A customer’s journey is as unique as her fingerprints—which explains why marketers are harnessing technology to analyze, classify, and reach customers on the basis of which actions they take before they convert.

In the pre-digital past, a buyer’s journey was predictably linear; now it can involve numerous searches and site visits, traversing several different devices. The twisting trek that customers take—progressing through research and consideration to the brink of conversion—doesn’t end when a purchase is made but continues through loyalty and potential repeat purchases.

By understanding the unique customer journey for different segments, marketers can gain insight into the behavior of their most valuable ones. There are now tools and technologies, such as machine learning and data-driven attribution, that enable marketers to focus on customers who have the highest potential lifetime value (LTV), strengthening ongoing customer engagement and, ultimately, boosting business.

Lifetime achievement

As consumers use an ever-expanding collection of touchpoints, brands must find ways to calculate which marketing activities—and in what combinations—drive returns. It’s no longer sufficient for marketers to measure their impact as they have traditionally done: isolating and assessing a few key variables after a campaign. Nor is it enough to simply monitor last-click activity, attributing conversions to a customer’s most recent activity.

Image credit: Kheng Ho Toh

With access to analytics technology, brands can reallocate marketing budgets, using real-time data about how effectively some marketing activities are performing compared with others. Marketers can also test different scenarios to pinpoint the ideal amount to invest in certain channels along the customer journey.

Figuring out what those high-priority customers look like requires mastering LTV, a forward-looking measurement of the overall value that a customer will generate throughout the relationship with a brand. When marketers focus on LTV, they can identify the customers who bring in more business over the long term—and then spend more marketing dollars to reach them. In a recent survey of 1,419 marketing executives, conducted by MIT Technology Review Insights in association with Google, 89 percent of leading marketers use strategic metrics such as gross revenue, market share, or LTV to measure the effectiveness of their campaigns*. In fact, LTV is the metric used most by 51 percent of leading marketers.

“Do you look like a high-value customer? If you do—based on what I know my high-value customers do—then I can market to you based on the return you’re offering,” says Allison Hartsoe, founder and CEO of Ambition Data, a data analytics consulting firm.

Papyrus, the stationery and greeting card retailer, is one company zeroing in on LTV. Partnering with Google, the company recognized that members of its Perks loyalty program were 66 percent more valuable than other customers, says Sean Downey, vice president of platforms at Google. Expanding its marketing on those customers, Papyrus increased profits tenfold in three months. “By using LTV, Papyrus determined that Perks customers were the biggest spenders and the most frequent shoppers,” says Downey. The Perks program also enabled better segmentation. Papyrus was able to reach out to lapsed members with special offers and personalized messaging to help drive further engagement.

Revving the data engine

Do you look like a high-value customer? If you do—based on what I know my high-value customers do—then I can market to you based on the return you’re offering.

Machine learning enables brands to cull insights from voluminous data, evaluating the effectiveness—as measured by the behaviors of customers—of different paths. Machine learning works fast, continuously updating its model. It’s a quick study, too; Hartsoe cites a publishing company that can render an LTV estimate using just two days of a customer’s initial interaction.

Data-driven attribution (DDA) uses machine learning to calculate the contribution of each customer action along the conversion path. It examines how people find a business and decide to become its customers, and then assigns credit to conversions, according to Downey. That helps marketing teams determine which ads, keywords, and campaigns will most directly affect business goals.