Here is a brief excerpt from an article written by Brian Gregg, Hussein Kalaoui, Joel Maynes, and Gustavo Schuler for the McKinsey Quarterly, published by McKinsey & Company. To read the complete article, check out other resources, learn more about the firm, obtain subscription information, and register to receive email alerts, please click here.

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Personalization drives growth. But to scale it, companies need to do four things.

Customers decide very quickly—in a matter of seconds—whether they like your marketing message. Provide something relevant and you’ve got a satisfied customer. Miss the mark, however, and they’re gone.

This issue of relevance in our era of instant gratification is particularly pronounced because consumers are bombarded with messages, most of which are off target. Personalization—the tailoring of messages or offers to individuals based on their actual behavior—promises to address this issue.

While many companies have been able to personalize with a few product lines or segments, most still struggle to scale across all the ways they engage with customers. And although technology has an important role to play, in our experience, most companies already have plenty of tools. The real challenge is to transform the marketing organization’s processes and practices to achieve the full potential of personalization.

Done right, personalization enhances customers’ lives and increases engagement and loyalty by delivering messages that are tuned to and even anticipate what customers really want. These benefits to the customer translate into benefits for the company as well. Personalization can reduce acquisition costs by as much as 50 percent, lift revenues by 5 to 15 percent, and increase the efficiency of marketing spend by 10 to 30 percent.

Through more than a hundred engagements over the past five years, we’ve found four steps that lead to successful digital personalization at scale. [Here are the first two.]

Step 1: Take a journey lens: Use behavioral data to find where the value is

The foundation of personalization is acting on behavioral data. The first step is to group customers with similar behaviors and needs. For example, mothers who exclusively shop a brand for their children or fashion-conscious young women who buy new private-label styles. Most companies find it useful to start with eight to ten such behaviorally based segments as a first step in their evolution to 1:1 marketing.

The next task is to understand, for each segment, the customer journey3—the series of interactions with a brand from initial consideration, to purchase and use, and then to subsequent purchases. Marketers can do this by integrating information from internal sources such as visits to the company website, purchases at a store, or calls to the contact center, with information that can be acquired from external sources, such as prospects’ visits to a competitor’s website.

Combining these segments and customer journeys creates hundreds if not thousands of “microsegments,” which form the basis of 1:1 personalization. Not all microsegments are created equal, of course. The potential of each must be evaluated and prioritized carefully, based on relative value. For example, a leading retailer we worked with determined that it is more valuable to engage its customers within their “resupply” window—e.g., by reminding them they may be running out of toothpaste and their favorite brand has a limited-time offer—than by pushing them to go deeper in the category by suggesting other oral-care products such as mouthwash or teeth whiteners.

Step 2: Listen and respond: Plan in advance to react quickly to customer signals

Personalized marketing is a two-way street: The customer provides signals—information about his or her needs and intentions—through activities like purchases, online browsing, and social media posts. The company responds to the signal with a relevant and timely message, which we call a trigger, that is sent to the individual customer.

Doing this effectively requires careful advance planning. The marketing team needs to develop a library of trigger messages matched to individual signals.

Trigger messages can be of different types—images, copy, titles, offers—that can be combined dynamically to match the situation.

Coming up with trigger events involves creative problem solving grounded in sound analytics. For example, a next-product-to-buy algorithm based on machine learning could send a message suggesting a set of related products triggered when mothers have clicked on a different product but not bought it (see sidebar, “How personalization that works creates value for customers”).

For all the preparation, getting the full value from triggers requires a test-and-learn process: sending an initial message, evaluating the results, altering the trigger, and measuring the results again. It typically takes four to five attempts to refine a personalized trigger to capture 80 percent of its potential value. For example, a leading apparel retailer we worked with went through four different iterations of a next-product-to-buy email until it found the winning formula, which ended up yielding twice the impact of the first iteration. Additional refinement after that usually yielded diminishing returns.

These sorts of personalized triggers have been shown to be three to four times more effective than blast messages. They can also introduce the customer to new products and new modes of interaction (such as buying online versus in store) that are more convenient, further enhancing their experience.

Once a signal and associated trigger have been shown to be valid and refined, it becomes a business rule, and all future customers associated with the signal automatically receive the appropriate trigger message.

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Brian Gregg is a partner in the San Francisco office and leader of McKinsey’s Digital Marketing Practice; Hussein Kalaoui, Joel Maynes, and Gustavo Schuler are associate partners in McKinsey’s Los Angeles office.