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.
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.
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
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.
Among survey respondents, 60 percent of leading marketers believe DDA is essential to understanding the journeys of high-value customers. The more effectively marketers can do so, the better return they’ll realize on their marketing investment.
HomeAway, a vacation rental marketplace, used DDA to attain ambitious growth goals, according to Downey. “A siloed, last-click approach to measurement wouldn’t cut it” in an ultra-competitive industry. So the company adjusted its media strategy, aligning metrics and business goals. DDA enables HomeAway’s marketing teams to obtain, and act on, real-time insights into customer intent and interactions. The company saw a 46 percent increase in travel bookings in 2017, compared with the year before, and a 115 percent jump in revenue.
What customers want next
Rather than following customers on their journeys and trying to meet them along the way, marketers now need to predict where they’re going and help them get there. The capability to steer and even shape a customer’s next move opens a new competitive front. In the survey, 63 percent of leading marketers say they believe that anticipating consumer intent will drive greater results.
Manual marketing tools like probabilistic modeling and remarketing aren’t enough to keep abreast of today’s customers, who switch between channels and devices as they look for the products and services they need, according to Downey. To make sense of trails of customer intent signals, marketers need to do more. “They need to know what customers want before they do—and put the most relevant content in front of them,” he says.
TGI Fridays, the casual-dining chain, uses data about customers’ digital activities, preferences, and habits to evaluate and deliver timely messages over different platforms, optimizing its product strategy and media spending, according to Sherif Mityas, chief experience officer for the restaurant chain. Diners who have opted into sharing their data trigger personalized events. Guided by data, restaurant workers present options—soliciting a second drink or suggesting an appetizer—that will raise the amount of the average check.
For example, customers who occasionally appear at 5:00 p.m. on Wednesdays to share wine with friends will receive a message on the Fridays app, just before 5 o’clock, announcing the arrival of an unusual vintage. By analyzing check-level detail, the chain can tell that more than 25 percent of consumers receiving personalized messages will visit the restaurant and make purchases—without receiving a discount.
For data-driven insights to drive front-line strategy, analytics must be spread throughout the enterprise. In the past, the need for new marketing capabilities led companies to keep adding discrete departments—one for digital analytics, say, and another devoted to the customer experience. But the magnitude of the digital challenge supersedes such distinctions, as it also unites digital and offline channels in pursuit of a shared business goal.
“We leverage predictive analytics across a variety of business areas,” says Shyam Venugopal, vice president for global media and consumer data strategy at PepsiCo. “How do you make it more pervasive across everything you do? There’s always an opportunity to scale it further and further.” For customer insights to permeate the business, every team must incorporate data analysts. And leaders in every team must be given the power to turn data analysis into smarter business decisions.
With the advantage of an organizational structure that supports agility, companies can reach across consumer touchpoints. They can harness machine learning technology to tap into real intent and predict it, exceeding customer expectations. As marketers grow increasingly proficient at using these types of technologies, they’ll enhance both the customer’s journey—and the company’s long-term profits.
* In the survey, leading marketers were determined to be from companies that achieved more than a 15 percent increase in revenue over two years or a greater-than
Learn more about machine learning’s impact on marketing.