TikTok post·Other·Slideshow·Vintage Photo Filters·camh4tesu

“A retro camera roll slideshow you can use to promote a vintage camera app - it hooks with an atmospheric travel photo, then showcases warm, film-filtered memories.”

Watch on TikTok - @camh4tesu

A retro camera roll slideshow you can use to promote a vintage camera app - it hooks with an atmospheric travel photo, then showcases warm, film-filtered memories. - slide 1
61× outlier1/12
260K
Views
18K
Likes
104
Comments
1.6K
Shares
4.3K
Saves
Outlier score
61×

4/5 reach magnitude vs the account's own baseline.

Pull score
5/5

How hard it moved viewers to save, share, and comment.

Why this qualified

260K views on a slideshow post, 61x the account's usual reach. The Outlier score reads reach as a multiple of the account's own median, and that median sits around 4.3K views, which is why the multiple matters more than the raw view count - it isolates the posts that outran their own channel instead of the ones riding a big following.

Its Pull score of 5/5 (15.9% weighted) measures how hard it moved viewers to save, share, and comment - the actions weighted heaviest because they signal real intent rather than a passing tap. The product it puts forward is A vintage camera app. Reach gets a post seen; Pull is the read on whether it landed.

Broken out, the post drew 18K likes, 104 comments, 1.6K shares, and 4.3K saves. Those are the numbers the Outlier and Pull scores are built from, nothing modelled or projected.

The full breakdown - why the hook works, the comment-intent read, and the ready-to-shoot dupe kit - lives inside Handler.

How this post compares

0.9x
vs pool median views
Top 49%
reach multiple in niche
Top 35%
Pull in niche

Its 260K views run about 0.9x the median of recent outliers in the library. That reach multiple ranks in the top 49% of the 354 other outliers we currently track. Its Pull score sits in the top 35% of that same set.

Likes led the raw counts, though saves and shares are what move Pull hardest. It is one of the few Slideshow breakouts among recent outliers in the library, just 14% of the pool.

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Want to dupe these outliers for your own app?

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