How To Target Customers With Data
As the market emerges from this protracted recession, when marketers were laser-focused on maximizing the customers they had, a new priority is emerging. According to IBM’s most recent survey of marketing professionals, the top marketing challenge in 2013 for almost half of the respondents is new customer acquisition. Customer retention and loyalty came in second. The fact that marketers have prioritized customer acquisition indicates that the U.S. economy is recovering, and marketers are finally shifting from CRM to acquisition mode.
In order to help them target specific audiences, marketers now have access to troves of data; they can paint a richer portrait of their targets and understand the nuances of their audiences’ behavior. It makes sense, for example, that affluents take exotic vacations, buy expensive cars, subscribe to certain magazines, or own homes in certain ZIP codes. But the process of finding such consumers can be overwhelming to conduct manually. It takes a smart automated platform and scouring sets of data that are useful to a particular business, both by relevance and location, to find the right consumer who has not previously interacted directly with a business.
At our company, we have 3.4 billion consumer records that are at the disposal of marketers who want to reach the affluent, for example. Our clients can look at characteristics that act as predictors, and by diving deep into the data, marketers can clone their targets and generate messaging and creative with a proposition that will convert. Traditionally, marketers had taken the approach that affluents will always spend, no matter how the economy behaves, and the difference between marketing to affluents as compared to non-affluents has been one of message. But it’s imperative that even before they think about messaging, marketers find the right customers.
One of the main deterrents for businesses looking for new consumers has been the set of unknown factors in the undiscovered market. This is where big data comes into play. Big data provides real knowledge of the behavior of consumers in previously untapped markets, where the business or brand doesn’t already have a history and the first-party data that comes with it. Data helps marketers understand demand: According to a McKinsey study conducted over five years, businesses that prioritize data in their marketing and sales efforts can see an increased ROI of 15-20% — including a more-than-60% operating margin for retailers. Big data offers the opportunity to scale marketing efforts in ways that had previously been unimaginable.
Let’s take the example of a luxury automaker. There are millions of consumers who are interested in looking at photos of sleek, beautiful cars. Far fewer are in a
position to actually make a purchase. To focus on affluents looking to buy, and not just to stare, luxury marketers rely on “selects” to reveal users’ ability to buy. Not to be
simplistic, but if a consumer has shown an interest in high-end clothing, has taken several exotic vacations and stayed in luxurious accommodations in the past year, and purchased other
high-ticket items on their Black American Express credit card, those are indications of a more affluent lifestyle and buying habits.
By diving into the data and finding high concentrations of “clones,” other, similar consumers in nearby ZIP codes, luxury brands can recognize an untapped or an underserved market. Through algorithmic analysis of big data, marketers can understand the behavior patterns of affluents, and recognize the difference between aspirational browsing and real intent to purchase. With this data, marketers can scale their efforts like never before, and can approach new consumers in new markets with a level of personalization they’d previously only been able to conduct with existing customers.
Through quality data and smart algorithms, we can see who these affluents are (anonymously of course) based on what they do. And that understanding can make luxury marketing outreach more streamlined and successful, on a local, personal, practically one-to-one level.