I analyzed 100+ Facetune ads with AI: what I’d test next as their creative strategist
The dominant pattern in my August 2026 Facetune ad review was a hairstyle reveal, “I’ve always had the wrong hairstyle”, shown in quick, TikTok-native cuts. Two of the three recent test lanes in the sample rebuilt that idea. The biggest opportunities are to extend it (new cuts, colors, creators, languages), carry the “wrong choice” mechanic beyond hair, build ads from what their reviewers already praise, and get far more emotionally specific about how the app does it.
The review uses public ads and customer reviews. It does not establish conversion performance, and the original analysis does not specify a ranking metric for Facetune. The recommendations below are tests I would run, not proven growth results.
By the numbers: 100+ ads analyzed | 1,000+ App Store reviews read | 18 recent test ads in three lanes | 2 of those 3 lanes build on hair
What stands out in Facetune’s ads?
Facetune is Lightricks’ photo and video editing app. We deconstructed their ads with our agent, BruteForce AI, and read over 1,000 App Store reviews to see what users actually want.
The recurring pattern is hairstyle-focused video: quick cuts of different women showing similar hairstyles, very TikTok native, with the message “I have always had the wrong hairstyle.” Several examples are in Spanish, showing language localization; language alone does not tell us the countries targeted. Another prominent ad is a split screen with quick cuts and the hook “these are literally the same videos”, in English.
These examples are built on the reveal, the transformation: helping someone imagine themselves different.
How is Facetune testing?
In that August review, we looked at 18 recent test ads across three lanes.
- The hair reveal. Four versions of “these are literally the same videos”: one in Spanish, two in English, and one in a language I couldn’t identify, all carrying the same line.
- Avoiding a bad haircut. “Avoid bad hairstyles”, “avoid bad style”, and a “style one, style two, style three, let me show you how” ad that mirrors the recurring reveal pattern.
- Natural look versus heavy filters. The new lane: bad, fake AI filters, including an AI-generated creative with an AI creator overlaid, plus a static.
They are building on an existing pattern rather than starting from scratch. But much of the extension in this sample was the same script remade with a new AI creator. I would test a wider range of variations.
What are the four biggest opportunities?
1. Extend the hair-reveal idea
Keep the “wrong hairstyle to the right one” shell and change one thing at a time: the cut, the color, the creator, the language. Even inside hair, you don’t stop at bangs and bobs. Most of the examples I reviewed focus on women and many are in Spanish, so I would confirm who the ideal user is and keep the extensions aimed there. Formats are a lever too: they have run talking-head demos, and could test split screen, wall of text and POV-style ads.
2. Carry the “wrong choice” hook beyond hair
In the sample, the “wrong choice” framing was applied to hair. It could be the wrong color, the wrong length, the wrong look before a party or before a date. Ask: if this is true, what else is true? If people want to imagine a different hairstyle, they may want to imagine different outfits, makeup, lighting, nails, backgrounds, retro looks or a different era. Those are the adjacent extensions I would test next. More on this in how to get dozens of ad variations from one winning ad.
3. Build new lanes from the reviews
Their reviewers do talk about hair. They also keep praising background and object removal, skin smoothing, lighting and golden hour, and natural, subtle edits. Little of that appeared in the ads I reviewed, which focused on hair try-on. Those are ready-made lanes to test, already written in their users’ words. How to turn customer reviews into ad hooks shows the method.
4. Get emotionally specific
The quick-cut, TikTok-style aesthetic sets a recognizable tone. The other big opportunity is emotional specificity, and the way to think about it is enemy, dream and bridge: – Enemy: what is the product fighting? The fake filter, the fear of looking fake. – Dream: looking glamorous, looking like a model. – Bridge: how the app gets you there, shown inside the app.
The ads I reviewed emphasized transformation, with little about how it happens. Showing the in-app steps is another test I would prioritize.
What is the takeaway?
Hair transformation is the recurring idea in this sample. I would test the same mechanic and “wrong choice” framing across outfits, makeup and lighting, then use the review themes to develop distinct concepts. The current product site lists tools for those sorts of edits; the tests still need to show what the app actually does. For more on transformation ads in this category, see before-and-after ads for AI photo apps and our Remini ads analysis.
The full scene-by-scene teardown is here.
Related reading
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Frequently asked questions
What kinds of ads appeared in the Facetune sample? The August 2026 review highlighted short, TikTok-style hairstyle reveals, including Spanish versions, plus a newer lane contrasting natural looks with heavy AI filters. Public visibility does not establish which converted best.
How did you analyze Facetune’s ads? We deconstructed 100+ of their ads with our agent, BruteForce AI, in August 2026 and read over 1,000 App Store reviews to compare what the ads say with what users praise.
What should a photo editing app test after a winning transformation ad? Change one element at a time inside the winner, then carry its mechanic to adjacent use cases, then mine reviews for features users love that the ads never mention.
What is the enemy, dream and bridge framework? A way to add emotional depth to a script: name what the user is fighting (the enemy), show the outcome they want (the dream), and prove how the product gets them there (the bridge).
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