Do you know many 20-somethings who own a RV? How about any Millennials (the generation that is fanatical about wealth) who think it's a good idea to accumulate colossal debt for a depreciating asset? Neither do I.
But that's not what many data mining services would have me believe. Perhaps it's because data brokers know that I like to travel based on my web browsing and purchase history. Or perhaps some third-party cookie data showed that I browsed for luxury goods recently, and thus inferred that I must be fabulously wealthy.
Whatever the reason, this much is clear: Marketers have created a temple that needs to be torn down. For more than a decade, data from third-party browser cookies have told brands and advertisers who their consumers are -- poorly. The result is rampant mistargeting of content and offers, which has understandably left consumers fed up. Nearly half of consumers will ignore a brand after being targeted with irrelevant content just twice.
When Acxiom recently created a way for consumers to see exactly what information is tracked about them, many were surprised to see just how inaccurate our consumer profiles really are. And yet marketers continue to pay for access to this information, and we rely on it to inform our segmentation and targeting strategies.
No wonder that ad for RV accessories keeps showing up. If I actually owned one, these ads would be relevant, and possibly even useful to me. Maybe our targeting algorithms aren't broken after all -- maybe we just need better data.
With web browsers and regulations cracking down on third-party tracking cookies, marketers are suddenly scrambling to determine how to identify their consumers and target them with personalized, relevant content. As new ways to collect both demographic and psychographic customer profile data have emerged, marketers now have much better ways of segmenting their customers and targeting them using more accurate first-party data -- data that brands collect and store on their own, as opposed to data purchased from third-party sources.
Big players like Pandora, Facebook, Google, and Microsoft were quick to recognize this tectonic shift in how we can track consumers, and they are rapidly adjusting strategy. The information consumers provide via registration or account creation, coupled with the ability to tie their behaviors across devices to a single customer, makes login data the new cookie. What cookie-based data lacks in accuracy, it certainly makes up for in scale since not every visitor will log in. But, what login data may lack in scale, it certainly makes up for in accuracy and quality.
The term "holy grail" gets thrown around far too often in marketing circles, but at the risk of introducing hyperbole, psychographics are fundamentally what marketers want to know about our customers. Right? We want to know what consumers like, their interests, and how they think. But psychographic data is like unobtanium -- everyone covets it, but except for complex inferences based on predictive modeling or surveys plagued by low response rates, it's always been hard to get.
Think about it. These days, we employ expensive analysis on clickstream behavioral data, or we buy third-party data, just so we can make guesses about what consumers like and how they think. What if there was a better way? What if you could just ask consumers for permission to this information, and they gave it to you?
Thankfully, there are technologies that make this possible. Social login, for example, lets brands access customer profile data with permission from the consumer. People are maintaining a lot of detailed information about themselves on social networks including demographics, hobbies and interests, favorite books, music, movies, TV shows, sports, and more.
And because this information is visible to our friends, it is much more accurate than third-party data or data inferred from browsing behavior. Let's just say that you won't find any pictures of my RV on my Facebook profile.
Just how valuable is this type of psychographic data for brands that want to remain relevant? One music label saw the number of people who read and take action on its emails improve by 600 percent after it began segmenting consumers and targeting them with artist recommendations, album releases, and upcoming concerts, all based on the music interests declared within their social network profiles.
A news media publisher increased the CPM rates it was able to charge by 20 percent, all by gaining the ability to segment its audience based on declared interests from user profiles. If an advertiser knows exactly who it is targeting, it will pay more money to publishers in order to reach those consumers.
Shopycat, a product discovery site created by Walmart Labs, recommends products to shoppers based on their Facebook "likes." So, if you're a comedy enthusiast and a fan of Bradley Cooper on Facebook, and you agree to share some of your social profile information with Shopycat, you may see a recommendation for The Hangover Part III instead of that new historical biopic about Hannah Arendt.
As they used to say (a long time ago), you can't make a silk purse out of a sow's ear. Similarly, marketers can't solve their mistargeting problems when they are relying on bad data. Ad tech has improved rapidly during the past decade, and marketing teams are full of smarter and more creative talent than ever before. By building customer profiles using the right data, marketers can survive the cookiepocalypse and remain relevant in the minds of their target consumers.
Michael Olson is a product marketing manager at Janrain.
On Twitter? Follow Olson at @michaelolson10. Follow iMedia Connection at @iMediaTweet.
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Micheal, I enjoyed reading your article. For the past few years, I've been helping publishers, brands and agencies begin to understand that declared data trumps inferred/cookie data by huge margins. Simple question. Which is more valuable? Your car's dashboard that tells you your condition OR one that tells you what a group of drivers like you did, in the past. The latter would make driving a risky adventure of misreads and missed destinations. Hmmm...sounds like ad targeting today. Yes, the declared vs inferred conversation has been challenging because the amount of that data is small by comparison. (Warning - Obvious Statement follows) However, that makes sense because ANY site-based data should be much smaller than data from the entire internet. Smaller, but accurate and meaningful to buyers and sellers alike because it's real. People don't lie about their interests or act in ways not consistent with their own interests. So, declared data demystifies targeting in useful ways.My point has been that the declared data, on your site, is the best data to infer, model or proxy in order to actually form a plan and target. Any publisher can leverage declared demo data AND declared behavior to understand their audiences better and set up revenue and targeting models that actually make sense. But they have, until now, had no place to sell that in real terms because the buying community has focused on cookie-powered audience disaggregation. In meaningful ways, all forms of declared data returns us to content and context. And context, for most marketers, is still the best amplifier of messaging and messaging effectiveness.
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