The true question, though, lies in whether the sum total of first-party data conceivably available to a brand is really captured and converted into something meaningful. In general, the most common category of first-party data is the type collected based on the behaviors, actions, or interests demonstrated across an advertiser’s Web sites and mobile apps. This could be anything from a single visit to the homepage, to an app download to an abandoned shopping cart.
As mechanisms to collect and store first-party data have grown more sophisticated, so too have the types of data able to be leveraged in this way. Now, an advertiser’s first-party data arsenal might also include offline data, CRM data, subscription data, and data collected from social media. Yet while brands have become more adept at harnessing these types of data, one source of first-party data—the ads marketers serve—is almost completely untapped.
If you’re an advertiser, here are four ways you should be using data collected from your own ads in order to market more effectively.
Frequency capping across demand-side platforms (DSPs). When buying across multiple DSPs at one time, most brands think it’s virtually impossible to enforce global frequency caps (how would the DSPs communicate?). However, if you’re synching audience segments between your ad server and various buying platforms, all you would have to do is create a dynamically updating segment of users who received an ad a set number of times, and then anti-target that segment on your buying platform. As soon as a user reached the defined threshold and became part of the given segment, you wouldn’t bid on that impression. And there you have it: cross-channel frequency capping.
Create user-interest data. Most buyers are familiar both with the practice of contextual targeting, and with the concept of publisher interest data. But if you’re buying inventory that meets a certain context across a large, diverse set of publishers, and capturing those users through the ads you serve, you have essentially just created your very own user interest data. For example, if you segment users exposed to a campaign where you bought only “gardening” content, you would end up with a segment of users interested in gardening.
Retarget engaged users. The most common type of retargeting, site retargeting, works by effectively limiting your targetable pool of users to people who have visited your Web or app property. But what about people who clicked on, played with, expanded, or otherwise interacted with your display ad? What about people who got through an entire in-stream video without skipping? These are users who potentially already have a positive view of your brand. And with the right tools in place, records of their activity can be captured, segmented, and targeted within the ad server, either for creative versioning or deployment to a media-buying platform.
Viewability. Over the last several years, viewability has matured to become a currency for digital media transactions. But viewability can be used for more than just as a filter for served impression reports. It can also be used as a basis for segmenting first-party data. Suppose your creative strategy involves sequencing two different creatives. As it stands now, most ad servers or DSPs will perform sequencing based purely on a user’s exposure to a cookie. However, there’s no guarantee that the creative was ever truly viewed, and a user might get your follow-up message without ever having seen your first one. If you determine your targeting logic based on viewable impressions, however, you wouldn’t have that problem. The same can be said for any strategy, including frequency capping and retargeting.
Once you incorporate into your media strategy the idea of collecting and segmenting data generated from your digital advertising, you will find many more doors open for finding and connecting with new, more-likely to-convert, audiences.