From 5 new users per day to 300! Incredible growth for CircleRanks
We didn’t just run ads. We built a compounding growth loop that took Circle Ranks from 3K to 91K followers, pushed content from 300–400 daily views to 11M, and drove 300+ new users per day, and increased conversion rate from 2% to over 21%.
THE CHALLENGE
We were tasked with building and launching an inbound campaign for CircleRanks, a movie review app that had been struggling with user acquisition for 6 months prior. Typically seeing 0-3 new users per day, peaking at 5, and struggling with daily active userse.
1) Content performed inconsistently: view counts didn’t reliably translate into clicks
2) Common measurement tracking gaps to attribute ads to website to app store downloads, and also no tracking & data passback for in-app usage
3) The team needed a repeatable system to turn attention into downloads + active users, not just likes.
“Is Anthony a super genius? …we have 12 million views on one post… the moment we started working with you, the things exploded.” - James, CEO
OUR APPROACH
We didn’t come in and “reinvent” Circle Ranks. The concept was already strong. The real job was turning that concept into a repeatable growth loop.
First, we treated content like distribution, not just posting. Instead of hoping a great post “takes off,” we built a system where your best posts get pushed on purpose. That meant running Meta campaigns that amplified the winners and quickly tested what the audience actually reacted to.
At the same time, we separated attention from conversion. One set of campaigns was designed to generate massive engagement and reach (the snowball). Another set was designed to turn that attention into real actions: clicks to the site, click-throughs to the app stores, and installs. Those two lanes fed each other. Engagement made the brand bigger and cheaper to reach. Conversion made the growth measurable and reliable.
Then we tightened the creative and messaging so the posts didn’t just get watched—they got acted on. Small copy shifts mattered. We tested language that clarified “this is ranked by real users,” and in some cases that kind of adjustment drove meaningfully stronger conversion behavior.
Finally, we tracked the whole path instead of guessing. We watched where people fell off between ad → website → app store and used that to guide what we adjusted next. We also compared platform performance (Facebook vs Instagram) so budget wasn’t being “split evenly,” but split intelligently.
The end result wasn’t just viral numbers. It was viral numbers that turned into installs and daily active users.
