Most branded content today underperforms because it isn’t built for how organic distribution actually works. How much organic reach and deep engagement your content gets obviously depends on content quality, but also on the algorithm’s ability to match signals in your content with recent signals in audience behaviour.
When those signals are limited, distribution is too; when they are high quality, reach potential expands. This is what this edition explores.
Algorithms distribute content based on what people have already engaged with — and how skilfully your content connects to those earlier interactions. We call this signal loading: the deliberate act of embedding recognisable elements into your content to maximise the number of people the algorithm can serve it to.
Every item — brands, sounds, cultural references, people, locations, trending topics — can act as a signal the algorithm uses to find your audience. Meanwhile, audience priming (to be covered in a future newsletter) is the accumulated work that grows the pool of receptive viewers over time.
Let's look at a few examples of signal loading.
When you load every signal: Dr Pepper at B&M
Case study shared in our Q1 Earned highlight newsletter
This post hit 380,000 engagements as a direct result of the creator inserting multiple brands and franchises (visually, orally) in a way meant to surprise, confuse and/or annoy members of these communities. The post engine is that sheer density of signals: Dr Pepper, BuzzBall, Scrub Daddy (brands), B&M (retailer and Gen Z cultural space), Stitch, South Park, Rainbow Friends, viral toys (trending topics), the creator himself (people). Each one is a matching pathway. The algorithm didn't need to guess who might care because it had a dozen routes to choose from.
A creator can get away with this kind of kitchen-sink approach. A brand on its own feed can't and shouldn't (or at least not to this extent). So the question becomes: which signals do you load, and how deeply do you commit?
Cultural references: Dr. Squatch × Stranger Things
Cultural references are one of the most powerful signal types because they plug directly into existing audience memory — with built-in meaning.
Dr. Squatch makes the most of its Stranger Things collaboration by having its content operate within the Netflix show’s world. By staging Murray fully in character, the tie-up just makes sense and compels engagement from true fans of the show.
Other recent examples of quality cultural tie-ins: Starbucks x Devil Wears Prada, Lana del Rey fans x Pepsi, Audi x Nihilist penguin, Pepsi Super Bowl ad (e.g. link to Coldplay Kisscam)
People and celebrities: the most powerful and most dangerous signal
Celebrities are the ultimate (and most expensive) algorithmic shortcut. Their face and name alone unlock millions of primed users. But the high view numbers often mask a less glamorous reality: if the content doesn’t surface a natural overlap between brand and celebrity worlds, the paid partner’s aura overshadows the brand and negates the intended benefits of the collaboration.
✅ When they align: Adidas × Bad Bunny
In this masterpiece, the Adidas tracksuit becomes a uniform for Puerto Rican cultural resistance. The product has a role in his story — you can't tell the narrative without it. We call this “Tier A” celeb/brand integration. (See our tiering here)
👎🏼 When they don't align (and high numbers are misleading!): Armani × Namtan
2.4M engagements, but a tiny fraction acknowledges the brand: most engagement is focused on her
Numbers look impressive, but 66% of sampled comments are bot accounts. Only 2% of real comments mention the actual product. These results can be expected from watching the post itself: there is no real plot or attempt at integrating the brand with the celeb’s personality or her backstory. Armani loaded one signal — Namtan — and the algorithm duly matched on her, not on them. The reach (and impact) belong to the celebrity.
The implication is straightforward: of course brands should continue to master today’s creative codes (earlier newsletters talk about these), but content performance is increasingly also a function of how well signals are selected, inserted, combined, and activated. Designing for algorithmic memory has become a non-negotiable for your content to stand out.
More to come on how to build and compound these signals over time.
Enjoy more recent examples of top posts dissected by our content frameworks:
Q1 Earned gems, March 2026 Owned stand-outs
