We are not marketing mind readers, but we are getting closer thanks to the next generation of consumer behavior data. That’s a relief for marketers who are exhausted by hit-or-miss efforts that produce minimal sales but lack the customization necessary to drive big gains backed by genuine loyalty.

A 2016 Direct Marketing Association and Winterberry Group survey sheds light on the current state of data-driven marketing: About 41 percent of marketers reported their companies’ gains from such marketing tactics improved from the first to second quarters of 2016—but the remaining 59 percent reported no change or a decrease in those returns. Fortunately, personalizing customer interactions using data will help boost that revenue.

Before the internet era, retail interactions had a personalized touch because sellers and buyers were connected by proximity. Although we have lost a sense of physical closeness, we are poised to regain a similar relationship.

Data-based insights into customers’ activity and future behavior allow us to “circle back” to a time when the neighborhood grocer knew you so well he could make accurate guesses about your buying needs each visit. Netflix is developing its Nostradamus-like skills right now, using predictive software to make relevant user recommendations.

The key to foreseeing consumers’ needs is in how we use big and unstructured data, social listening tools, and other resources and information to track their journeys, ultimately allowing us to dive headfirst into understanding target audience populations.

Measuring the Steps of a Digital Footprint

Every customer leaves behind clues on the internet about his preferences and likely next moves. From what he says on Facebook to how and when he browses the web, his actions and habits reveal elements of his psyche. This digital footprint contains vast amounts of data, and we are finally beginning to understand how to bring it all together.

For instance, consider how customers are making their way around the web to find and purchase merchandise and services. Jumpshot sifts through the online movements of 100 million consumer panelists. Our goal isn’t to develop and record only linear movements—it is to understand the context behind those actions.

It is not enough to define your ideal customer; you must also know how to focus your attention, energy, and resources on how, when, where, and why they make purchases. The only way to do this is to use available data in creative ways.

Similarly, customer on-site and in-app behavior pulls back the curtain on how audiences really live, not how we assume they live when we’re brainstorming at work. If you can use collected data to count on where a customer will be at a certain time, you will see a boost in ROI and future opportunities.

A Closer Look at Customer Habits

Let’s say you build and publicize a music app that isn’t selling like you thought it would. By collecting and analyzing data about the people who are buying your app, you realize they tend to purchase sports items on Amazon. In other words, physical activity is important to them.

Knowing this, you explore the time of day they use your app and how long they use it. It’s an “Aha!” moment. You now understand when they will likely be listening, and you can tailor advertising to them during those times—for example, you could play ads for sports gear.

By interpreting the language of the data and implementing what you’ve learned, you have a better chance of making your app users feel like you understand them intimately. This ratchets the user experience to new levels, especially when it’s combined with customer browsing behavior data.

Many marketers see click-through rates as numbers or percentages, but these rates hold the secrets to behavior prediction. Which call to action gets clicked most often? Which is overlooked? How many pages are consumers visiting, and which ones seem to be constant exit doors? Analyzing customer browsing behavior information allows marketers—especially those at e-commerce sites—to maximize every opportunity to reach interested audiences.

Stay Ahead of the Competition With Predictive Marketing Strategies

Interested in developing ways to read your customer’s minds? Begin connecting with customers through data-driven personalization methods.

1. Predict customer purchase behavior.

Your customers might already be segmented into a few groups; it is time to sift them further. As you develop more groups of users with similar interests and habits, you can customize the way you approach each group by their previous actions.

Look beyond what’s occurring on your site to what’s happening on other sites. Purchasing behavior elsewhere can be a treasure trove to modern digital marketers.

With this newfound information, you can explore the unique traits and habits of each customer “bucket,” noting where, when, and why purchases occur. Your insights might even provide the basis to partner—or compete—with companies or domains that serve as natural magnets for a portion of your target user base.

2. Explore customer on-site and in-app actions.

App use is gaining momentum, up an average of 11 percent annually with no signs of slowing down. Understanding which apps your customers actively use, and when they do so, can unlock partnership possibilities.

Detailed data insights are becoming available for in-app and online behaviors, but knowing how to use those insights is key. For example, when you know a customer’s normal browsing behavior, you can identify when that behavior suddenly changes. That switch can signal that the customer is now in-market for a specific purchase rather than just browsing.

Developing marketing campaigns on the basis of changes in users’ intent rather than just their normal consumption habits can help avoid headaches and save money.

3. Analyze customer online behavior data.

The best way to gain insights into who your customers are and how they are likely to behave is to see what they do everywhere online. You can then compare their activity against the activity of the general population.

As differences bubble to the surface, work with your demand-side platform to build look-alike models. Look-alike modeling moves past cookies, which can be deleted, dropped, and blocked; it focuses on actual individual customer browsing activity.

Not familiar with the term? You are in good company. A quarter of marketers are confused about what it is and how to apply it. Here’s an easy way to think about it: Your best customers are out there, and if you leverage historical data and third-party data sets, you can unearth prospects with “look-alike” traits and interests to target.


Digital marketing is at a crossroads. We have tons of information at our fingertips; we just need to use it wisely and consistently. As we create comprehensive models of customer profiles based on big data, we get closer to a predictive, two-way seller-buyer relationship. It is as close to reading minds as you can get without a crystal ball.