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General Attribution
Not all brand operators have the time – or inclination – to wade into the technical thickets of attribution.
So here are a few useful, loose rules that brands at various stages of development can follow, courtesy of Alex Greifeld’s ecommerce marketing newsletter, No Best Practices.
So-called “level 0” brands don’t even need to worry about attribution, because they’re too green to even know how to drive new customer orders profitably and predictably.
Moving on, “level 1” brands rely on one channel for customer acquisition. It might be YouTube or TikTok, but most of the time it’s one of the apps in Meta’s fleet. These brands should use in-platform reporting to track their business results.
“Level 2” brands have at least one solid support channel – like influencers or YouTube – driving the core of their advertising on Meta.
“You can then pick ONE attribution methodology to help you make that decision,” Greifeld writes, whether it’s a plug-and-play model, custom model or experiments. And then stick with it for at least six months to a year.
And lastly, you’ve got “level 3” brands, which are rarely DTC. They involve store distribution and more complex physical supply chains, not to mention brand ecosystems built around diverse media.
“Level 3 brands probably should pick ONE attribution methodology and stick to it, but that rarely happens,” Greifeld laments.
We Stan For Standards
What’s stopping retail media standardization?
It isn’t such a massive technical lift, after all. But many retail media networks (RMNs) aren’t particularly interested in standards, because they do quite well by operating as walled garden black boxes.
“Marketers need to be able to make apples-to-apples comparisons between RMNs,” writes ANA EVP Bill Duggan at Ad Age. “That could potentially lead to greater spending for the overall category, with a rising tide lifting all boats if RMN spending drives business outcomes.”
But that “if” is carrying a heavy weight.
There is a common bad practice whereby RMNs tag users or cookie IDs on garbage-y made-for-advertising sites or other cheap, low-level inventory that doesn’t drive results for the brand. But the retailer can then claim credit on any purchase within an attribution window.
In that way, retailers can effectively take credit for organic sales – which a brand would have made anyway – because RMNs get paid by conversions.
Not all RMNs take advantage of this easy malfeasance. But retail media standards may not be a rising tide for all boats, which is why change has been slow.
Putting the “Rizz” in Plagiarism
If you can’t beat ’em, pay ’em.
That’s the strategy AI company Perplexity seems to be using to stem the tide of recent plagiarism accusations lobbied at its large language model.
The Verge reports that Perplexity is launching a “Publisher’s Program” that will share ad revenue whenever one of its products features content from its partners.
Those partners, which so far include Time, Fortune, Automattic and Entrepreneur, will also get a free one-year subscription to Perplexity’s Enterprise Pro tier in addition to some other perks (if you consider access to AI products a perk).
No word on whether this crop of publishers will also include Forbes or Wired, both of which very publicly accused Perplexity of plagiarism. (In Wired’s case, it was alleged plagiarism of a Wired article about Perplexity’s history of plagiarism!)
But for those pubs that are participating, something is better than nothing.
In the words of Automattic CEO Matt Mullenweg: “It’s a much better revenue split than Google, which is zero.”
But Wait, There’s More!
Yahoo will start selling programmatic ads for Disney and Spotify podcasts. [Axios]
Meta is running ads for illegal drugs on Facebook and Instagram. [WSJ]
Songwriters are getting screwed over by streaming media. [Variety]
The Senate passed new kid-focused online privacy bills, KOSA and COPPA 2.0, by a 91-3 vote. [WaPo] After the Senate vote, presumptive Democratic presidential nominee Kamala Harris voiced her support for the bills and urged the House to pass them, too. [tweet]
