Warranties are part of the service component of the product mix.
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Product support can be as simple as a set of instructions and a throwaway wrench that comes with an assemble-it-yourself child’s bicycle or as complicated as warranty programs, service contracts, parts depots, and equipment on loan to replace a defective machine while it is being repaired. All of these constitute product support; they are designed to ensure that customers obtain the most value from use of the product after the sale. Such factors as heightened customer awareness and higher expectations about support levels, reduced ability to perceive product differentiation through superior technology and/or features, and improvements in support methodology have greatly increased the importance of product support in company strategy. The identification of customer expectations regarding product support and the development of cost-effective strategies for meeting those expectations is, these authors demonstrate, a major facet of successful marketing today.
When making purchases, customers often believe they are buying more than the physical item; they also have expectations about the level of postpurchase support the product carries with it. This support can range from simple replacement of a faulty item to complex arrangements designed to meet customer needs over the product’s entire useful life. Our investigations show that defining these expectations of support and meeting them effectively can be critical to a successful marketing effort. Consider:
- Caterpillar Tractor and John Deere, two companies whose marketing strategies are based on providing superior product support. Over the past quarter century both have concentrated on strengthening their dealers’ service capabilities and on upgrading parts availability. They have backed these efforts with extensive service staffs and emergency parts ordering systems. They have directed equipment design to emphasize reliability and serviceability, and to minimize downtime. These two companies have made product support cornerstones of their organizations’ corporate cultures and values.1 This has remained true despite damaging strikes, recession, and acreage taken out of production.
- The failure of Olivetti to establish itself in the United States, despite considerable investment during the past 15 years, primarily because of poor product support. The company has vacillated in its choice of distribution channels, thereby demoralizing its dealers. Parts and service training support have been inconsistent and usually poor. Initial buyer enthusiasm for new products has been repeatedly dampened by inadequate documentation and user training. As a result, despite excellent products at competitive prices, the company has failed to gain a strong foothold in the U.S. market.
Caterpillar and Deere illustrate the value of using support to improve marketing effectiveness. Product support, however, is an underutilized marketing resource in many companies. Developing and executing support strategies with marketing impact is difficult, and managers frequently do not know where to begin.
To maximize the marketing impact, managers need to have an accurate idea of customer support expectations and how to measure them. They can then use this information to segment existing markets in a new way or, in some cases, even to define new markets.
In developing a support strategy, it is necessary for managers to make trade-offs between effectiveness and cost. Our studies show that these trade-offs are often quite complicated and need to be evaluated carefully. Managers need to understand the nature of each trade-off and to develop a suitable framework for choosing among competing alternatives.
Why Support Fails
To many people, product support means parts, service, and warranty. In the early stages of market growth, customers concentrate more on technology and features and are concerned with only a few aspects of support, such as parts and service. As the market starts to mature, customer needs become more sophisticated. Product support encompasses everything that can help maximize the customer’s after-sales satisfaction—parts, service, and warranty plus operator training, maintenance training, parts delivery, reliability engineering, serviceability engineering, and even product design.
In many companies, however, the earlier limited view still holds sway; as a result they separate product support from marketing strategy. In our experience, companies in which this is the case exhibit some or all of the following characteristics.
An explicit support strategy is lacking.
The company views product support as a collection of individual tasks—enhanced product and/or service reliability, upgraded parts availability, improved training of service personnel, investment in additional service facilities—without an overall integrating theme. Improving support means “more of the same.”
Responsibility for support is diffused.
Many companies do not centralize responsibility for product support; individual departments such as reliability engineering, service administration, and customer relations carry out support tasks. As a result, management receives a disjointed picture of product support and its relation both to the customers’ needs and expectations and to the company’s overall product design and marketing strategy.
Support needs are considered late in the development cycle.
Managers often fail to contemplate such needs until after the design is frozen and the marketing strategy decisions have been made. Individual departments adopt support strategies that may not be compatible with one another.
Management focuses on individual support attributes.
Because of the diffusion of responsibility, management tends to focus on internal matters—engineering reliability, parts availability, warranty costs—rather than on customer-oriented measures such as downtime per failure.
Taken together, the foregoing characteristics lead to an often-observed cycle:
1. Top management becomes concerned about customer complaints relating to product support.
2. Individual departments demand more resources to improve customer satisfaction.
3. Lacking an overall strategy, investments in individual areas (e.g., reliability, parts inventories) rapidly reach a point of diminishing returns.
4. Customer complaints continue because basic problems have not been addressed.
5. The cycle repeats.
The net result is a waste of resources and potential or actual loss of market share to competitors with superior support strategies. To break the cycle, managers must first appreciate how customer expectations can affect support and marketing strategies and then learn how to use these expectations constructively.
Segmenting the Market
Customer expectations about product support add a crucial dimension to market segmentation. In most cases the package of support services that must be offered—implicitly or explicitly—changes significantly from one market segment to another. While many companies break down markets in terms of product features and performance, few segment markets on the basis of customers’ support expectations. The result is that some support areas are overserviced while others are neglected.
Think of a word processor for a secretarial station. Potential buyers range from small one secretary offices to large companies. There appear to be two market segments—one needing a basic model at a low price and the other a more comprehensive model at a higher price. Yet when customer expectations about support are analyzed, distinct differences emerge.
In the one-machine office the duration of downtime because of failure is crucial. Equipment failure means work virtually ceases, which can be extremely expensive. Disruption costs may be high because a small office cannot spare the people to search for replacements. The customer therefore expects both a low failure rate and minimum downtime per failure. Support costs or maintenance expenses are of secondary importance.
In the multimachine office downtime is important but not crucial; another functioning machine can be used to get important work out. Assuming that both the failure rate and downtime per failure are reasonably low, the customer is likely to be more interested in keeping maintenance and repair costs low over the life of the product.
These different expectations regarding support focus on varied attributes—failure frequency and downtime on the one hand, and maintenance and repair costs on the other—that form two distinct support segments. To meet customers’ needs in each segment, management can choose a variety of strategies. For the word processor market, a company could (a) design for higher reliability (and charge a premium), (b) provide parts and service support as needed without a fixed-fee service contract, (c) develop a monthly service contract, or (d) use a spare machine on-site and incorporate its cost in the maintenance contract.
Each of these support strategies affects such major elements of marketing as product design and development, production and delivery, sales, and pricing. Choosing the right strategy involves a series of trade-offs such as product cost versus support effectiveness, product cost versus support cost, and support cost versus support effectiveness.
The importance of customer support expectations as an added dimension in market segmentation now becomes evident: different strategies are best for different segments. Ignoring these differences runs the risk of under- or overservicing segments, or under-or overpricing the product and the support services. The three steps involved in developing effective support strategies for a given product are:
1. Defining customer expectations regarding support.
2. Understanding the trade-offs implied in each support strategy.
3. Identifying the strategies that best fit management’s objectives.
In planning a support program, however, managers need to be aware of the character of customer expectations, of the limitations of different support strategies, and of the interactions among strategies.
Defining Customer Needs
A major problem in segmenting the market on the basis of customer expectations lies in defining what these expectations are. Unlike product features or performance levels, customer support expectations focus on intangible attributes such as reliability, dependability, or availability. Without a suitable framework, the task of defining support segments is very difficult.
Because these intangible qualities can be viewed as proxies for underlying costs, the life-cycle cost concept used in equipment purchasing decisions can provide the basis for quantifying customer preferences regarding support. The life of a product after it is placed in service can be viewed as a sequence of uptimes and downtimes, terminated eventually by final failure, obsolescence, or sale and replacement. As the product goes through this cycle, customers can incur three types of costs:
1. Fixed costs on each failure occasion, independent of the length of downtime.
2. Variable costs that depend on the length of downtime and whose major component is the value of service lost (opportunity cost).
3. Maintenance costs of the product or service.
Because random events determine some of these costs, and since customers are likely to be risk averse, another factor must also be considered: uncertainty concerning the length and frequency of failure, the time needed for repair, and the magnitude of costs incurred.
To illustrate how underlying costs measure customer expectations, consider a washing machine used by a household and a large crawler tractor used by a builder. If the washing machine breaks, the homeowner incurs a repair bill (the fixed cost of failure). By and large, the homeowner is unwilling to pay a large premium to reduce the downtime (low variable costs of failure). Other things being equal, the purchaser of a domestic washing machine wants to keep repair costs low (high reliability).
On the other hand, if the crawler tractor breaks, the builder incurs significant fixed costs (of repair) and variable costs (wages paid to crews that sit idle until the tractor is back in action). Very often, the builder pays out more in wages for every hour the tractor is down than for repairs (the variable costs are far higher than the fixed costs). For this reason, the builder wants a tractor with both high reliability and low downtime per failure, and may even trade off reliability for less downtime.
In practice, customers incur fixed, variable, and maintenance costs. They are also risk averse and therefore concerned about uncertainty. Furthermore, as we have observed, in many cases customers do not clarify the relative importance of costs and risks. “want a dependable product” often describes a wide variety of support needs. To define customer expectations accurately it is therefore necessary to find out which costs and risks customers are likely to be concerned about and then to develop suitable techniques for measuring them.
Once the costs and risks of concern to the customer have been identified, managers can single out attributes such as reliability, availability, and dependability, and measure these in such terms as failure frequency, mean time between failures, downtime per failure, and the like.
While conceptually straightforward, translation of expectations into measurable terms is complicated by the fact that many customer expectations regarding support are nonlinear, support effectiveness is measured by many different variables, and statistical averages are misleading.
By and large, we are conditioned to think linearly: if one hour of downtime is bad, two hours are twice as bad. Unfortunately, customer expectations regarding support do not follow this simple logic. Instead, a threshold can be established for each expectation.
During the harvest season, for instance, farmers are extremely sensitive to the length of time a piece of farm equipment is out of commission because of a failure. Their reactions to downtimes lasting a half-day versus a day or more are vastly different. A downtime failure of a combine that can be repaired in four hours or less is tolerable; in fact, it often provides a welcome respite from harvesting. As the length of downtime increases past four to six hours, however, farmers become concerned, and by eight hours or so, they may be frantic. Beyond eight hours, the actual period of downtime is immaterial; farmers will go to almost any lengths to get up and running again—even if it means purchasing a new or used combine.
Farmers appear to have a similar threshold regarding the frequency with which a combine fails. Naturally, they hope it never fails; but, being realists, they’re willing to accept an average of one or two failures per season. Farmers’ tolerance of failure decreases very rapidly beyond this point, however, so that a combine design averaging three or four failures per season acquires a poor reputation. This attitude appears to be independent of the downtime duration at each failure; the number of failures is what the farmers remember, not how quickly the repairs were made.
Not all support expectations have clear thresholds. For instance, customers expect gradual improvements in the operational availability of a product or service (i.e., in its effective use during a given period). Since expected life-cycle costs—the purchase costs combined with discounted maintenance and repair costs less discounted salvage value, if any—vary in a smooth progression, expectations about these are predictable and linear. Customer reactions (to operational availability, life-cycle costs, and so on) are proportional to the value of the support variable.
Only in the case of low-cost household appliances like toasters or alarm clocks does a single variable such as reliability adequately measure support effectiveness. The farmer measures the support provided to his combine or tractor in terms of at least two variables—failure frequency and downtime per failure. The sophisticated purchaser of electronic office equipment weighs the support packages available as well as the training and programming assistance provided.
Moreover, customer preferences are often non-compensatory. Customers rank-order their preferences and do not consider an excess of one type of support as a substitute for deficiencies in another. A contractor buying a bulldozer, for example, wants both high reliability and low downtime per failure. He will be dissatisfied with any equipment that causes excessive downtime per failure, no matter how infrequently the failure occurs. Similarly, the office equipment buyer wants rapid response, irrespective of how infrequently it may be needed. For both, the risks and requirements of downtime are too high.
One customer may get a dreamboat; another a lemon or a succession of lemons. Parts can be obtained over the counter—right away or ten days later. To cope with random fluctuations, people tend to use the average or the mean: the average weekly sales, the average wage rate, the average time between failures, and so forth.
In our investigations, we found ample evidence that averages are not only misleading but potentially dangerous when measuring support effectiveness. An industrial equipment company, for example, prided itself on the apparently high reliability of its product. Engineering tests indicated that the mean time between failures for its major product line was 400 hours. Since the average annual usage was 600 hours, management felt satisfied; after all, the machine experienced between one and two failures per year.
On conducting a survey of users, however, the company received a rude shock. True enough, the average number of failures was 1.65 per year. But, more than 40% of the users reported more than two failures a year; and of those, 20% had four or more failures. As the sales vice president put it,“If that’s true, over 40% of our customers are not happy with our performance”
This situation is also true of other support measures such as downtime per failure. These measures tend to be distributed in a skewed fashion, with a significant proportion of them lying well above the mean. For this reason, the mean is an extremely misleading measure. A more appropriate measure is a percentile, such as 80th or 90th percentile of the variable in question. This measure would have shown the industrial equipment company that a large proportion of their users were in fact experiencing more than two failures a year. Similarly, the office equipment company that assured purchasers, “We can usually have a service person out to your location within four to six hours” would have found that response time in the 80th percentile was closer to two working days.
Choosing an Alternative
Having defined customer needs, the company can set about designing suitable support strategies. Normally, the manager can use one of several alternative support approaches. Each meets certain customer needs, such as greater reliability, shorter downtime per failure, or lower repair costs. At the same time, each affects the manufacturer’s costs or revenues by creating higher product costs, increasing support costs, or lowering revenues. Choosing an alternative involves a trade-off between the effectiveness in meeting customer needs and impact on costs.
Such trade-offs are complex; neither effectiveness nor cost can be judged in terms of a single variable. Since support strategies meet diverse customer needs and affect the manufacturer’s costs in various areas, trade-offs have to be made along several dimensions of effectiveness and cost.
Two additional factors further complicate the process of choosing a support strategy—the limitations of individual strategies and the interactions among strategies.
Limitations of Strategies
The building blocks of any support package are the individual strategies designed to improve reliability, make the design modular, provide equipment on loan, and add diagnostic capabilities. Exhibit I lists some typical strategies, together with suppliers’ costs and customers’ benefits. While the impact of each varies with the technology and the industry, we have observed that all strategies exhibit diminishing returns to the customer, increasing costs to the supplier, and limited areas of impact.
Exhibit I Support strategies: costs and benefits
Every support strategy produces diminishing returns with respect to customer benefits; beyond a certain point, further improvements are increasingly ineffective. For example, reliability improvements that extend the mean time between failures increase the availability of equipment to the customer, but the rate of increase slows down past a saturation point. Customers recognize this phenomenon, and once this point has been reached their focus shifts to other concerns, such as repair time.
The initial improvements in any strategy are the simplest and therefore the cheapest. Succeeding improvements are progressively more expensive. It will cost the manufacturer more to raise the mean time between failures from 100 to 150 hours, for instance, than it did to raise it from 50 to 100 hours.
Each of the strategies shown in Exhibit I affects only part of the failure and restoration cycle. Diagnostics reduce the time required to locate the failure but do not affect repair time. Providing equipment on loan lowers the variable costs of a failure but does not alter the fixed costs.
Interactions of Strategies
The foregoing limitations require the use of a suitable combination of individual strategies to meet customer needs. In synthesizing an overall strategy, a manager must know how individual strategies interact to ensure that the proposed combination achieves desired levels of customer benefits while keeping supplier costs as low as possible. Specifically, the manager needs to be aware of how strategy interactions can raise or lower overall costs to the supplier, complement benefits, and cause benefits substitution.
The way in which separate strategies interact affects the overall cost. For example, increasing reliability will lower the cost of supplying equipment on loan. It may, however, raise the cost of warranty repairs because it requires more expensive components.
Certain combinations tend to reinforce the benefits of individual strategies. For instance, diagnostics are more effective with modular designs, which, in turn, are more effective when used in conjunction with on-site repair.
One strategy may serve as a substitute for another in terms of customer benefits. For example, speed of repair is less important when equipment loans are made available; therefore, both diagnostics and to a lesser degree modular design are substitutes for equipment on loan. Modular design reduces the need for large field inventories of spare parts; thus, these two strategies are to a certain extent substitutes for each other.
Developing a Structured Process
The need to use several measures of cost and effectiveness and the limitations and interactions of individual strategies make a structured process for choosing support strategy essential. In its absence, a manager may not realize that existing strategies cost more and are less effective than alternatives, may yield to the pressures of individual departments and choose a suboptimal strategy, or may fail to make the decisions needed to stay competitive.
While situations vary, in general a manager should:
Define suitable measures of cost.
Life-cycle costs are often appropriate; other measures can also be used.
Categorize all feasible support alternatives.
Alternatives involving major design changes should not be excluded as they could be essential to improving support effectiveness.
Develop techniques to evaluate the cost and effectiveness of alternative strategies.
Computer simulation, use of mathematical modeling, or field trials may be useful.
Measure the cost and effectiveness of each alternative.
As measurements will be imprecise, it is necessary to show ranges and estimates of error.
Choose one measure of cost and another of effectiveness and plot the results.
The most important or significant measures should be analyzed in this step; other measures will be checked later.
Identify key strategies.
Some strategies will stand out as superior in cost and effectiveness.
Repeat trade-off analysis using other measures.
Determining if different measures change key strategies is a valuable check.
This process can narrow the options to two or three major choices. The final decision will depend on external factors such as management’s preferences, the competitive situation, or other marketing or product concerns.
What to Focus On
Support strategies are not static; a strategy that is effective today will, if unchangeable, become ineffective in meeting future customer needs. Generally, customer satisfaction increases with improvements in one area (e.g., reliability) up to a point. As diminishing returns to the customer set in and the manufacturer’s costs increase, companies will need to switch to another, often radically different strategy, like lending equipment. And when customers demand higher levels of satisfaction than can be economically provided with loaners, the company has to switch to still another approach, like improving access to components that fail and thereby reducing repair time.
This pattern appears to be characteristic of product support systems in general. A different, dominant strategy provides the most customer satisfaction at successive stages, and the level of customer satisfaction increases progressively. Each rise in the level of satisfaction raises the manufacturer’s costs and accentuates the need for choosing another, more efficient support strategy.
To ensure that their products remain competitive, managers must identify the various stages that exist for their products and market segments. Having chosen a support strategy, they must ascertain their company’s and competitors’ relative positions, anticipate when customer needs or competitive pressures will require the company to shift to the next stage, and plan for shifts in support strategy.
A manufacturer’s relative market position often determines support strategy. If customers and competitors perceive the company as a leader in identifying and meeting support needs, management can set the pace at which support effectiveness is improved. On the other hand, a company that is perceived as an “also ran” has to follow and, if possible, anticipate changes in the strategies of any leaders or in customer expectations.
Since shifting to a new stage raises the level of effectiveness considerably, companies that are slow to react to changes in customer needs and/or the level of support provided by competitors risk being frozen out. Improvements in the level of support given in other industries, raising customers’ expectations across the board; pressure on competitors to maintain or increase market share; and introduction of new support techniques—any or all of these could signal the need for a shift. Managers also need to plan for such shifts to ensure that existing support strategies don’t box them in.
Designing Support: A Case Study
An industrial equipment manufacturer started to design a new series of industrial tractors to replace its current models in the mid- to late-1980s. The company was aware of customer dissatisfaction regarding existing levels of support, which it had made several efforts to improve. Realizing the value of designing support and product strategies in parallel and recognizing that responsibility for support was fragmented, management appointed a study team reporting to the president. The team’s charter was to develop strategies that would profitably deliver superior support for the new series of industrial tractors.
Existing marketing data indicated a number of diverse customer needs regarding product support. Although it identified individual support elements such as greater parts availability, higher reliability, and more service training, the data gave little insight as to how customers measured overall support effectiveness or made purchase decisions.
To determine failure frequency, causes of failure, downtime per failure, and the components of downtime, the study team mailed a survey to the entire user population and received more than 3,000 responses. In addition, the team used a combination of focus groups and in-field interviews to determine customer preferences and develop measures of support effectiveness.
The team’s investigations showed that customers focused on two key factors: the downtime caused by an individual tractor failure and a combination of how often their tractors failed, how much total downtime these failures caused, and the level of regular maintenance required.
The relative importance of these factors in purchase decisions varied. For one customer group, downtime per failure made the greatest impact on purchase decisions, while a second group weighed other factors as well. This suggested the existence of two separate support segments. The team decided to use the following measures of support effectiveness for both segments: the 85th percentile of downtime per failure (i.e., no more than 15% of the failures exceed this level of downtime), and the annual operational availability or ratio of uptime to the sum of uptime, downtime, and maintenance time.
The team felt that the operational availability ratio best captured the effects of improvements in engineering reliability. It also smoothed out random fluctuations in parts availability and showed the impact of improvements in maintainability and serviceability.
When the team analyzed the causes of downtime, it found that parts delay accounted for more than half the total downtime, with repair time taking up a third, and travel time the rest. This fact suggested some alternative strategies: increased dealer-level parts inventories, improved service training, and the use of mobile repair vans. Where downtime was critical, equipment on loan would be furnished, if economical.
When the team analyzed the causes of failures, it discovered that a large number were breakdowns in electrical and hydraulic components. Individually, these failures were easy to repair; their cumulative effect was, however, large. Engine and power-train failures did not have the same impact because, while each failure caused considerable downtime, such failures occurred infrequently. These facts suggested some additional strategies: improved reliability (especially of electrical and hydraulic components) and tractor design to permit modular exchange of defective components in the field.
After identifying all feasible alternatives, summarized in Exhibit II, the team developed a computer-based simulator, which duplicated as far as possible the effect of using a given strategy or combination of strategies in terms of downtime and operational availability. Finally, the team calculated the costs of the various alternatives, using a life-cycle cost model.
Exhibit II Alternative support strategies for an industrial tractor
To identify the optimal choices, the team then plotted the costs and effectiveness of the various strategies. Exhibit III shows a typical plot. Overall, effectiveness improved substantially (for example, the 85th percentile of downtime was reduced from 45 hours to 10 hours or less. However, support costs increased at least fourfold too). In addition, the analysis showed that:
Exhibit III Trade-off analysis plot of support strategies
- While parts delay was a significant factor in total downtime, improving parts availability had little impact. This was because most repairs required several parts, and absence of even one part caused at least a one-day delay because of shipping time.
- Built-in diagnostics had little impact; in most cases, diagnostic time wasn’t important.
- Equipment on loan was not economic until overall reliability had reached approximately 400 hours between failures.
- Equipment loans and modular exchange were complementary. Loaners reduced customer downtime while modular exchange reduced the number of loaners by allowing rapid in-field repairs.
As shown in Exhibit III, there were basically three stages—from 50 hours of downtime to 30 hours, from 30 hours to 20 hours, and from 20 hours to 10 hours. In each stage, the most efficient strategy was quite different. In Stage I, improving reliability was the best strategy. In Stage II, providing loaners was the most efficient, while in Stage III, a combination of modular exchange and loaners was most efficient.
After reviewing the team’s analysis, management decided that under current market conditions, supplying loaners (Strategy F in Exhibit II) was the most cost-effective. However, loaners would not provide long-term advantages because competitors could easily do the same. Therefore, the company decided to make major changes in its design philosophy and to aim for greater modularization of critical components. A combination of modular exchange and loaners would provide superior support at least cost, while the long lead times required for design changes would ensure long-term competitive advantage. Management therefore decided to proceed as follows:
1. Improve the reliability of its existing design to allow use of equipment on loan.
2. Introduce equipment on loan (Strategy F) in the mid-1980s, or earlier if competitive pressures demanded it.
3. Change its design approach to allow progressive modularization of key components.
4. Switch over to a combination of modular exchange and loans (Strategies H and I) in the late 1980s.
The industrial equipment manufacturer needed three to five years to change its design and to modularize its components. Had the company concentrated on improving reliability of its existing product, it would have found itself locked in and unable to change without incurring large engineering and tooling costs as well as a premature phase-out of its current designs.
A version of this article appeared in the November 1983 issue of Harvard Business Review.