide margin. IH marketers understood the true situation only when they conducted in-depth interviews. Asking farmers about repair problems revealed that what was important to farmers was the downtime caused by breakdowns. IH customers viewed a breakdown as a big deal to be avoided because of the days of lost productivity waiting for repairs. Deere customers viewed Deere’s equivalent reliability as much less of a problem because Deere’s extensive, service-oriented dealer network stocked spare parts and offered loaner tractors, getting a farmer working again in less than a day. IH’s benefit segmentation had missed the mark. A value segmentation would have revealed that Deere served a different segment of farmers—those driven by the value of a total-service solution, which perfectly fit Deere’s strengths.
To conduct a value-based segmentation, we recommend a six-step process.
The goal of any market segmentation is to divide a market into subgroups whose members have common criteria that differentiate their buying behaviors. A simple example illustrates the concept. A business marketer of, say, an industrial grinding machine could segment customers in terms of their industries, their applications for which they use the marketer’s product, or the total value they receive from the product. A segmentation done by industry using industrial classification criteria would not indicate, however, whether customers use the grinders in similar ways. A segmentation based on application criteria would account for different ways of using the grinders, but would not indicate if the grinder is more important to one segment’s business model than to another’s. Only a segmentation based on the value delivered by the grinder would reveal, for instance, that customers in one segment consider grinder use a small part of assembly line costs, while in another segment the grinder delivers much more value by performing a finishing step that allows the grinder buyer to earn a price premium from its customers. In our tractor marketing example, had IH chosen rapid service needs as its segmentation criterion, it would have seen that it could not match Deere’s field service capabilities. Had IH done its homework, it would have realized that it needed to try and outweigh its service shortcomings with other offering attributes—which would be tough with farmers for whom downtime is very costly—or concentrate on other segments that put relatively more emphasis on attributes where IH excels.
Choosing appropriate segmentation criteria starts with a descriptive profile of the total market to identify obvious segments and differences among them. In consumer markets, basic demographics of age, gender, and income provide obvious discriminators. Enterprise firmographics such as revenue, industry, and number of employees clearly separate firms into nominally homogenous groups. Inputs for this basic analysis can include existing segmentation studies, industry databases, government statistics, and other secondary sources. Outputs include buying patterns, customer descriptions, a preliminary set of current customer needs, and a provisional list of unmet customer needs. You should be able to design first-pass segmentation maps based on those outputs. Along the way, check if those preliminary maps look sensible to salespeople and sales managers. Though your eventual pricing strategy will rely on value-based segmentation, communications and sales strategies are likely to be heavily dependent on those obvious customer characteristics on which media choices and sales territory assignments are based.
Having preliminary segmentations in hand, you identify those value drivers— the purchase motivators—that vary the most among segments but which have more or less homogenous levels within segments. This allows you to zero in on what’s most important to each customer segment. The GenetiCorp example earlier in this chapter determined that segments classified by obvious firmographics—commercial and non-industrial research institutions— also differed on several cost-reduction and profit-enhancement value drivers. Never assume for pricing purposes that preliminary segmentations based on obvious criteria will coincidentally yield effective discrimination on value criteria. Commercial and non-industrial medical laboratories probably have similar needs, for instance, and derive similar value from an undifferentiated product such as laboratory glassware.
In-depth interviews probing how and why buyers choose among competitive suppliers provide the additional input required. Industry experts, distributors, and salespeople can provide supplemental information for double checking the value perception patterns revealed by the interviews. The outputs of this step include a number of useful building blocks for value-based market segmentation, including a list of value drivers ranked by their ability to discriminate among customers (statistical cluster analysis of quantitative data is a useful tool here), an explanation of why each driver adds value, and whether customers in each segment recognize that value. The list should also include the value the customer will receive if your product or service offering satisfies unmet needs.
In this step, you examine where you have operational advantages. Which value drivers can you deliver more efficiently and at lower cost than others? Also, which drivers are constrained by your resources and operations? Experience, capital spending plans, personnel capabilities, and overall company strategy are among the inputs to this step. Use the discipline of activity-based costing (a fascinating diagnosis of your own business, but a topic beyond the scope of this book) to build a customer behavior spectrum mapping your true costs serving different customers. Will some require more on-site service than others? Which have shorter decision-making cycles? Those factors contribute to customer profitability, value delivery, and the price you can charge for bundled and unbundled offering features. You should also examine competitive strengths and weaknesses on key drivers as closely as you can.
With these data, you can cross-reference and compare lists of customer needs served and unserved, the seller’s advantages and resource limitations, and competitors’ abilities. Where do you have sustainable competitive advantages, and where do rivals hold the upper hand? Which customers can you, therefore, better serve than can competitors, and which are likely to be beyond your reach, assuming that prospective customers are well informed?
This step combines what you’ve learned so far about how customer values differ and about your costs and constraints in serving different customers. Unless you’re comfortable with multivariate statistical analyses accounting for several value drivers simultaneously, you’ll find it most convenient to segment your marketplace in multiple stages, value driver by value driver. The number of stages depends on the number of critical drivers that create substantial differences in value delivery among customer groups. In theory, your primary segmentation is based on the most important criterion differentiating your customers. Your secondary segmentation divides primary segments into distinct subgroups according to your second most important criterion. Your tertiary segmentation divides second segments based on the third most important criterion, and so on.
In practice, however, the deeper your successive segmentations, the more unmanageable the number of segments you identify. It doesn’t make sense to split hairs by segmenting according to drivers with less than critical discriminating power. Minor differences among such sub-segments will have little impact on pricing policies.
Also, your primary segmentation should account for your company’s capabilities and constraints as well as customer needs. A primary value segmentation that recognizes such a “strategic overlap” discriminates on what is likely to be the most important differentiator among customers: The needs that have the most impact on the seller’s operational constraints and whether those needs can be satisfied profitably, if at all. Your secondary segmentation, therefore, will use the value driver that varies the most among the sub-segments within each primary segment.
The example in Exhibit 2-10 illustrates the process for an industry-leading commercial printing company serving catalog marketers. Catalog companies have a variety of printing needs. Some are primarily concerned with brand image and ensuring that their direct marketing integrates well with their other sales channels such as retail stores. Others have unique needs, such as the ability to tailor catalogs to particular segments of a market by varying the “signatures” (groups of printed pages) bound into different parts of the print run. In this industry, print timing appears to be the major value differentiator. Some catalog companies insist on firm printing dates demanded by their business models, while others are more willing to let the printer determine when their jobs run. The strategic overlap is the cost-to-serve implication that results from the printer having only a finite number of presses and so many hours in the day, which limits the ability to commit to a firm print time.
Exhibit 2-10 shows a primary segmentation based on the strategic overlap of customer scheduling needs and printer operational capabilities. Two primary segments emerge: Buyers needing precise timing and those who are willing to relinquish timing control for a break on price. Within the “customer-controlled scheduling” primary segment, three secondary segments have different needs for special service, as follows:
EXHIBIT 2-10 Primary and Secondary Segmentation: Catalog Printing Industry
The printer originally treated customers who could be flexible in their scheduling like all other customers, assigning them firm print dates even as they demanded and negotiated lower prices. Value-based segmentation revealed that these buyers would be willing to trade some flexibility in scheduling for reduced prices. The printer could schedule their jobs for off-peak demand periods when capacity otherwise would be idle. These secondary segments differed by the services they would trade for a lower price:
Value-based segmentation variables can look fine to the price strategist, but segments should be described in everyday business terms so that salespeople and marketing communications planners know what kinds of customers each segment represents. Exhibit 2-11 lists the needs and typical firmographics of the customer-controlled scheduling segment’s three sub-segments. It also lists specific catalog publishers within each segment.
This is the next logical step in pricing strategy and management, a step we cover in greater detail in Chapter 4. Here, it’s important to recognize that segmentation isn’t truly useful until you develop the metrics of value delivery to market segments and devise fences that encourage customers to accept price policies for their segments.
Metrics are the basis for tracking the value customers receive and how they pay for it. For example, car rental companies once used a distance-based value metric and charged customers for the mileage traveled in addition to the time used. Over time, competition forced rental companies to drop mileage charges. Time alone has become the market-recognized value metric. Sellers define discounts such as weekly and monthly rental rates on time bundles.
EXHIBIT 2-11 Characteristics of Three Printer Customer Segments
Fences are those policies, rules, programs, and structures that customers must follow to qualify for price discounts or rewards. For example, minimum volume requirements, time-based membership requirements, bundled purchase requirements, and so on keep prices paid and the value delivered to customers in line. Some fences can also force customers to pay higher prices regardless of the seller’s costs; the notorious Saturday night stay requirements for reduced airline fares are a good example. Until competition forced airlines to drop the requirement, Saturday night stays effectively separated business travelers, who, presumably, could afford higher fares, from price-sensitive pleasure travelers.
Choose metrics and fences that establish and enforce premium prices for high-value segments, and allow feature repackaging and unbundling to appeal to low-value and low-cost-to-serve segments. As we shall see later in this book, the result is a menu of prices, products, services, and bundles that reflect different value received for different prices paid.
Identifying value-based segments, the metrics of pricing offerings, and the fences that maintain a price structure allows a marketer to expand its profit margins by aligning its prices, service bundles, and capacity utilization with the different value levels demanded by different customers. That’s a win–win balance for sellers and buyers; everyone gets something. But, as we will see in later chapters, just how much either side wins depends on how much of the differential value created in a transaction each side captures. That’s when policies to facilitate value-based price negotiations become important.
The foundation of a profitable pricing strategy begins with a complete understanding of the economic value the product delivers to buyers because, ultimately, value is the primary determinant of a buyer’s willingness-to-pay. This foundational understanding of value contributes to a comprehensive pricing strategy in a number of ways. First, it provides insight into how willingness-to-pay differs across segments. As the commercial printing company example illustrates, a value-based segmentation can inform not only pricing, but offering design as well. Second, understanding value is the only way to develop effective communications campaigns to increase customer’s willingness-to-pay. Although a hot beach-goer probably recognizes the value of a cold drink delivered to her blanket, most customers are not so well informed, and it is the job of the seller to get the value message across. Finally, value can and should be one of the key inputs to the price-setting decision because, as we demonstrated in Chapter 1, building a pricing strategy on other metrics such as market share or costs leads to less profitable results.
1. Ron Johnson was the Senior Vice President of Retail Operations at Apple Inc.
2 . Toyota fuel savings calculator. Accessed at www.toyota.com/priusprime/calculator.
3. Peter H. Lewis, “For Finances Past the Checkbook, a Small-Business Helper,” New York Times, May 3, 1992. Accessed at www.nytimes.com/1992/05/03/business/the-executive-computer-for-finances-past-the-checkbook-a-small-business-helper.html.
4. See Thomas Nagle, “Money Back Guarantees and Other Ways You Never Thought to Sell Your Drug”, PharmaExecutive, April 2008; and Tracy Staton, “Novartis Defies Naysayers with Newfangled Pay-For-Performance Deals on Entresto,” FiercePharma.com, February 10, 2016. Accessed at www.fiercepharma.com/sales-and-marketing/novartis-defies-naysayers-newfangled-pay-for-performance-deals-on-entresto.
5. The quote is attributed to Ken Olson, founder of Digital, who reportedly made the statement at a 1977 World Future Society gathering. We should note that the quote is a little out of context; in later interviews Olson explains that he was referring to the idea of the “connected home,” where lighting, temperature regulation, and tracking pantry inventories was controlled by computers. Source: www.snopes.com/quotes/kenolsen.asp.
6. For additional related discussion of this “proportional value-proportional price” argument, see Gerald E. Smith and Thomas T. Nagle, “Pricing the Differential,” Marketing Management, May/June 2005; and Gerald E. Smith and Thomas T. Nagle, “A Question of Value,” Marketing Management, July/August 2005.