Despite confusing messages from Data Management Platform start-ups, publishers need to understand the differences between contextual and audience targeting solutions. This knowledge will help them effectively match consumers to marketers at scale.

Let’s put match rates into context (pun Intended)

Some of the new DMP start-ups are presenting publishers with a playbook to perform a match test that will show how their platform delivers higher user match rates.

These DMP’s ask: “How many of your users are invisible for targeting in your DMP?” Then, they answer with:

• Create an “Everyone” segment in your current DMP (all users with 1+ pageviews)
• Run an adserver (i.e., Google Ad Manager/DFP) ad impression query against this “Everyone” segment
• Run a second ad impressions query without the “Everyone” segment (e.g., untargeted)
• Divide the “Everyone” segment by the untargeted segment

And, according to them, that’s your match rate of targetable users.

I’ve seen match rate used in many ways throughout my 20+ year marketing career. But this is the first time I’ve seen a company purposely confuse and conflate ad impressions with users, adservers with DMPs, and devise a testing methodology that mixes up contextual targeting with audience targeting. Oh, and it actually distracts from the unique benefits that DMPs can provide to publishers.

Some of these new DMP’s present contextual targeting solutions as if they are audience targeting solutions. The fact is that publishers need to better understand the differences in order to make informed decisions.

First there was contextual targeting…

It’s said that modern advertising began to take shape with newspapers and magazines in the 16th and 17th centuries. Most advertising relied on the associated content to aggregate an audience with certain attributes (demographics, interests, etc,). This is contextual targeting. The early days of digital advertising were remarkably similar. Whether advertising on dial-up services or the emergence of the web, advertising “banners” were contextually aligned with digital content.

Today, contextual targeting is still incredibly effective and compelling for marketers to access relevant groups of consumers in digital environments. Technologies have evolved and improved to provide everything from semantic content analysis to transmitting content categories and keywords into programmatic bidding exchanges.

One of its key benefits is that it does not have direct dependencies on cookies or other identifiers in order to target and deliver advertising. With increasing challenges persisting cookies/web identifiers and expanding privacy regulations, contextual targeting continues to be important for publishers. 

DMPs can play important roles supporting publishers and marketers in their contextual targeting initiatives. DMPs with strong inter-platform connectivity are excellent tools for providing analytics and insights on consumers that visit certain web pages or consume certain content.

For example, if a publisher would like a 360-degree view of the interests and attributes of consumers that read certain content, then a connected DMP can provide valuable insights by overlaying 1st, 2nd, and/or 3rd party data segments onto content reader segments. This can help publishers and their marketer partners better understand whether certain contextual media buys will provide a material volume of consumers that fit the marketer’s criteria.

However, there are also important shortcomings to contextual targeting as a standalone approach. Modern marketers are increasingly using their own 1st party data and select 3rd party data sources to help drive media planning, activation, and analytics. While pure contextual targeting is important to access consumers in certain channels, and in premium contexts at scale, there’s a significant need to more precisely access data-driven consumer audience segments for better efficiency and efficacy. Enter audience targeting.

Audience targeting and consumer addressability

About 10 years ago, the first generation of DMPs came on the scene to provide publishers with the ability to create 1st party audience segments, and then activate those audience segments through the publishers’ adserver. This afforded publishers and marketers with a set of audience targeting tools to augment their existing contextual targeting tools.

One way to think about the differences between contextual targeting and audience targeting is that contextual targeting reaches all consumers in the context of particular content, with no ability to distinguish one consumer from the next. However, audience targeting can more granularly reach individual consumers irrespective of context. Simply put, audience targeting can be thought of as “addressable” consumer targeting.

It’s important to note that in early audience targeting, the DMP-to-adserver integration technique of choice was to pass audience segment IDs as in-page key-values. Subsequently, that technique shifted to server-to-server integrations. A simple ID-sync (a.k.a. “pixel sync”) allowed each party to access their own Profile IDs (PIDs) for those users and easily translate that PID across platforms. So for example, the DMP accesses its PID  from a cookie and sees it as DMP_123. An adserver such as DFP accesses its PID from a cookie and sees it as DFP_456. By syncing the PIDs between platform partners, both parties would know that DMP_123 = DFP_456.

If 3rd party cookies are blocked — and only 1st party cookies or local storage are available to store Profile IDs (PIDs) — then it’s very challenging to sync PIDs for server-to-server audience segment transfers from DMPs to adservers. Therefore, as a result of Apple Safari ITP and Mozilla Firefox ETP blocking 3rd party cookies, publishers have reverted to the classic in-page key-value passing of segment IDs from DMPs to adservers. DMP start-ups feature the in-page key-value integration method with adservers.

There’s something these so-called “next gen” DMPs don’t tell publishers about their match test methodology. If you are currently using a pixel-sync server-to-server method with your current DMP, then it will only return addressable consumers (and media avails) that are associated with persistent PIDs stored in 3rd party cookies or Mobile Ad IDs.

Their supposed test is designed to convince publishers that they have a deficiency in their DMP’s capabilities. Instead it highlights that publishers should examine their DMP-to-adserver integration techniques and augment/replace server-to-server integrations with in-page key-value integrations. Established DMPs all feature in-page key-value integration options for publishers.

Knowledge is power

It’s troubling that DMP start-ups are purposely providing a match test methodology to publishers that purports to point out how a publisher’s current DMP is technically deficient. Really, they are taking advantage of a lack of knowledge around differences in adserver integration techniques.

They know that regardless of which DMP you use, the in-page key-value method for an “Everyone” segment will always return more users (and media avails) than the pixel-sync server-to-server method due to the absence of PIDs stored in 1st party cookies and local storage in the latter technique. When using the same in-page key-value integration technique, a full-featured DMP platform will return the same number of “Everyone” users and media impressions as the new breed of contextual targeters.

This match rate methodology focuses on the specific use case of transforming contextual targeting avails into audience targeting avails. Is this a more important metric than ROI? Not by a long shot. A modern day connected DMP will provide publishers with ad products and solutions to assist with audience development, content development and personalization, consumer marketing and subscriptions, and open up new revenue streams such as data licensing.