As marketers all over the world prepare for the demise of cookies, many fret over what will become of their ad campaigns. The worries stem from myths, not facts, about cookie-based targeting.
We’ve never been fans of cookie-based targeting. In fact, our concerns about its impact on consumer privacy inspired us to found Digiseg, providing marketers with a solution that respects privacy while delivering better campaign performance.
Cookies were never meant to serve as a tracking mechanism. They were designed as tools to remember a user’s preference on repeat visits to a website. And frankly, that’s a fine use of cookies. Website owners can present personalized content to the user, and consumers get a more relevant experience.
But it was abused. Too many opportunists snooped the consumers’ browsers without their knowledge or permission, grouped them into audience segments and sold them off to marketers — also without the consumers’ knowledge or permission.
And while the industry flip flopped over cookie deprecation, consumers took the matter in their own hands, availing themselves of the opportunity to block all cookies in their browsers, hobbling the website owner’s ability to personalize their content to the individual user.
It’s time to end our addiction to cookies once and for all. And the first step to breaking an addiction is admitting to the problem. So let’s acknowledge the myths about cookies once and for all.
Myth #1: Cookies drive campaign success.
If a 0.35% CTR meets your definition of success, it’s easy to see why cookie-based targeting might appeal. At Digiseg, we see things differently, achieving CTRs that are consistently double, and sometimes triple, the cookie-based average.
Of course, cookie-based targeting relies on a bunch of assumptions (to wit: if a user visits a parenting site she’s a mom). But who’s to say that’s even remotely accurate?
We take an approach to data that’s wholly different from the cookie mindset. Our data asks: Is this a consumer whose lifestyle and circumstances mean they have a valid need for a product or service? If we can’t prove a need exists, we don’t show them an ad. That validation is the main driver behind the stellar performance of Digiseg data.
Myth #2: Measurement is really hard.Understanding who responded to a campaign and why requires expertise, and that’s a reality you can’t avoid.
This is true, but only in some cases. First, when one buys cookie-based audience segments, you’re never really sure what you’re buying. Such segments are based on assumptions, so you may think you’re targeting women aged 25 to 45, but who knows who you’re really targeting? I see ads all the time that assume I’m a woman, or that I have interest in supplements.
Now add the complexity of multichannel targeting and piecing together cookies with MAIDs and it’s an utter mess. In such scenarios, measurement is difficult and requires data analysts to engage in a painstaking process of making probabilistic guesses.
Now contrast that with Digiseg data, which uses the exact same audience segments to target users across all devices — desktop, mobile, CTV, smart radios — as well as measure campaign results. And, by the way, creating detailed reports with actionable insights can be had with just a few keystrokes.
Myth #3: Measurement will always be probabilistic, except in the rare 0.50% of cases where identity can be verified. This means cookies are no different from other tactics.
Um, no. Digiseg data matches IP address to neighborhood characteristics. We ask: Is this IP address associated with a neighborhood with single family homes and households with school-aged children (or any of the other characteristics reported by national statistics offices)?
We use that same approach to target and to measure, probabilistic data is the only way to truly ensure consumer privacy.
Myth #4: Sure consumers don’t like to be tracked, but without it, we can’t do one-to-one marketing, which is the great promise of digital advertising.
This myth is based on the assumption that one-to-one marketing is possible, effective and good, while one-to-many marketing is sub-par by comparison. But one-to-one marketing was a promise that the industry never could deliver on (a promise that brand managers have said all along misses the mark).
We’ve already discussed how cookies aren’t the most reliable indicators of user characteristics. A more pressing concern, however, is consumer trust in the digital advertising ecosystem. Consumers have shown their discomfort with being tracked by installing ad blockers, disabling tracking in browsers and devices, and advocating for stronger data privacy laws. It’s clear they’re not on board with the industry’s push for one-to-one marketing.
Digiseg’s probabilistic, one-to-many approach delivers much stronger results than cookie-based one-to-one strategies, without ever tracking individuals.
Myth #5: User ID graphs are a privacy centric way to measure website traffic and campaign performance.
Once again, this is a hard no. User ID graphs are a form of tracking that offers consumers little choice or transparency. These graphs enable companies to link the same consumer across multiple devices, which can offer some conveniences. For example, when a consumer registers for a website and accesses it from various devices, they can enjoy a personalized experience regardless of how they sign in. This is acceptable when the consumer willingly registers on the website and grants permission to link their devices.
However, if a site allows advertisers or partners to target consumers across all devices without their consent or input, it would be met with strong objection. The spirit of the myriad consumer data privacy laws is to enable individuals to go about their digital lives with a fair degree of anonymity. Including consumers in User ID graphs so that they can be tracked across their devices without their knowledge or permission violates the spirit of these laws.
Now theoretically users can opt out of inclusion into a user ID graph, but how realistic is that? As your husband, grandmother or neighbor if they even know what an identity resolution graph is, or how they go about removing their devices from them.