Big Data and the 1 Percent

Luxury shoppers make up only 3 percent of the population. And truly wealthy, versus affluent, customers make up an even smaller percentage—that fabled 1 percent.

These consumers look and behave differently than the average consumer online in terms of everything from what they buy to the types of devices they use to buy them. But as different as they are, identifying and reaching them online isn’t always easy. A recent Luxury Institute study surfaced the fact that 63 percent of affluent consumers would choose to opt-out of online tracking, given the opportunity. That’s a pretty strong indication that these customers, more than other groups, cherish their privacy and don’t necessarily want to be found online.

In the era of Big Data, there’s enough information available to marketers to seek out nearly any target audience, including affluent and wealthy consumers. With the right data, marketers can successfully reach these desirable consumers—and ideally, deliver a message or offer that will encourage them to opt in, rather than out.

How are affluent consumers different?

Affluent consumers are different from average consumers in ways beyond their buying power—their behaviors are different, too. Practical Ecommerce reports that affluent men shop frequently and spend liberally online:

There are approximately 19 million men with an income of over $100,000 a year and based on the survey results, comScore extrapolates that 27 percent of the group make purchases on a weekly basis; 13 percent of this audience spends more than $30,000 annually online, and almost 50 percent spend more than $4,000 a year online. Of those that spend more than $30,000 a year, 39 percent are making purchases more than twice a week; 84 percent of these men are shopping for themselves.

To balance that data, Luxury Institute recently reminded marketers that affluent women typically rule the household. In their survey of 800 women making over $150K annually, these women were making 68 percent of household purchases, and more than 40 percent were the family breadwinners.

What are the best data sources for targeting affluents?

The first thing marketers must understand is that Big Data does not necessarily have to be complicated or overwhelming. Often a company’s own data provides the most actionable insights. Purchase data and browsing history from a retailer’s site, for example, can be a tremendous source of customer information. Look at how customers navigate your site pre-purchase, what drives them back, and what the unique behaviors of frequent customers are. Understanding your customer and website data will provide rich, actionable information about who your customers are, as well as how, when, and what they’re likely to purchase.

To that point, once you know who your customers are, it is time to understand how they actually engage with your messages. Campaign analytics are a terrific data source. Gleaning a wide array of information from the entire flight of a campaign can offer surprising findings that, when coupled with psychographic and demographic data, create a powerful mixture of intelligence.

Whether you leverage contextual data to understand what content environments help drive or deter results, or whether you analyze how the creative itself has impacted engagement, there are several opportunities to find hidden gems from live campaigns. For instance, an advertiser may want to reach C-level executives who are responsible for purchase decisions. With the brand’s own data, they may know who this audience is and where to find them—across the business sites they frequent. But through a further analysis of past campaigns, they may learn that this audience is more likely to engage with the message on sports and lifestyle sites when content is integrated with the ad unit.

Furthermore, if available, loyalty program data can be extremely helpful. Not only has a customer willingly provided personal data in this situation, they’ve opted in to provide information about their preferences and purchase history on an ongoing basis.

Social media is also an excellent data source. Social data can teach you not only what excites consumers about your brand, it can reveal affinities your active followers have for other socially active brands—television shows, musical acts, sports teams, etc.—revealing insights that can inform marketing and product decisions.

You’ll also want to review device-specific data: Do your customers tend to shop more from mobile devices or PCs? Who’s using which devices and at what times?

You have the data: Now what?

With all this information, it’s important not to get caught in the weeds. Big Data is called that simply because there is so much of it. Deciding which data points are most important to you will simplify things considerably. With so many cuts available, it’s critical to determine the ones you absolutely need to make decisions that will impact your bottom line. So, for example, if you’re selling luxury items online and launching campaigns regionally, you’ll probably want to know where your most profitable consumers live, but you may not need to know what level of education they’ve achieved. You may want to know their gender and what their ages are, but you may not need to know where they work.

Technology certainly goes a long way in helping us make sense of all this data, but the best way to crystallize the data is to bring a human element to the process. Hiring smart individuals that understand how to interpret trends and turn that into actionable insights is vital. The technology we have all come to rely on is great, but it’s still only a tool that needs to be wielded by capable data scientists.

Big Data may sound intimidating and confusing, but it doesn’t have to be. DMPs and ever-improving analytics are making it easier to create more effective marketing and advertising campaigns. In fact, with only the essential data in front of you, you’ll have a virtual roadmap to an effective campaign.

Erik Pavelka is COO of Martini Media.

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