Intent Data, Customer Match & Google Ads - Is It Panic Time?

Mar 31, 2021 | Author Ed Marsh

Tl;dr - David Temkin worked the digital marketing world into a dither recently with his announcement that Google Ads would cease support for 3rd party cookies. That leaves companies wondering how to efficiently target active buyers - and the standard answer, first party data, isn't necessarily satisfactory. Contact level™ third party data provides an effective and actionable work-around.

We Really Needed Another Digital Marketing Acronym!

Like the proverbial hole in the head, right? Nevertheless, we got one - FLoC, or "Federated Learning of Cohorts.

This term got traction on the heels of Google's March 3rd announcement that, after lots of teasing, it would use technology that would "prevent individual tracking."

No doubt you've read and researched in the meantime. Here are some highlights you might have missed.

Vox/Recode said "It doesn’t mean that Google will stop collecting your data, and it doesn’t mean the company will stop using your data to target ads...What Google will stop doing is selling web ads targeted to individual users’ browsing habits, and its Chrome browser will no longer allow cookies that collect that data. Ad companies that rely on cookies will have to find another way to target users...In other words, while the announcement will have huge implications for the digital ad industry, it probably won’t for Google itself."

Business Insider noted "The plans throw a wrench into advertising-industry efforts to create cookieless "universal identifiers" that use more privacy-conscious methods of user tracking, such as encrypted email addresses or login information."

Axios emphasized that "But many ad tech companies are building work-around solutions so that advertisers can still target people on the web using other types of individual identifier technologies."

Clearly there's angst. But we've known this is coming.

What Does The End Of Cookies Mean? The Rise of First Party Data!

The WSJ cited an analyst who said "'If you can only target based on first-party data, then the people with the most first-party data do best'", and CNBC offered insights from a digital agency founder who said "'Following last year’s announcement to phase out third-party cookies, many of our clients have been moving swiftly to build their data infrastructures and to invest in their CRM, to better leverage their first-party data'".

And Google's David Temkin devoted about 1/3 of his March 3rd post to the continued relevance of first party data.

Nobody's really clear (it seems not even Google) on exactly how the FLoC will work - in either meaning of the term; its function modality or its effectiveness.

It seems, though, safe to bet on first party data.

But.....what if you don't have enough first party data in your marketing data stack? Or what if you're working with a martech stack that limits your ability to unify data from multiple sources into a Golden Record? (That's an example of the value of a CDP on top of the typical CRM and marketing automation.)

Obviously that creates some challenges and not just a bit of disruption.

There's no doubt it substantially changes many paid ads and retargeting strategies. In fact it seems to blow some of them up.

Fortunately for B2B companies the disruption is probably not as profound as for many B2C brands. Just because it's not as traumatic, though, doesn't mean it's minor. This is a problem many companies have to solve.

The good news is that this is an important use case of intent data for marketing, and there's a little-understood aspect of contact level third party intent data that helps to plug this gap.

Supplementing First Party With Third Party Contact Level Intent Data

Most people associate third-party intent data with domain level paid ads in support of account-based marketing programs. In fact, that's the primary function of some prominent ABM platforms - to provide tailored paid ad interfaces to show ads based on recent account level activity to platform users with desirable account affiliation and job role/seniority .

That doesn't solve for the specific insights that drive effectiveness of many demand gen paid ad programs - namely specific, relevant behavior by individuals.

To understand the 3rd party data workaround, let's quickly back up to understand data collection and detail. You'll find more here and here, but here's the quick version. Most 3rd party data models (e.g. bidstream, publishing coop) draw inferences from activity (being shown an ad as reported by a DSP, or seeing an increase in aggregate account level - as determined by IP address resolution - activity with some "topic" relevant content) and reports that the account is potentially in the market based on that activity.

Contact level data, in contrast, observes the public actions of individuals (what they're doing for the whole world to see) and then resolves the public profile associated with those actions to identify the person taking them. This provides specific contact insight (account, job title, location) and granular detail (key term vs. topic, specific competitor, stage in buying journey, composition of buying team, problem to solve, outcome to achieve, etc.)

From there it's a relatively easy step to append an, or multiple, email address(es).

With those addresses - obviously not for email marketing, but perfect for custom audience and customer match - and the other inferences that are possible - role in buying team, stage in buying journey, specific competitor engagement, problem to solve, etc. - then it's feasible to build very carefully segmented customer match campaigns.

In other words, even though a 3rd party cookie might not log the activity on which you've built campaigns and retargeting, contact level observations of public activity can provide very similar insights. Association of those contacts with email addresses drives high customer match rates (ensuring efficiency of campaigns) and the detailed granular insights support segmentation.

Voila - you can amplify your customer match campaigns on Google's properties and other platforms which support similar custom audience match capability regardless of the status of third party cookies!

The Nuts & Bolts - How do you use contact level third party data for Google customer match campaigns?

Contact level intent data can be delivered in numerous ways, including ingestion by a CDP/data lake, integration with marketing automation and CRM, and provision of .csv files. Data can be delivered in separate, segmented streams (e.g. customers and ABM accounts into marketing automation, demand gen data into files for analysis.) So you can use intent data for the full panoply of intent data applications across the customer lifecycle, integrated accordingly, with a couple tailored streams of data for your paid ads programs.

One would be for ABM and target accounts - to run "air cover" awareness and conversion ads on those.

Another would be for broader demand gen programs - looking for activity among the right contacts (filtered by seniority and function) from companies that match your ICP (filtered by geography, industry, size, technographics - and other factors via enrichment data.) This group generally has significant enough volume to allow for tight segmentation of the campaign message.

You might manually manipulate and load these lists, or manage them through various ad platforms (manual upload, integration with the data source, or via CDP) or even share the data with an outside agency that manages your ads programs.

Keep in mind one important point - this approach is not possible with typical, account level intent data. The key to make it work is to know which person took the action.



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