Published Thursday, Oct 24, 2019
Marketers have long used the buyer's journey as a framework for understanding consumer needs and desires at a given point in time. Historically, this model has been a fairly limited tool for deciding how to engage with a customer because traditional advertising tactics rarely allow marketers to gain real insights into consumers' thoughts and feelings.
Thanks to modern technology, that’s changed. Consumers are constantly generating data that marketers can collect and analyze to get a much clearer picture of what they’re thinking and feeling. Moreover, evolving technology has fundamentally changed the way consumers interact with brands and the expectations they have for those interactions.
These changes mean that traditional buyer journeys are no longer sufficient as marketing campaign guides. Assessing the consumer buying behavior process is now a data-driven exercise rather than one built on assumptions, and marketers must rethink the buyer’s journey stages in that context.
The new consumer decision journey includes a multitude of online and offline experiences, and it must lead to more targeted messaging that moves customers closer to purchase. Of course, there are a number of factors affecting consumer buying behavior that can come into play at any given moment, and marketers aren’t mind readers. However, there are four key data points that advertisers can focus on to identify where an individual consumer most likely falls along the larger consumer decision journey:
As consumers, our online activity provides marketers with a wealth of information about who we are and, thus, what products and services might appeal to us. In particular, our content consumption habits give marketers some useful clues.
The key word here is "habits." This data is most useful when aggregated over time. If we’re generally consistent about what we read and watch on the web, marketers can draw solid conclusions about our interests. For example, if you’re on ESPN all day, you can appropriately be classified as a sports fan. If you spend your free time browsing restaurant menus or seeking out Ina Garten’s recipes, it’s probably fair to put you in the foodie category.
If your behavior suddenly changes and you begin reading something new or different, this might signal a change in your intent. Whereas interest data is derived from behavior over time, intent data tells marketers about what you might be in the market for at a specific moment in time. According to Demand Gen Report’s yearly "ABM Benchmark Survey," just 25% of B2B companies are taking advantage of intent data and monitoring tools. If you’re in the other 75%, you’re missing an opportunity to reach your customers in the right place, at the right time, before a competitor does.
Location data is more than just a set of GPS coordinates. At any given moment, those coordinates can’t tell marketers anything more than where you are “right now.” They don’t say anything about your buying habits or interests. However, if marketers can observe your locations and movements over time, they get some interesting context. Based on where you live, for instance, they can begin to draw conclusions about you by observing what your neighbors are doing in aggregate because neighborhoods are largely homogenous. Based on your most frequented retailers and grocery stores, they can determine whether you’re in the market for luxury goods or good deals.
Again, if this data suddenly changes (you’re visiting car dealerships or real estate offices), marketers can draw significant conclusions about your intent. For example, say your son wants a pet fish. You might be against it, but you're not opposed to taking him to the aquarium aisle at PetSmart every once in awhile, if only to kill time while Mom is shopping at DSW next door.
At any given moment, your location at PetSmart doesn’t say much. But when assessed in the context of other data — where you live, the schools you spend time at, the sporting goods stores you regularly visit, and the youth sports content you're frequently clicking on — your PetSmart location becomes just a tiny piece of a much more significant picture of you as a consumer.
Marketers can rightly conclude that you're a father of two kids who have a soccer season right around the corner. Certain consumer characteristics influence buying behavior more than others, and the aggregated data says it all: “Forget the fish food. Sell that man some shin guards!”
Past purchase data is a fantastic way to predict future purchases. Marketers can look at where you buy, the categories you tend to buy in, and the amounts you typically spend when shopping. This gives them a good idea of your income and helps them make solid guesses about what you might buy next. If someone spends more than $50,000 on a loaded SUV, it makes sense that the buyer might be interested in related items like a towing hitch, a horse trailer, or a camper.
Netflix’s famous viewing behavior algorithm provides an iconic example of how past behavior can be used to predict future intent. The vast majority of activity on its site is driven by a sophisticated recommendation system, and the accuracy of its recommendations shows just how predictable we are as consumers.
Most consumers don’t go into a store unless they have some interest in buying something (either from that store or within the category of products that store offers). Many of us are simply too busy to waste our time browsing offline. Sure, you’ve walked into Best Buy with at least some intent to buy a TV, but just because you walked out empty-handed doesn’t mean you've changed your mind.
Foot traffic doesn’t always mean a purchase is imminent, but it typically is preceded by signals of interest and intent, and until it stops altogether or a purchase is made, a consumer is likely moving forward along the buyer's journey. If you see the latest Samsung model and get an offer to mount it at a discount, that might just seal the deal for you.
Each of these areas gives marketers a different view of a consumer’s mindset. It’s tempting to think of them in a linear sense, but try to avoid that trap. Most of us will bounce around from phase to phase, and where we are at any one point in time might be completely irrelevant. When aggregated over time, however, savvy marketers can use the information gathered from these data points to understand what we’re interested in and to convert intent into purchase when the time is right.
To learn more about the evolving consumer journey, check out Valassis' webinar "Powering the Evolving Consumer Journey," featuring digital marketing expert Joanna O'Connell, vice president and principal analyst at Forrester.