testTag: targeting without cookies

Momentum Behind Ad-Tech Innovation & Privacy Goes Beyond Cookies

Posted on June 28, 2021 by - Privacy

Google’s timeline shift for its retirement of support for third-party cookies in Chrome, announced June 24, 2021, was met by the programmatic advertising industry with relief. And with good reason: Most of the industry was simply not ready for the transition to happen in the first quarter of 2022.

Under the new timeline, Chrome’s phase-out of support for third-party cookies will begin in the middle of 2023, exactly two years from now.  With that longer horizon, it might be tempting to power down efforts to develop alternative targeting and measurement technologies, and address it in the future, maybe in mid-2022.

To do so, though, would be to waste a powerful opportunity.

Over the last six months, the industry has seen an explosion in innovation in preparation for the change and has developed tremendous momentum.  The new class of emerging solutions has been inspired by the threatened imminent demise of cookie-based targeting, but their value propositions are aligned with the more macro trend of rising consumer privacy standards.  

Indeed, Chrome’s third-party cookie retirement is less of a one-off catalyst for more privacy-friendly targeting than it is a punctuation of a multi-year trend.  Regulations like GDPR and CCPA and commercial moves by Apple and Mozilla have all set the stage for Google Chrome’s final act in support of third-party cookies.

The previous short-term timeline was a wake-up call for the programmatic advertising ecosystem.

Advertisers faced a probable disruption to their programmatic campaigns & ROI.  Agencies faced a discontinuity in their digital services and revenue streams.  Publishers faced a precipitous decline in the value of their ad inventory.  And advertising technology companies faced what for many would be an existential reckoning.

Faced with this imminent disruption, the whole programmatic ecosystem has been investing behind a wave of innovation that includes a host of new opt-in identity solutions, publisher-led targeting solutions, and new privacy-compliant targeting techniques built around signals that are not user-based.  That momentum has been breathtaking to behold and has inspired a renaissance for the advertising technology industry that has included renewed investor interest, appreciating valuations, IPOs, and strategic M&A.

It is ID-free™ because it enables targeting without knowing anything at all about the user seeing the ad. Custom AI, because it uses a bespoke AI model, powered by a brand’s own first-party customer data.

Best of all, our ID-free Custom AI performs on-par with our best cookie-based targeting solutions, and outperforms contextual, the next-best alternative to cookie-based targeting.

Our ID-free targeting is creating a new category of ad targeting — a third way. It starts with an understanding of the common digital journeys that an individual brand’s customers take to conversion, and targets moments in that journey, without tracking or relying on cookies.

While the imperative to replace third-party cookies has become less immediate, we as an industry should ride the cresting wave of innovation to address the macro condition of rising consumer privacy standards and capitalize on the powerful momentum we have built.  We now have more time to test new technologies, to learn and adapt them, and to deploy them well ahead of the catalyst.  

Adopting new and innovative solutions now, including Dstillery’s ID-free®, can provide real value-add today, demonstrate the industry’s respect for consumer privacy, and de-risk the retirement of third-party cookies, whenever it occurs.  

Questions? Contact us today.

*Patent Pending

Why post-cookie targeting solutions can not retread old ideas

Posted on June 9, 2021 by - Audiences

It has been more than a year since Google announced that it would stop supporting third-party cookies in its Chrome browser in early 2022. In that time, the advertising technology industry has been developing post-cookie plans and products to fill the white space in targeting that cookie retirement will expose.

The trouble is that many of the proposed solutions are really simple retreads of old ideas. This is not a moment for retrograde thinking — it is a moment for the industry to innovate.

Path of Least Resistance

For advertisers, the path of least resistance may be to fall back on targeting technologies available today that are not dependent upon cookies, or to rely on one or more yet-to-emerge ID spaces to replace cookies.

As far as currently available solutions, proposals usually involve one or more of the following strategies:

  1. Double down on first-party data to target existing customers who have opted in to receiving an advertiser’s messages
  2. Return to classic contextual targeting, using words, images, audio and video on page to show ads in environments that are brand-safe and relevant
  3. Dedicate even more budget to the walled gardens that benefit from a large base of opted-in users with consent

Surely, brands and their agencies will employ all of these strategies to some degree, but they come at a cost. Plainly stated, this is why advertisers do not concentrate their budgets against them today.

First-party Data

When brands focus messaging against first-party data, they sacrifice new customer growth opportunities. Needless to say, abandoning any method of new customer acquisition in a crowded advertising market is not a sound business strategy. Yes, brands can try to find new prospects who look exactly like current customers, but without cookies and retargeting, that will become more difficult.

Contextual Targeting

By allocating more budget to classic contextual, brand marketers give up precision, which deteriorates return on ad spend. Most advertisers are aware that contextual goes beyond basic keyword targeting. But, it’s harder to create a perfect match between the ad and customer interest when using page signals alone.

Walled Gardens

If brands increase reliance on walled gardens, they cede control of customer relationships to these massive platforms. These behemoths share woefully little data or insight with the brand and agency, forcing marketers to rely on a black box of reporting. It is sort of the opposite of relying on first-party data — the brand might get scale and new customer opportunities but at a loss of any insight into their best customers or prospects

Emerging Multi-ID Options

Those are the options available now. As for what will be available in the future, there are a number of strong proposals across the industry for IDs that are functionally equivalent to third-party cookies, yet respectful of consumer privacy.

The Trade Desk’s UID2, LiveRamp’s ATS, LiveIntent’s nonID, ID5’s Universal ID, and Britepool’s ID have all emerged as independent options. While Google is developing a one-to-several solution called FLoCs, or Federated Learning of Cohorts. FLoCs propose to protect consumer privacy by grouping users into cohorts with similar behaviors – allowing one-to-several, rather than one-to-one tracking and targeting.

This emerging multi-ID space will definitely occupy a valuable place in the post-cookie targeting landscape. These solutions do not suffer the same drawbacks as the options described above, but they do have their own.

Most notably, the opt-in paradigm of the one-to-one proposals means these new identifiers will have much less scale than cookies. Given the number of competing proposals, the multi-ID space will be fragmented, introducing tremendous complexity.

Embrace the post-cookie transition

The transition away from cookies gives advertising organizations choices. Anchor to the familiar, such as first-party data, contextual advertising, walled gardens, or a raft of cookie-like IDs. Or, brands and agencies can embrace the transition.

The readily available cookie-free options come with clear limitations. Simply substituting an emerging identifier for cookies carries lots of uncertainty. Settling for a substitute is not going to give advertisers what they are looking for, and they will undoubtedly express disappointment.

But resting on one’s laurels, waiting for the ideal future path to emerge is not exactly innovative — it is passive. Brands and agencies can proactively develop new solutions to the issues of targeting and privacy. This requires these organizations to embrace some of the new limitations, rather than finding new ways around the resistance. If brand marketers only have access to certain signals, how can they combine them to deliver the best ad experiences? How much are brands willing to invest in testing right now? How willing are they to accelerate the percentage of budget they devote to cookieless solutions?

If the current post-cookie options on the table make one thing clear, it is that marketers cannot wait. If none of the options seem palatable, each individual brand has time to create the formulas that will work for them. Digital advertising is not going anywhere. But those that opt for the path of least resistance are going to be left behind.

If you want to learn more about how Dstillery can help you prepare for a post-cookie future, contact us.

How Retailers Can Invest in Data Now to Prepare for the Post-cookie World

Posted on May 28, 2021 by - Audiences

There’s no denying online retail’s momentum. E-commerce sales accounted for nearly $1 out of every $5 spent on retail purchases at the end of 2020. While retailers are suddenly eager to invest more online and expand their eCommerce operations, they are speeding towards a wall. 

The technology and tactics deployed today to stand out in a crowded marketplace are not going to work the same way a year from now, due to continued changes in browser privacy policies. Chrome, the web browser with the largest market share, will stop supporting cookies, the mechanism through which retailers collect data and then target their ads, beginning at some point in 2022. Google compounded marketers’ fears earlier this year when it stated that it would not support any identifiers that replace cookies for one-to-one targeting.

Retailers rely on retargeting to effectively reengage their current known prospects. Retargeting relies on cookies. Therefore, these changes mean that many of the data signals powering online retail’s surge will dry up. Requiring new solutions to effectively reach a brand’s best audiences. Retailers need to lay the groundwork now. They should test and learn which solutions to incorporate into their programs in 2021, to avoid stalling out in 2022.

Your real customer insights

While nearly every media expert can advise retailers to look at their first-party data, making that data actionable requires digging a little deeper.

Start by considering your image of the customer. It might be the single audience for which a product was specifically designed. It might be a few different segments that the brand sees as likely buyers. But often, this vision can differ from an e-commerce vendor’s actual customers.

While big data can help test a hypothesis and prove it right or wrong, it’s also great for uncovering surprises. One thing we constantly find in ad performance — and the resulting sales conversions — is that the audience that responds best is not always the image with which the brand started. This is a good thing because it opens new worlds of opportunity for brands.

The coming shifts in data collecting are unfortunately going to make this harder, so it’s important that retailers learn as much about their entire audiences as they can right now. Dig into the insights and conversion data to find out who your actual customers are. Who are the high lifetime-value buyers and the repeat buyers? Do they match your perception? The information you gather now can help define your campaigns and creatives going into the future, so invest heavily in a deep understanding now. This will prove to be valuable when retargeting site visitors becomes increasingly harder. Even better, these insights don’t have to be cookie or even ID-based right now.

Every online retailer is aware of the looming change to third-party cookies in Google’s Chrome Browser. What many may not be aware of is that there is already a large swath of internet traffic that is happening outside of cookies. Browsers like Safari and Firefox have had cookies turned off by default for years, and the audiences using those aren’t producing the same kind of behavioral insights. Right now, a little more than 40% of U.S. web ad inventory is not addressable. Once Chrome retires the third-party cookie, many in the industry expect that more than 70% — and even up to 90% — of inventory will lack a login or ID.

But that doesn’t mean they’re worthless. Safari is found on Apple products, which are often more expensive than competing products, and therefore likely owned by consumers with higher incomes. That’s a valuable audience for retailers. To prepare for the post-cookie future, start running campaigns that don’t rely on third-party cookies on these browsers today and gather all of the campaign data you can. Then, compare it to the cookies campaign data, and make adjustments as needed.

This cookie-free data is going to form the baseline for many of the campaigns you run in 2021, especially as retailers wait for wider adoption of identifier alternatives. So get familiar with cookie-free campaigns and establish a baseline you’re comfortable with.

A post-pandemic forecast

Retailers are already trying to understand how pandemic-influenced buying decisions will impact their revenue in the long run. There are looming questions over whether customers who mostly purchased online over the past year will return to stores, or if they will remain avid e-commerce shoppers. Are surges in spending temporary, or here to last?

Retailers will respond to this question differently, too. Those with online storefronts and physical locations will hope to retain customers, no matter how they purchase. Those with online-only or delivery businesses need to predict upcoming churn and prepare media plans for both retaining customers and continuing to convert new ones.

Understanding these behaviors is even more critical in light of the changes to targeted advertising coming in 2022. Start getting a sense of the messaging mix now, so that you can deploy the right plan in 2022 when buying behaviors will likely be more predictable than they were in 2020.

Above all else, retailers shouldn’t panic. The recent ad industry events aren’t the end of effective online advertising – they are simply the end of the previous era. Retailers that take the rest of this year to pore over their data and better understand their customers, their campaign performance, and how to activate these learnings in the authenticated and unauthenticated digital ecosystem, should have no trouble transitioning to a new era.

Dstillery offers a solution for the post-cookie world called ID-free®. Please contact us if you want to learn more.

A Deliberate Oversimplification of Audience Targeting without Cookies

Posted on April 20, 2021 by - Audiences

Paradigm shifts are hard. When you’re used to doing something a certain way, realizing that you can do it in a completely different way (and get similar results) is really hard to grasp. It can take anywhere from 18 days to almost a year to form a new habit. So it’s no surprise that many advertisers are expecting to have a difficult time with audience targeting, once third-party cookies go away. 

But what if there is a way to achieve the scale and accuracy of audience targeting in today’s cookie-based world, without third-party cookies or any form of alternative identifiers? To explain, here’s a deliberately oversimplified example of how to do audience targeting without cookies.

Deliberately oversimplified audience targeting example 

Let’s use a hypothetical campaign with very simple audience targeting criteria. A video game company is launching a new game and wants to target “gamers.” In the current cookie-based paradigm, a programmatic buyer could go into their buying platform, search for “gamer” and run the campaign across any of the hundreds of relevant gamer audiences that show up.

In the post-cookie paradigm, the programmatic buyer has a much harder task at hand. Without gamer audiences readily available, he/she will have to use a variety of identity-free signals and tactics. In the examples below, I’m going to make some generalizations for the sake of simplicity but will show how three identity-free signals — hour, designated market areas (DMAs), and website domains — can be used to accurately target the gamers audience.

Using HOUR as a signal

There are 24 hours in a day. If you had to guess, what are the times that video gamers are browsing the internet and most likely to be receptive to an ad for a video game? To keep things simple, let’s say that late nights from 9 p.m. to 2 a.m. are more likely than early mornings from 6 a.m. to 9 a.m.

Using DMA as a signal

There are 210 DMAs in the US. Using first-party data or third-party research, a brand can determine those that might be the best targets for a campaign.

Based on a study from WalletHub that used factors like internet speed and video-game stores per capita, certain cities in the US tend to be very conducive for a gamer lifestyle. These include Orlando, Seattle, Austin, New York, Los Angeles, Las Vegas, Irvine, Boston, and San Diego. This is just one data source, but for the purposes of this hypothetical campaign, it’s a good list of areas to target gamers.

Using DOMAIN as a signal

There are a handful of domains that gamers frequent to learn more about games: kotaku.com, reddit.com, twitch.tv, steampowered.com, and ign.com. But of course video gamers also visit websites not related to gaming at all. Tech blogs like gizmodo.com and theverge.com, music sites like pitchfork.com and spotify.com, news sites like nytimes.com and cnn.com, and much more.

Letting AI do the complicated parts for audience targeting 

Using these hours, DMAs, and domains as individual signals would probably yield a decent strategy for targeting an audience of gamers. But what if I told you there is a way to get much more accurate results, with a lot less manual work?

Using AI, there are ways to identify the most precise hours, DMAs, and domains that work best for an audience without having to have any pre-existing understanding of that audience or having to perform market research to figure out the signals. Further, as the target audience changes and shifts over time, the AI model will pick up on nuanced changes in behavior and automatically adjust to pick out the most accurate signals.

Using AI, decisions can also be made not only across the individual signals but across a combination of all three signals for maximum precision and accuracy. For example, imagine if an ad opportunity at 10 p.m, in the Orlando area, on kotaku.com was available in the buying platform. There’s a really good chance that you’d be looking at a gamer.

This might sound simple, but it can get complicated really fast. Take 24 hours, 210 DMAs, and let’s just say 50,000 domains. That creates more than 250 million combinations that have to be scored and ranked to accurately target a gamer audience! And of course, the audience targeting strategies that brands deploy get much more granular and complex than simply targeting “gamers.” They want to target gamers who use specific consoles or have preferences in the kinds of games that they play.

Shifting to id-free signals

The good news is all this complication is a problem that machine-learning and AI have been solving in digital advertising for a decade. While the application to date has primarily been for a cookie-based world, reapplying this technology to identity-free signals — and shifting our preconceived notions on how audience targeting has to work — would create a path to scalable and performant audience targeting, without using any IDs at all.

If you want to learn more about Dstillery’s latest product offerings, contact us.