How to Use Your PPC Campaigns as A Prospecting Tool for SEO

When launching a digital brand, PPC can be a great way to immediately break into a new market and start generating website traffic. But with rising CPCs and unmitigated click fraud, PPC can also get really expensive quickly. Even the most skilled digital marketers can struggle to run profitable Google Ads campaigns. For this reason, […]

The post How to Use Your PPC Campaigns as A Prospecting Tool for SEO appeared first on CXL.

When launching a digital brand, PPC can be a great way to immediately break into a new market and start generating website traffic. But with rising CPCs and unmitigated click fraud, PPC can also get really expensive quickly. Even the most skilled digital marketers can struggle to run profitable Google Ads campaigns.

For this reason, getting more out of your PPC spend is not just about properly optimizing; it’s about using all of that PPC data to shape a smarter SEO strategy. In this article, I’ll break down how to use your Google Ads campaigns as prospecting tools for SEO. 

Why PPC is an effective short term strategy, but SEO is the long game.

With PPC, it’s critical to remember that you’re in essence “renting” visits to your website in the short-term. 

Brands that rely heavily on revenue from paid than organic search are only one crisis away from having their business turn upside down. The moment you stop paying for search ads is the moment your traffic dries up.

You can certainly jumpstart traffic with paid search, but if you’re on a budget this strategy can be difficult to maintain.

Unlike PPC, organic SEO allows brands to earn that same traffic without paying, and if done correctly, continue to benefit over time. That doesn’t mean Google Ads can’t be a valuable part of your marketing strategy, but putting all your eggs in one basket can come back to haunt you.

Depending on the competition and the rate at which you build your site authority, seeing the rewards of SEO can take months. That’s why PPC is one of my favorite ways to quickly start testing out digital strategy and sales processes. 

To prospect well, you need to properly optimize your Google Ads campaign

Before we look at how PPC campaigns can help with SEO, it’s critical to ensure you’re already optimizing your PPC campaigns first. 

There is a lot that goes into optimizing Google Ads campaigns, here are some 

1) Take an iterative approach

Most likely, your first campaign will not be profitable. But an unprofitable campaign can still give you loads of information about your set of keyword targets, bid amounts, ad formats, and the details you need to improve the effectiveness of your campaigns .

For example, if your ad is being triggered for irrelevant search terms or generating the wrong types of clicks, add those terms to your negative keyword list. If your ads are earning impressions but not clicks, revise your ad copy and work towards making them more relevant. 

In terms of the ideal length of a PPC campaign, I recommend you have enough monthly budget to acquire at least a few hundred clicks (you need enough conversions to calculate a statistically significant conversion rate.) In my experience three months of PPC budget is enough time to iterate your optimizations and prospect for SEO. 

Make small adjustments, monitor your results, then implement new changes accordingly. 

2) Use single keyword ad groups

Many PPC managers agree that one of the best optimization practices is single keyword ad groups. Yes, it takes more work for your marketing teams, but pages with the most relevant ad copy will generally improve click-through-rate and conversions. 

Adwords Account.
Single Keyword AdGroup Structure (Image source: LinkGraph)

I find the SKAG campaign structure to be incredibly helpful, and it uses only one keyword per ad group (rather than one ad group targeting multiple keywords). SKAGs make it easier to determine which keywords will perform well or not for SEO because:

  • The SKAGs with the highest CTRs and conversions will likely be winning SEO keywords
  • SKAGs help uncover relevant search terms that are worth targeting organically
  • SKAGs make split testing easier (the next optimization step) and can help you identify the most effective headlines and descriptions that you can utilize on SEO-driven pages.

3) Write highly-targeted ad copy and utilize A/B testing

If you’re structuring your campaigns properly and using SKAGs, you can create unique text ads for each target keyword in an ad group. With the Google Ads built-in A/B testing feature, you can also test out different headlines or descriptions to see which performs better. 

With the “Optimize,” ad rotation setting, ad served get weighted toward the ad that statistically appears to perform better. To run a proper A/B test, you need to have a clearly defined variant that you are testing, as well as two sets of ad copy that are unique enough to produce different results. In the below example, the description is the variant being tested, and the data shows that the B variant performed better (despite far fewer impressions).

Ad tests.
Example of Google Ads A/B testing feature

One of the most common pitfalls of A/B testing is that advertisers test out too many variants making it difficult to determine why one ad performed better over another. For this reason, it’s important to only test one variant at a time. 

4) Use a Google Ads bid simulator to determine the price you’re willing to pay

A bid strategy will ultimately play a big part of paying less for better clicks in PPC campaigns. There are benefits and drawbacks to manual and automatic bidding, but both require advertisers to determine appropriate keyword bids for their highest-value keywords—marketing effectively is hard work!

The Google Ads bid simulator is a great tool for finding this magic number. Many digital marketers often set their max bids too high and end up overpaying for clicks.

Google Ads bid simulator.
Google Ads bid simulator for the keyword “kitchen curtains.” 

The degree of the curve can help you determine an appropriate price to set your max bid amount. Where the curve flattens off shows where increasing your bid will only result in minimal traffic increases. 

In the above example, if you increase CPC from $1.41 to $3.00, the marginal cost-per-click for the incremental traffic is over two times more expensive for only 7% more impressions. I would bid $1.07 – $1.41 here. 

5) Optimize your landing pages for conversion

The work of PPC doesn’t end after the click. Although some brands run PPC campaigns just for brand awareness, performance-based campaigns are easier to measure, and in my opinion, conversions should be the ultimate goal of paying for your clicks. This means your  PPC landing pages need to be designed to be efficient mouse traps. Check out CXL’s guide on how to build high converting landing pages to help ensure your landing pages are optmized. 

How to use PPC Campaigns to Prospect your SEO strategy 

Once you’ve optimized your Google Ads campaigns and start buying clicks, you will begin collecting loads of data not only about whether your PPC campaign structure is effective, but whether or not you can redeploy it in SEO. 

PPC campaigns can help digital marketers simultaneously test out three things: 1) keyword targeting, 2) traffic quality, and 3) their website’s conversion funnel.

1) Use PPC to identify the high-value keywords for which your website can realistically rank 

One of the most advantageous elements of a PPC campaign is it helps digital marketers test out certain keywords before designing an SEO strategy around ranking organically for them. 

The Search Terms Report is the best place to go to get information about your keyword targeting. 

Example of a Google Ads Search Terms Report

It’s important to remember that your Google Ads are not only triggered for the search phrases or words that you add to your campaign, even if you use “Exact Match.” 

So be sure to review your search terms report to see the various phrases your ads are being triggered for and utilize that data. 

There will likely be many search terms that are generating clicks that weren’t originally on your radar. This report will also give you a broader sense of the long-tail keyword variants that present SEO opportunities, because those keywords are often less competitive to rank for (but still have high search intent). You can then create new landing pages or blog posts that are optimized for those long-tail phrases.

The search terms that generate quality clicks help establish that those keywords are likely worth targeting in SEO. If you find search terms in this report that are not relevant to your products or services but your ads are showing up, there is likely something off with your keyword targeting. 

There are of course many possibilities for this, but the most common errors are that your keywords are either too broad or they are multi-intent keywords that bring traffic that is not necessarily in the sales funnel. To correct this, add those keywords with less relevance to a negative keyword list.

The cost-per-conversion of your Google Ads can also help you understand the potential long-term economic value of ranking organically for certain keywords. 

Google Ads cost per conversion metrics.
Google Ads cost-per-conversion metrics

If it would cost hundreds to thousands of dollars to generate clicks in a PPC campaign, but you can find a way to get that same traffic to perpetuity from organic rankings, you can make significant headway in improving the overall ROI of your marketing spend. 

SEO has a wonderful way of drastically lowering cost-per-acquisition over time. Once you understand which search phrases have the potential to bring clicks and customers, you can optimize your website to rank for those same keywords and get the same traffic (but this time, for free.)

2) Understand traffic quality and the economic value of clicks

The second major benefit of PPC is that you can use their campaigns to prospect the economic quality of the traffic that comes with specific keywords. 

What makes traffic have economic value? If it enters your conversion funnel. 

Naturally, Google charges advertisers more money when the data shows that the keyword is more likely to result in conversions for your business. But any well-seasoned digital marketer will tell you that high CPCs don’t always directly translate into quality traffic. 

If a user clicks on your search ad and doesn’t enter the conversion funnel on your website, you’ve essentially paid for nothing. The consequences can be deadly: Low-quality traffic (whether from click fraud or improper keyword targeting), higher cost-per-conversions, lower Quality Scores, and higher CPCs in the long run. So the best place to understand the traffic quality of those keywords targets is by using Google Analytics

Another essential step in optimized PPC campaigns is setting up proper tracking (this is especially important for B2Bs where marketing attribution is already pretty tricky.) If you’re not doing so already, it’s critical to  link your Google Ads account with Google Analytics so you see exactly what your site’s visitors are doing once they arrive on your website via a paid click.

Here are some of the Google Analytics metrics that provide insight into the quality of your PPC clicks. Remember, bad keyword targeting and irrelevant ad messaging is bound to return low-quality clicks (but that’s on you). 

  • Geographic Location: Traffic from certain geographic areas can mean site visitors with smaller budgets or less buying power. To understand buying power even more, you can use geo-targeting to segment audiences in their PPC campaigns by region and compare conversion rates and economic value. When it comes to applying this to your SEO strategy, although some keywords may have high global search volume, it doesn’t guarantee the traffic will have strong buying intent.
  • Desktop vs. Mobile: In general, mobile has a lower conversion rate for most products and brings wildly different traffic than desktop. A poorly designed mobile version of your site may prevent qualified users from entering your conversion funnel, but if a lot of your PPC clicks are coming from desktop but are not converting, it could be a sign of low-quality traffic with less buying intent. 
Desktop vs mobile.
Desktop versus mobile conversion rates tracked in Google Analytics. (Image Source: Hallam)
  • Exit Rate: This metric represents the rate at which people leave your website on specific pages. If your exit rate is high on those pages that have lead capture forms, pricing information, or checkout pages, it’s likely that traffic is not ready to convert or make a purchase and should be categorized as low-quality.
Exit rate metrics on Google Analytics
Exit rate metrics on Google Analytics

Low-quality traffic can destroy your PPC campaigns, with organic SEO there’s more room for error. Even if it is easy to rank for a specific keyword organically, Google doesn’t consider site traffic in its ranking algorithm. Although that low-quality traffic might have brand awareness value, the SEO value is little to none.

3) Test whether your landing pages are well-designed to convert

PPC campaigns also provide the opportunity to test your website’s conversion funnel. With Google Ads conversion tracking, you can get a great sense of whether your landing pages are pulling their weight and guiding users toward the desired conversion action. 

To set up conversion tracking, you need to select which conversion actions you want to track. For ecommerce companies you’ll likely want to track when a user adds items to their shopping cart. For B2B or B2C brand (where the next step in the sales funnel isn’t necessarily a purchase) you may want to track actions like lead form submissions, downloads, or demo bookings.

Example of conversion actions that can be tracked in a Google Ads campaign.
Example of conversion actions that can be tracked in a Google Ads campaign

If certain conversion actions are significantly higher with your PPC campaigns, your landing pages that rank well will likely benefit from harnessing similar CTAs, lead capture forms, or design elements.

Traditionally, specialized PPC landing pages look much different than SEO-driven landing pages. With PPC, landing pages usually present users with a more obnoxious call to action, limit the content depth on the page, or sometimes even remove the nav bar to prevent users from browsing through the website. 

Illustration of a landing page designed for SEO and one designed for PPC (Image Source: TempleToaster)

These design elements can often conflict with what it takes to get a landing page to rank organically (e.g. In-depth content, breadcrumbs, external links, information architecture, rich media, etc.) 

Use your PPC campaigns to test out different landing page design elements or conversion-optimized practices and identify what works best. Some ideas include:

  • Number and placement of of CTAs;
  • Design elements like fonts, colors, size of buttons, etc;
  • Conversion-optimized features like sticky bars;
  • Removal of navigation menu.

You can also send PPC clicks to landing pages that already have strong keyword rankings, or you know have ranking potential, to test whether your conversion journey will translate for users who arrive to your website organically.


PPC campaigns can be a great way to generate clicks in the short term, but are also incredibly helpful in improving your overall SEO strategy as well.

Though coming at a cost, PPC campaigns provide incredible amounts of valuable data about keyword targets, traffic, and whether your website is or isn’t conversion optimized. 

Here are the key takeaways to execute a SEO prospecting process with your PPC campaigns.

  • Use your PPC campaigns to identify the highest value keywords for your SEO strategy— keywords that get impressions, clicks, and bring quality traffic to your website.
  • Prospect traffic quality by linking your Google Ads campaigns with your Google Analytics account. Look at the data to help you determine buying intent, such as geographic location, traffic by device type, and exit rate.
  • Use Google Ads conversion traffic to test and iterate on your website’s conversion funnel. Incorporate the conversion-optimized design elements that worked in your PPC campaigns to your SEO-driven pages. Or, send PPC traffic to your SEO-driven pages to test the conversion journey.

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FLoC: Google’s Plan to Kill Off Third-Party Cookies

Third-party cookies are the new Flash. Safari and Firefox have already started to wean advertisers from them. Now, reluctantly, Google is, too. Google plans to end Chrome’s support of third-party cookies by 2022, and they created a Privacy Sandbox to test new ideas and solicit feedback. Decisions that affect Chrome—with a nearly two-thirds market share—are […]

The post FLoC: Google’s Plan to Kill Off Third-Party Cookies appeared first on CXL.

Third-party cookies are the new Flash. Safari and Firefox have already started to wean advertisers from them. Now, reluctantly, Google is, too.

Google plans to end Chrome’s support of third-party cookies by 2022, and they created a Privacy Sandbox to test new ideas and solicit feedback. Decisions that affect Chrome—with a nearly two-thirds market share—are decisions that affect the Internet, especially paid advertising.

Google code.

But it’s still a time crunch for Google to figure out how to defend their ad empire without access to the user-level data that’s made it so lucrative. The solution has to balance four variables:

  1. Revenue for publishers that sell ad space;
  2. Targeting capability for ad networks;
  3. Return on ad spend for ad buyers;
  4. Privacy for users who see ads.

The first three go hand-and-hand—if advertisers can measure and get a good return on ad spend, they’ll keep buying ads. Ad platforms will keep selling inventory. Publishers will get their ad revenue.

But eliminating third-party cookies won’t improve ad targeting. It will get worse. The question is: Can Google develop a new system to keep ad buyers buying if users are anonymous?

Third-party cookies don’t affect everything

Third-party cookies are the backbone of display advertising, but they’re not the only way that websites gather user data.

Nothing is changing, for example, to first-party cookies. First-party cookies are set by a website when you visit it. Users can block first-party cookies, but doing so often impacts the user experience (e.g., clearing items you left in your cart, forcing you to log in again).

Third-party cookies are set by someone else (e.g., an ad platform) and are accessible anywhere else their code loads. They aggregate far more of your clicks across the Internet and power the hyper-relevant ads you see (e.g., an ad for a product you left in your cart on another site).

The incentives to block third-party cookies are high—the only real consequence is that you see less relevant ads. But without third-party cookies, what’s a display network to do?

FLoC tries to solve the simpler problem—interest-based targeting

interest-based targeting

Ad networks have three ways to determine which ads to show:

  1. First-party and contextual information (e.g., “put this ad on web pages about motorcycles”);
  2. General information about the interests of the person who is going to see the ad (e.g., “show this ad to Classical Music Lovers”); 
  3. Specific previous actions the person has taken (e.g., “offer a discount on some shoes that you left in a shopping cart”).

Plenty of sites aren’t making the most of their first-party cookies; fixing that should be a priority. The third category is addressed through TURTLEDOVE and related programs (more later).

Federated Learning of Cohorts, or FLoC, is all about number two. It’s slated for a trial in March 2021 with the release of Chrome 89.

How FLoC works

The idea behind FLoC is to hide individuals “in the crowd.” The technological breakthrough, announced in 2017, is the “federated” component—the ability to train a machine learning model without a centralized repository of data:

It works like this: your device downloads the current model, improves it by learning from data on your phone, and then summarizes the changes as a small focused update. Only this update to the model is sent to the cloud, using encrypted communication, where it is immediately averaged with other user updates to improve the shared model. All the training data remains on your device, and no individual updates are stored in the cloud.

Targeting based on personalization.
Your phone personalizes the model locally, based on your usage (A). Many users’ updates are aggregated (B) to form a consensus change (C) to the shared model, after which the procedure is repeated. (Image source)

The algorithm analyzes data from your browsing history—the sites you visit and the content of those sites. Ironically, for a company that runs the world’s most sophisticated search engine, the assessment of site content for FLoCs is elementary:

Our first approach involves applying a SimHash algorithm to the domains of the sites visited by the user in order to cluster users that visit similar sites together. Other ideas include adding other features, such as the full path of the URL or categories of pages provided by an on-device classifier.

Google and Facebook have been employing similar mechanisms in their bidding algorithms with great success,” says Amanda Evans, President of Closed Loop, “so there is no reason why FLoC won’t work from a performance perspective. But adoption of this practice outside of Google will require substantial investment and certainly favors larger ad platforms with large amounts of resources and data.

A FLoC ID protects users based on a principle of k anonymity. At k number of users, individual identities are unknowable. (FLoC IDs use non-descriptive names, like “43A7,” to prevent the ID itself from passing information about users.)

The value for k is still unresolved. Tests by Google—including the primary test they cite to demonstrate FLoC’s effectiveness compared to random cohorts (“a 350% improvement in recall and 70% improvement in precision”)—used a k value of 5,000.

“Whether or not FLoC works,” says Allison Schiff, who’s written extensively about FLoC for AdExchanger, “it will not be a replacement for third-party cookies. Nearly nothing can be, because cookies, as flawed as they are, have so many different functions. So FLoC might be just one of multiple alternatives for the targeting functionality that cookies are used for today.”

Word clusters based on FLoC k values.
Word clusters based on Google tests of FLoC at various k values.

The unsurprising logic is that a lower k value improves targeting at the expense of anonymity; a higher k value improves anonymity at the expense of targeting. This is the tension.

“If a FLoC is too small, that presents both data privacy issues as well as potential performance issues,” continues Evans. “While machine learning has improved, we continue to see flaws in machine-learning performance for extremely niche advertisers or advertisers with small data sets.”

Is anonymity even enough?

Anonymity at the user level doesn’t resolve all concerns. As a critique from the Electronic Frontier Foundation notes:

A flock name would essentially be a behavioral credit score: a tattoo on your digital forehead that gives a succinct summary of who you are, what you like, where you go, what you buy, and with whom you associate. The flock names will likely be inscrutable to users, but could reveal incredibly sensitive information to third parties.

Princeton computer science professor Arvind Narayanan agrees:

If an ad uses deeply personal information to appeal to emotional vulnerabilities or exploits psychological tendencies to generate a purchase, then that is a form of privacy violation—regardless of the technical details.

Google has discussed ways to exclude “sensitive” data from flock assignments, but, as they concede, there is no consensus as to what qualifies as “sensitive.” A FLoC associated with pregnancy is one thing for a 30-something and something else for a high schooler. Anonymity and privacy aren’t one in the same.

You can opt out, and Chrome will send a random FLoC instead of an accurate one. (The algorithm might also add “noise” by occasionally sending a random FLoC.)

Sites can also opt out of inclusion in FLoCs. In both cases, however, the default is “Allow.” As Firefox has argued, “defaults matter.” Before their Enhanced Tracking Protection was the default, only 20% of users had enabled it.

There are other risks for abuse, especially for sites that have access to personally identifiable information:

Sites that know a person’s PII (e.g., when people sign in using their email address) could record and reveal their cohort. This means that information about an individual’s interests may eventually become public.

And none of this enables marketers to target existing audiences, like cart abandoners. The solution for that is more complex—and contentious.

How do you retarget an anonymous user?

FLoC helps companies target users based on interests, even if they’ve never interacted with a company’s website. Targeting users based on past actions is a whole other process.

These user groups could come from a “user list,” “remarketing list,” “custom audience,” or “behavioral market segment.” The challenge, for advertisers, is how to target individual users without piercing the veil of anonymity.

The current solution is a patchwork of proposals from Google (TURTLEDOVE, DOVEKEY) and ad vendors (SPARROW, PARRROT, TERN). The core innovation is to store the data that builds these lists in the user’s browser or with an independent third-party—not on the ad network.

TURTLEDOVE: The foundation of a new system

(Image source)

TURTLEDOVE stands for “Two Uncorrelated Requests, Then Locally-Executed Decision On Victory.”

The “two uncorrelated requests” are from the browser to the ad network that places the ads:

  1. A contextual ad request based on the URL (e.g., and any first-party targeting information (i.e. user data from past browsing on;
  2. A separate request—oblivious to the current page or user data—based on an advertiser-identified interest previously pushed to the browser.

The second request could happen before a user lands on the page where the ad is served, with the browser caching the ad information until requested. That temporal gap protects users against “timing attacks”—an ad network seeing both requests come in at the same time and using that timing to match contextual data with interest data.

In the initial version of TURTLEDOVE, the user’s browser then holds the auction (based on decision logic delivered with the two requests). As the auction takes place on your browser and your machine, the two data sources can be combined to improve bidding without exposing your information to ad networks.

(That combination gives ad buyers control over where their ads show up—so an airline isn’t bidding for space on a news article about a plane crash.)

You see the ad with the highest bid.

Here’s what a step-by-step example of the process might look like:

  1. You visit and browse sofas. pushes your interest information (i.e. sectional-sofas) to your browser via a new API. It also gives an ad network, AdMatica, permission to view that interest.
  2. At some regular interval, the browser requests interest-group ads from AdMatica. AdMatica sends the sectional-sofa ads, including the logic needed to hold an on-device auction. The browser caches the information.
  3. Sometime later, you visit, which uses AdMatica to serve ads. The browser requests a contextual ad from Admatica. Admatica returns the contextual ad as well as a request to hold an on-device auction if an interest-based ad also exists.
  4. The browser finds the cached interest-based ad and holds an auction between it and the contextual ad based on the logic sent from the ad platform.
  5. The browser loads the ad with the highest bid.

A test by RTB House on product-specific ads suggests that this method can work well when interest groups include 30 users. Their experiment estimated that 90% of their advertisers would retain at least 74% of their current click-through-rate levels, with most retaining far more:

Still, it’s a big change. Presently, auctions take place on ad network servers—with all the accumulated data about user behavior and direct access to platforms’ bidding algorithms.

Moving the auction to the browser would require ad networks to serve any algorithm experiments alongside the two uncorrelated requests. The networks would learn about the success of those experiments only from aggregate reporting of results (with added noise).

That’s one reason that ad networks didn’t like the initial TURTLEDOVE proposal. Alternatives, such as SPARROW from Criteo, argued for moving the ad auction from the browser to a trusted third-party server—”The Gatekeeper.” Google agreed.

SPARROW and “The Gatekeeper”

SPARROW moves the auction from user devices to a third-party server. The shift makes it easier for ad networks to A/B test ads and avoids sending their proprietary auction algorithms back and forth millions of times per day. (It also skirts other issues with on-device auctions, like draining phone batteries or using up cell data.)

But it unwinds a central tenet of TURTLEDOVE—that the sensitive, de-anonymizing processing occurs only on your device. Whether SPARROW meets data privacy goals depends on how much you trust a third-party server to be, in fact, an independent third-party. (And, yes, they could get hacked.)

As the SPARROW proposal details:

Gatekeepers must remain independent from other parties in the ad tech ecosystem. In particular, DSPs cannot run as Gatekeepers for their own ad services.

This independence could be ensured by a legally binding agreement and appropriate audit procedures. An industry consortium, or regulators, could ensure that gatekeepers fulfil their duties and could certify new Gatekeepers. Ultimately, in case of contractual breach, browser vendors would be the ones blacklisting Gatekeepers since interest-based display opportunities are sent out by browsers to Gatekeepers.

Gatekeepers provide a service to advertisers, running their models to compute bids, and should be paid by advertisers.

Google’s DOVEKEY is a twist on TURTLEDOVE plus SPARROW. It turns The Gatekeeper—the third-party server—from the processor of the ad logic to a simple lookup table that “will cache the results of existing control and bidding logic. ”

Google’s DOVEKEY.
With DOVEKEY, the third-party server has a reduced role: “a trusted Key-Value (KV) server which receives a Key (a contextual signal plus an interest group) and returns a Value (a bid).” (Image source)

The proposal weakens anonymity, suggesting that anonymity from the advertiser is the only anonymity that matters:

Because the server is trusted, there is no k-anonymity constraint on this request. The browser needs to trust that the server’s return value for each key will be based only on that key and the hostname, and that the server does no event-level logging and has no other side effects based on these requests.

The trial rollout of the system, called FLEDGE, is happening in the first half of 2021, with ad networks serving as their own Gatekeeper (a temporary “bring your own server” model).

The changes to how ads are served has knock-on effects, especially when it comes to reporting.

How these new tracking proposals affect reporting

(Image source)

The post-third-party-cookie conversion reporting solution is called the Conversion Measurement API.

It works by tagging ads with metadata (e.g., click ID, campaign ID, URL of expected conversion). If a user clicks the ad, that metadata—up to 64 bits of information—is stored in their browser.

Adtech platform chart.
How conversion tracking for ads might work without third-party cookies. (Image source)

If, then or later, they convert, their browser pairs the conversion event data to the ad click data. (There is no current solution for view-through conversions.)

The conversion data is only 3 bits—enough to define the type of conversion that took place, not identify the user who converted. (Chrome even suggests adding noise by sending a random 3-bit value 5% of the time.)

The amount of data sent with the ad impression is controversial:

Apple’s proposal allows marketers to store just 6 bits of information in a “campaign ID,” that is, a number between 1 and 64. This is enough to differentiate between ads for different products, or between campaigns using different media.

On the other hand, Google’s ID field can contain 64 bits of information — a number between 1 and 18 quintillion. This will allow advertisers to attach a unique ID to each and every ad impression they serve, and, potentially, to connect ad conversions with individual users. If a user interacts with multiple ads from the same advertiser around the web, these IDs can help the advertiser build a profile of the user’s browsing habits.

Click data.
Far more data is passed based on ad clicks than conversions to protect individual identities.

The browser then schedules a conversion report to be sent—days or weeks(!) later to prevent timing attacks that can de-anonymize data. 

So, days or weeks after an ad campaign is running, you may be able to see which ads generated the most conversions (and the types of conversions they generated). But you won’t be able to dig into which individual users converted from which ads.

There are other practical challenges to the post-cookie era—like ensuring that those ad clickers and converters are, in fact, real people.

Trust tokens

How do you know if the clicks come from real humans? Historically, doing so required “fingerprinting”—all sorts of de-anonymizing methods (e.g., gathering data about your device, language preferences, user agent, etc.) that browsers are trying to eliminate.

Google’s proposed solution is a “trust token.” Trust tokens are “non-personalized” and “indistinguishable from one another,” which lets them be shared without undermining privacy.

Who gets to give them out? Other websites with which you’ve established yourself:

You might have shopping history with an ecommerce site, checkins on a location platform, or account history at a bank. Issuers might also look at other factors such as how long you’ve had an account, or other interactions (such as CAPTCHAs or form submission) that increase the issuer’s trust in the likelihood that you’re a real human.

While FLoC and DOVEKEY have generated criticism, the trust token concept has been universally welcomed, and Google’s ownership of most of the CAPTCHA market should help with its rollout.


Cohorts are “where the future is headed, at some level, in terms of targeting,” Google’s Chetna Bindra told AdExchanger.

If big changes are coming, what should you do now? Google recommends that you “implement sitewide tagging with the global site tag or Google Tag Manager in order to minimize disruptions during this time.”

“Get email addresses,” says Schiff. “That stuff is consented gold!”

Beyond that, encourages Evans, focus on first-party data:

Act now instead of “waiting for the industry to figure all this out.” First-party data will be a cornerstone of digital advertising targeting and measurement, so advertisers should start collecting first-party data; developing systems and processes to easily pull and segment the data; and push it back into the ad platforms.

Advertisers who have not yet implemented Google Offline Conversion Tracking and Facebook’s Conversions API should prepare to do so now.

As Schiff concurs, this is “an opportunity for publishers that have become disintermediated from their visitors due to too many middlemen to try and take control of their destiny again.”

Featured Image Source

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