What is first-party data? Does it apply to anonymous users or only known users? How accurate is it? Is it really relevant to our business?
Today, let's set the record straight on some common misconceptions.
First-party data is any data your organization has collected about your own customers, users and site visitors.
Email addresses and usernames, while absolutely first-party data, are just the tip of the iceberg.
There’s a huge misconception that first-party data is synonymous with email address, username or known user. But the reality is that Customer Data Platforms can track users across browsers and devices and stitch that information together at the individual profile level and all of that information is first-party data. All first-party means it that you collected it directly. It means you got the information straight from the source.
Anonymous website visitors are assigned a profile just like a known user. Though you can’t send an email to an anonymous user, you can still gain rich insights about them by looking at their behavior and browsing habits. Then you can reach them through other channels, like:
You can even package targeted segments of anonymous users to sell at a premium to advertisers.
Every second, users interact across your web properties and generate hundreds of thousands of events. Every click, every page view, every activity leaves breadcrumbs of data that can be pieced together to uncover a deep picture of each individual user—even if they are anonymous.
All of their behavior, preferences and content interests are extremely valuable to advertisers and can be used to provide a more personalized experience that increases engagement. With help from a Customer Data Platform that collects and organizes all of the data generated by anonymous site visitors, you can treat anonymous users just like you would known users.
First-party data is extremely accurate because it’s all real, observed data from your own sources and tools. Third-party data—particularly when it’s sourced from a Data Management Platform (DMP)—is notoriously inaccurate.
It’s hard to know precisely how inaccurate DMP data is, as there is no universal authority deciding just what qualifies a person as an “auto intender” or a “technology enthusiast.” However, you can get a general idea. Head over to the Oracle BlueKai Registry and see which segments you belong to. The registry was set up by one of the leading DMPs to allow consumers to see what kind of information is provided about them to advertisers.
I am personally in over 1,350 different segments—including four different age brackets, two different genders and three different income levels. According to the registry, I’m simultaneously a renter and a homeowner. I live both alone and in a household with multiple children. I’m a fan of TV shows I’ve never watched, and my hobbies include activities I’ve never tried.
With a Customer Data Platform, you can make your own rules to define what makes a user valuable and layer in data science, content affinity and look-a-like modeling to extract even more insights that reflect the real value of your audience.
Many companies ignore user-level web data because it’s hard to capture and use in a meaningful way. This data is largely unstructured and it’s generated so rapidly it’s impossible for a human to process and use it (which is why AI and machine learning are a must if you want to really harness the power of your data).
Companies tend to rely on things like income level, age and gender to predict if someone will click on an ad, read an article or pay for premium content. Companies also use intent data—provided at the cookie-level by black-box DMPs—which is, at best, not useful and, at worst, wildly inaccurate. It’s also extremely expensive.
Unstructured data, on the other hand, is arguably the most valuable user data you collect because it can show us trends in a person’s behavior, interests, activity level and intent. When used properly, unstructured data is a much better indicator that someone might be interested in a certain article or click on an ad than demographic data with negligible accuracy.
This is a common sentiment from media companies, particularly those with a local or niche audience. The problem arises when they try to create a targeted audience using generic, pre-defined criteria from a DMP from within their own user base.
One news-media company we spoke with was looking to close more business with automotive advertisers, but when they tried to find members of their audience who were “auto-intenders” using their DMP, the audience was miniscule. They couldn’t find a way to offer a targeted audience at a scale that was worth it to automotive advertisers.
But here's the thing: The company had an entire section of their digital newspaper dedicated to consumer automotive articles—covering topics like which cars guzzle the most gas, how to find the value of your trade-in and how to negotiate at the car dealership. Using Lytics’ Content Affinity technology, the company could uncover which of their own users recently engaged with those types of articles and create an audience in minutes.
Even better, they could filter by users who had recently begun engaging with automotive articles, a strong indicator of purchase intent.
First-party data is a publisher’s most valuable asset, and there are a variety of ways to use that data to grow audiences and increase revenue from advertisers. If you’re interested in learning how your business can leverage its first-party data for more personalized subscription content, advertising and marketing programs, we’d love to hear from you. Reach out for a demo today!
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