Internal research
Case studies
We run original studies on the platforms we manage every day - creative, ad accounts, audience signals - and publish what we find. Results are gated; submit your email on each study to receive the full readout.
Study 01 · Organic & brand
Is organic really dead?
"Can you still track revenue back to organic - and is a familiar brand name worth building anymore?"
A look at what organic actually returns in 2026 - how to attribute revenue to it honestly, and whether building a brand name people recognize still pays back when the feed is mostly paid.
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Study 02 · AI & audience
A personalized internet is not the internet you grew up on
"In an age where content speaks to individuals across every platform, what happens to shared context - and to marketing that assumes it?"
A study on how AI-personalized feeds, search, and chat surfaces are dismantling the shared cultural reference points marketing has always leaned on - and what brands have to do when no two users see the same internet.
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Study 03 · Creative research
Women & Men in Creative
"Does representation in creative actually move conversion?"
An internal study on whether the gender represented in ad creative measurably changes who clicks, who converts, and at what cost.
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Study 04 · Paid media
Meta's Algorithmic Impulse
"Can you actually work around the algorithm's sensitivity - and what does it really promote?"
A field study on how Meta's delivery system reacts to creative pacing, audience signals, and rapid iteration - and what it quietly prefers.
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Study 05 · Paid media
The 'New Ad Account' Structure
"Does Meta reward new ad accounts more than accounts with history?"
A controlled comparison between fresh ad accounts and accounts with deep spend history - same creative, same audience, very different outcomes.
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Study 06 · Audience strategy
Lookalike & Customer Match: Timing > Volume
"For audience optimization, does the timing of your data matter more than the volume?"
A study of how recency of customer data affects lookalike and customer-match performance - and why bigger lists often lose to smaller, fresher ones.
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