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Aftermarket Services: The near-term growth opportunity in targeting the right customers

There’s a gold mine in aftermarket services. But companies first need to know where to dig—and how hard.

For industrial manufacturers, capturing organic growth through new-product development can be slow, costly, and risky—as necessary as long-term innovation may be. Companies easily spend years, and millions of dollars, on R&D, prototyping, customer research, and marketing before finding out whether the new offering will resonate with customers.

In contrast, growth through aftermarket services offers a comparatively short and straight path to new revenue streams. By improving their approach to aftermarket services, companies can capitalize on their existing portfolio and installed base, along with existing account data, to segment customers and focus resources on the most attractive leads. The result is improved financial performance that sustains the company through the larger, long-term bets it makes.

But to succeed, industrial manufacturers must understand their customer base and target them based on their propensity to buy aftermarket services. In other words, how likely are these customers to buy something other than the product (such as parts, a maintenance plan, logistics support, or parts-management services)?

Manufacturers already know these customers, yet their sales forces may lack the scale or skill needed to prioritize likely buyers. But by using customer and market data—both internal and external—manufacturers can apply advanced analytics to quantify the propensity to buy services, and thus segment their customers accordingly.

Industrial companies that understand their customer base, adequately prioritize aftermarket sales, and relentlessly focus on execution can boost their services revenue by 30 to 60 percent within three to five years—without requiring large investments in capex, new-product development, or cost-reduction programs. Moreover, this growth directly feeds cash flow and tends to be stable through business cycles. And it can be quickly monetized, within just weeks in some cases.

Understanding the aftermarket opportunity

Industrial aftermarket services comprise a wide range of offerings, from spare parts to software to field operations, end-of-life services, and guaranteed up-time (also known as “power-by-the-hour” arrangements). Collectively, these offerings create a large value pool that few companies fully exploit, often because someone else is providing these services other than the company that sold the product.

Consequently, in most industries, the percentage of customers who purchase services in addition to equipment is low. In agriculture, for example, aftersales among even the highest-performing manufacturers typically cover less than half of the installed fleet, on average. Other industries, such as power generation equipment, small electrical equipment, and civil aerospace, show huge disparities between low and high performers, with best-in-class players exceeding 80 percent aftersales coverage (Exhibit 1). That leaves significant room for manufacturers to improve.

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The financial rewards can be significant. For large, rotating equipment, such as turbines and engines—which have complex servicing, relatively high failure rates, and longstanding customer relationships—lifetime revenue from services can exceed that from the original equipment sale, and EBIT margins can be four times as high (Exhibit 2). In contrast, for industries such as power transmission and distribution, where failure rates (and servicing needs) are lower, the opportunity can be smaller—yet even in these sectors the difference in EBIT margins can be just as high.

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Given this growth potential, it is fair to ask why more manufacturers do not put a concerted effort into selling aftermarket services. In our experience, they operate with a number of misperceptions. One is that manufacturers do not have the sales resources to devote to aftermarket services. Their field teams are so busy selling equipment—admittedly larger transactions—that they cannot be pulled away to focus on the services business. Other manufacturers may believe that success in aftermarket services requires a strategic reboot, or that companies need to create a dedicated services organization. Our experience suggests that these are all myths.

On the contrary, success in aftermarket services can come regardless of a company’s strategy or organizational structure. Why? We have found that aftermarket services are an execution game. It does not require a new strategy or a dedicated business function. Companies that improve their sales force effectiveness—by understanding their customers’ relative propensity to buy services—will win.

Deploying the “Propensity to Buy” approach

To that end, industrial companies need to analyze their installed base in order to understand a customer’s propensity to buy—i.e., its likelihood of purchasing aftermarket services. This seems axiomatic, but in practice it is rare. Many manufacturers that apply sophisticated targeting and prioritization programs to customers for equipment sales do not apply a similar approach to aftermarket services. Segmenting potential services customers in this way should be easy, given that companies have far more information about them—through existing account information—than they would for a typical sales lead. A healthcare technology manufacturer, for example, looked at whether its customers had their own in-house maintenance team, meaning it would have a low propensity to buy such services from the manufacturer. Similarly, looking at geographic locations of customers helped a manufacturer develop a view on their need for back-up generators based on the relative likelihood of storm-related power outages.

By aggregating this data and supplementing it with external public information on customers and markets, companies can run analytics to group customers into three categories: 1) high propensity-to-buy; 2) low propensity-to-buy; and 3) those in the middle, or on the fence (Exhibit 3).

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This kind of segmentation, together with the customer’s size, can lead to counter-intuitive choices. It turns out that it’s the middle that matters most in sales efforts. For customers in the high-likelihood group, sales teams will likely be able to close a deal with a minimum number of contacts. Similarly, among the low-likelihood group, the company should limit their approach to low-cost resources such as email or 3rd-party marketing companies, with the understanding that those customers are probably not persuadable. (One caveat: Manufacturers may still decide to devote their best resources to large customers in these two categories, since those accounts have the potential for correspondingly large potential services revenue.)

The third group—those in the middle—should be prioritized leads for the sales force’s attention and resources, through in-person contacts, targeted offerings, and other incentives. A manufacturer sales team, for example, was spending equal time reaching out to all customers. After using the propensity-to-buy approach and applying advanced analytics, the sales team spent 50-60 percent of its time on the middle group and was able to increase services revenues by $30 million—a 10 percent improvement—in less than three months.

By using a data-based means of prioritizing accounts, companies can create strategic account plans that replace the gut-instinct approach that the sales staff may have used in the past. Leaders can identify sales opportunities they have overlooked due to organizational blind spots, and ensure that they are seeing the right customers at the right frequency. Even for high-propensity customers that ultimately don’t buy services today, these kinds of interactions can influence future budgets and planning, reinforcing longer-term relationships.

To make sure that these insights are actionable, information can be fed into a live dashboard that identifies priority leads for the sales force and is constantly updated by fresh data, including new equipment sales and updated account and market information. Ideally, the dashboard can even develop specific targets for each sales rep weighted to the leads’ quality and propensity-to-buy score. This information also feeds into sales-force performance management: A salesperson with more high-propensity-to-buy leads, for example, will have a higher yearly target.

Case Study #1

Consider a large power-equipment OEM. The company offers long-term services contracts along with one-off services to its installed customer base—but it was struggling to prioritize promising leads. The company launched an advanced-analytics segmentation initiative. It sifted through data already on hand from customer accounts, including:

  • whether the service opportunity was associated with a power outage
  • the type of sale (parts, service, upgrades, repairs)
  • product-specific information, including the age of the current equipment
  • the length of relationship
  • recent sales behavior such as their price-sensitivity and brand loyalty
  • size of the customer and location

Through this analysis, the company bundled customers into the three propensity-to-buy categories of high, low, and in the middle. It also built two models—one based on power outages and a second one based on sales related to other needs—and it sorted potential aftermarket offerings by the dollar value of the transaction.

The analysis pointed to 39 percent of the customer base that should be prioritized by the sales team—those with a high propensity to buy (these leads should be allocated in relatively short time) followed by those with a medium propensity to buy (particularly with a potential sale). This analysis eventually resulted in a 30 percent increase in service revenues with no increase in the manufacturer’s sales resources.

Case Study #2

An aerospace supplier had insufficient customer data available due to internal system issues and a non-transparent market. The company took an alternative and complementary approach, leveraging data and analytics to improve aftermarket profitability. Spare parts, which represent the bulk of the aftermarket revenues, were clustered to reflect the acceptability of a price increase from a customer perspective. Clustering was based on:

  • Criticality of the part to the platform’s performance and safety, i.e. the platform cannot fly without that part,
  • Availability of third-party alternatives to OEM-built parts, and
  • Type of usage of the part: whether it is directly replaced on the platform, or is part of a more complex repair, making its price central to the business model of intermediary repairers.

Based on these three dimensions, a statistical methodology enabled clustering of over 40,000 parts into seven groups. The analysis of each group demonstrated a price-increase potential at the part level, if parts, for example, had historically experienced a lower price increase or if they had a lower price-to-cost multiple than the cluster average. This enabled a yearly upward price evolution of about two percentage points, while maintaining customer satisfaction levels and eventually leading to a margin increase of 25 percent in three years.


Aftermarket services is among the largest untapped sources of revenue and growth opportunity for most industrial manufacturers. It’s also one that is relatively easy and quick to capture, for those manufacturers willing to give it the attention it deserves.

About the author(s)

Guy Benjamin is an associate partner in McKinsey’s New York office; Harold Brink is a partner in the Boston office; Florent Kervazo is a partner in the New York office; Hugues Lavandier is a senior partner in the New York office, and Ilan Rozenkopf is an associate partner in the Paris office.

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