Chapter 9

Segmentation, Context, and Time

Now I am become Time, the destroyer of worlds.

—THE BHAGAVAD GITA

One size fits nobody.

—THE NEW YORK TIMES, ARTICLE ON CLOTHING SIZES, APRIL 24, 2011, PAGE A1

Context and segmentation both stem from the same truth: not every person and every situation is equal. Therefore, both contextual pricing and needs-based segmentation try to base market actions on what is happening in the customer’s mind. They both also take into account the question of price structure and price level. So what is the difference between them?

Differences between Context and Segmentation

The main difference is that contextual pricing focuses on the impact of the customer’s environment, adding the element of causation and a framework for addressing changes in the market. Another way to phrase it is that segmentation is typically based on the class of person or business; context is based on situation. Needs-based analysis will usually provide an insight like this one: “A single male needs to impress his date, so he buys flowers to give to his date.” Context, on the other hand, would go deeper: “A single male who still does not have flowers for his date by 6 p.m. on Saturday is pretty desperate and will pay anything to get some flowers or flower substitutes. On Friday, he will not have given it much thought.” Notice the difference?

In many cases, the key contextual element is what has changed—people notice change, and sometimes reduce the weight they give preexisting decision factors. Context may also focus more on whether third-party behavior has influenced the buyer decision framework. For instance, if the potential flower-buyer senses strong competition for the attentions of the lady of his dreams, he may buy a bigger bouquet. The important point is that in comparing demographic bases for competition with different contexts, the contextual differences are more important for pricing. An illustration of how segment and context interact is in Table 9-1.

Table 9.1 Interaction of Content and Segmentation Provide the Field of Required Prices

You may ask: “How the h—l are we going to address a constantly changing set of circumstances? It’s enough work to keep up with just demographics change (e.g., an increase in Latino demographics, younger marriage age, income shifts, etc.); it seems like contextual situations would end up shifting even more.” Actually, not so.

Contextual events that shift demand and price sensitivity are as predictable as segment or demographic changes; you just have to be aware of them. In fact, much of context is more certain than demographic change: people will always be late and require rush orders, committees will always be divided, buyers will always focus on larger purchases, consumers will always compare prices if they can easily do so and make less comparison if that is not easy, people on hot beaches will always pay more for soda. Sound reasonable?

An advantage of context is that it explains causality of customer price preferences and in some ways is more constant than segmentation.

Management should take advantage of traditional segment information. To obtain the best pricing, however, it must also take advantage of contextual information. Just as some segment information requires work to assemble, so does contextual information. Since segmentation has had a 40-year head start with management, your company probably has assembled more segment information.

Contextual data is also available—it just takes a decision to procure it. You can find it hidden in usage data or coincidentally gathered in market research. One advantage of context over segment data is that context is quite obvious. For instance, you cannot easily tell if a phone user is Latino or Caucasian, but you can tell if she is traveling or at home or at college.¹ You cannot always tell if a shopper is old or young, but you can tell whether he is buying last-minute before a holiday.

A final assurance on context to allay any reader fears of added complexity, which may still be present after many pages: unlike segmentation, which often increases the number of price points, context can reduce the actual number of realized prices by 80 percent or more because it allows enforcement of price points. Welcome news, and easier to administer because actual customer prices are what propel your company’s results. Even better news: the modest increase in target prices (contextual baseline prices) can easily be handled by systems. A system lookup table is how your company probably prices now, and this lookup table does not care if it is looking up contextual baseline prices or nominal list prices.

Segmentation and contextual information are not substitutes; they complement each other in decision making. Therefore, you should initially consider context as an overlay on segmentation. Since segmentation came first, we show context as the incremental layer on the pyramid of management tasks shown in Figure 9-1.

Figure 9-1 Context is the final stage of price setting.

This is an additional layer, but one (among others not illustrated) that pays for itself, many times over.

How Segment and Context Complement Each Other

Despite having spent some pages distinguishing context from segment, it is important to note that context and segment go well together. Best of all, you can use both to improve pricing. Let us take a look at a case study from a B2B market. This case study involves five steps taken over time, pertaining to the computing and data communications equipment market during the late 1980s to the early 2000s.²

During this time, as is well known, the method with which users processed business data evolved from mainframes to distributed processing. This evolution occurred over five distinct phases that companies passed through, each of which represented a context for data communications vendors. Some users passed through a few stages, while others went through all five.

The first phase, which harkened back to the prehistory of the 1970s, was a mainframe-only approach featuring a central information technology (IT) department. This was the IBM-dominated Hierarchical Proprietary stage.

Many companies next experienced a radical policy and power shift in information management: the end users of computing decided to buy PCs, minicomputers, and server clusters, bypassing the IT department. Inevitably after buying this equipment, the end users tried to network these devices, leasing lines and setting up networks that were independent of the IT department. Doing so involved major infrastructure buys. For instance, in the early 1990s at United States Trust, the IT department conducted a survey and was surprised to discover that more than 230 networks had been set up by end user departments. This phase was called the Departmental Computing stage.

The next phase was an organizational response to the chaos produced by departmental mixing and matching. Companies formed an independent central-purchasing department, or shared service, that tried to regain financial control and reduce costs. This effort economized through the consolidation of purchasing and the physical consolidation of networks. This phase was called the Telecom/Computing Purchasing Function (Utility) stage.

In the fourth stage, coming after a period of several years, companies integrated telecommunications and computing purchase, again within the IT department. The new arrangement typically allowed end users greater access to information, which was kept in large corporate databases administered by IT. To access these large databases, high-capacity data networks were created to allow selected (“mission critical”) users in remote locations to pull down large fi les through a corporate network and work on them on-site.

For instance, the pharmaceutical manufacturer Merck & Company allowed mission-critical staff, like researchers, to pull huge molecular models out of data repositories and download the material to their workstations. This architectural phase was called Mission Internet.

The last stage is either an alternative or a successor to the fourth stage. Instead of giving over the corporate databases back to the IT department to place in a few large repositories oriented to the most important end users, some companies adopted an architecture to allow all end users to access any data they need. Doing so meant throwing out a lot of obsolete equipment and introducing a homogeneous infrastructure in which all the components could connect with one another. This was expensive and, for the time, technologically quite complex.

To pay for the new equipment and endorse the technological risk of this new architecture, non-IT business leaders had to be intimately involved and convinced of the need for the expenditure. Thus, the new power structure was a close partnership between IT and functional departments. This stage saw departmental computer specialists stripped of some of their autonomy as a cohesive new approach to purchasing and technology took hold. This final stage was called Integrated Computing.

The Contextual Logic Chain

The initial shift from the monolithic architecture of the first stage set in motion forces that inevitably propelled a company up the evolutionary chain, link by link. This is illustrated in Figure 9-2.

Figure 9-2 The evolution of context as computing and data communications architecture evolved

The relative inflexibility and slow response times of the first stage set in motion the contextual evolution. Powerful division and functional heads decided they could do better than the IT department in building needed computing capabilities and funded that effort. They brought in new technologically savvy managers and told them to do what was needed to improve speed and capacity. The new departmental managers did exactly that, but with little regard to expense. This was because the new computers did not seem like a big budget increase when compared with their overall operational budget.

After a while, however, the poorly managed multiplicity of computers and networks in the second (Departmental Computing) stage became expensive—sometimes growing to account for more than 10 percent of a company’s total cost base. This expense provoked top management to reduce costs. Thus, many companies ceded departmental control back to IT or, more commonly, to the newly created Computing and Utility Purchasing function. Which route they chose depended on whether or not IT had become more flexible and responsive.

Because the Utility phase had a strong focus on minimizing costs, there was usually little attention paid to innovation. This resistance to new technologies, many of which would benefit line operations, caused line managers to look for alternatives to the “penny-pinching” purchase function. This pushed a company to move on to the next evolutionary change in which dissatisfied end users were wooed by a new, more enlightened IT department. Business managers looking for new capabilities, and IT managers looking for greater architectural consistency, drove department heads to accept a return of central IT and seek more forward-looking approaches. This pushed them into the subsequent phases.

The choice of the next step, whether to move to Mission Internet or Integrated Computing, depended on a company’s business economics, in particular the contexts of budgets and mission needs.

If a company’s overall return on investment was high, it would most likely choose elegant and uniform database architectures (Integrated Computing). This was the case at Morgan Stanley and at Federal Paperboard, the specialty manufacturer, which both had ample cash to invest in internal information technology. Typically, this architecture was administered by IT, but oversight was by IT and senior line managers, vice president and above, who got intimately involved in funding choices and alternatives.

Alternatively, many larger enterprises with lower rates of return on investments could not afford to replace the existing hodgepodge with a clean new data architecture, which usually required all new software and often a major makeover in hardware. In that case, line managers handed over the problem to IT. That is what happened at the Chrysler Corporation and other large manufacturers. The buying context of Mission Internet was more price sensitive than the Integrated Computing phase, especially for less “mission-critical” functions.

The strong internal logic of the evolution formed the context for purchases of computing and data networks. Understanding this context allowed understanding of buying behavior. In the move from the Hierarchical Proprietary stage, departmental computer specialists sought immediate solutions to interconnection requirements. Thus the performance of equipment and ease of use were key factors. In the move to the Utility phase, cost and centralized control played the central role. In the shift to Integrated Computing, the focus shifted to the ability of equipment and vendors to perform smoothly in conjunction with software packages and to innovate to meet end user needs.

To highlight how different these requirements were, note that a low-cost price tag is needed to win only in selling to the Utility phase. Thus, it would be foolish to deeply discount pricing in bidding in the other environments. On a more subtle level, Mission Internet will be price sensitive in buying for nonmission-critical users but not price sensitive for critical users. Pricing was evaluated in terms of individual component buys with limited global procurements.

In an Integrated Computing context, price pressures were low if you could meet the technical requirements. This fueled explosive growth and the success of Sun Microsystems, Cisco, and others. Price structures favored global sourcing and sometimes enterprise pricing, not component buys. Successful vendors had to understand this logic (low cost versus high capabilities, special user groups versus enterprise, etc.) in order to price optimally. This required an appreciation for the differences in context and—even better—an overall road map of potential migration paths.

Although most industries appear to evolve in coherent fashion as managers adapt to the same technological, social, financial, and regulatory changes, suppliers rarely exploit this fact fully. Planners routinely identify key industry trends on an annual basis, but they do not always tie these trends to pricing segments, strategies, and targets when analyzing market opportunities. For analysis, planners often revert to more limited but safe and convenient segmentation categories such as Standard Industry Classification (SIC) codes or customer size. Low effort, but lower utility.

Most industries appear to evolve in coherent fashion, allowing sellers the opportunity to anticipate changes in buying criteria and decision makers.

Context, as we have seen, involves more than simply purchasing prepackaged groups of sociological/psychographic/demographic classes. These prepackaged collections will rarely bear any resemblance to situations created by fundamental forces changing a company’s market. Superior pricing is based on understanding the underlying reasons for the creation of new forms of demand.

Context is also different because industries do not ever repeat a “cycle” in that predictable sense. Market contexts seem to change with broad industrial and social changes, which have the effect of constantly shuffling the deck for all the players at the table. For instance, software companies today are subject to greater economic, competitive, and quality pressures than they were 10 years ago, forcing them to respond in different ways as well.

Benefits

There are three benefits to contextual understanding highlighted in the computing case study: reaching customers when they are ready to buy, understanding how to appeal to them, and setting price structures and contextual price levels. Most important of these is that context helps companies recognize opportunities before competitors do.

Being slightly ahead of the competition is a huge price and service advantage. By utilizing evolutionary understanding, a company can deploy products and pricing in the right way at the right time. Examples of “early is best” include Best Buy. Originally an also-ran electronics superstore, Best Buy boldly innovated to address the increasingly sophisticated customers who desire a friendlier, less-pressured purchasing environment. That context changed during the 1990s in that electronics became more standard and consumers were no longer intimidated by electronics purchases, so they no longer needed the guys in ties to help them select a product. By being the first specialty retailer to understand this new context (which, initially, also happened to mirror an age demographic), Best Buy outperformed its traditional competitors. Best Buy’s decade-long compound sales growth rate was more than 75 percent per year.

An understanding of evolutionary contextual paths can turn to gold for a vendor’s sales force. That is because every step taken on the contextual journey changes what matters inside a potential customer’s organization: decision makers, buying criteria, and budgets. The sales force that can anticipate these changes can get to a new decision maker before competitors do. Arriving first, they can begin shaping the decision maker’s time frames and criteria. When the laggards finally arrive at the new decision maker’s door, they may find that it has already been closed.

Summary

Context and segmentation are both vital to companies that seek to maximize value capture and profitability. These two concepts work well together, but management might wish to emphasize context because it is the new kid on the block and can be the more powerful lever of pricing results.

Context will help identify times and situations where price can be taken. Time is particularly important because the same buyer (person) will behave quite differently at different times, because context has changed.

Understanding context makes the difference between servicing a growing willingness to pay for a company’s special attributes versus responding to a heterogeneous collection of needs with one solution that fits nobody well.

Vendors that understand and categorize context can reap huge rewards. By rigorously isolating different contexts, observing how customers’ buying behaviors change in different contexts, and perhaps categorizing customer evolutionary stages and predicting potential next steps, vendors can understand customers’ future decisions earlier than the customers themselves—and far sooner than the competition. This can result in superior sales growth.

Notes

1. Actually, with sharp and creative IT, today many companies know a lot more about their customers than what makes it into their pricing. For example, when AT&T was looking for ways to commercialize its calling pattern information, some remarkable inferences emerged. One was an analysis of calling patterns that could show dual road-warrior couples and when there might be a need for flowers. A particular pattern of call frequency spikes and locations showed stressful episodes. Correctly, this kind of inference was treated with caution, but the point is that it is possible. It may be even easier on the Web. See also “A Web Pioneer Profi les Users by Name,” The Wall Street Journal, October 27, 2010, p. A3.

2. This section is built, in part, from an early article before the concept of context was recognized as distinct from segmentation. At that time we called it “evolutionary segmentation,” which was imprecise but still useful. Rob Docters, John Grim, and John McGady, “Segments in Time,” Strategy & Business, first quarter 1997, pp. 42–51.