Mellon’s subscription apps, Videa AI and Cleaner Pure. I built the paid marketing function from the ground up, and the portfolio reached monthly revenue in the seven figures.
This is the case for paid UA for subscription apps and for signal engineering done before scale. It is also the account behind the $383K audit of how often Meta hits its target.
The starting point
No paid function to speak of, and a portfolio of subscription apps that needed one built rather than tuned. The work was the whole stack: Meta, Google Ads, TikTok and Apple Search Ads, the team, the processes and the way the company thought about growth.
What was built
- A custom purchase signal through CAPI, so Meta optimized toward payers rather than trial starts. Why that comes first.
- Predicted LTV in the bid, so the platform valued a user by what they were likely to pay rather than by what they had paid on day one.
- A ROAS framework set by country, so budget followed payback by market. The method is written up with the numbers.
- Four channels run daily by the same person reading the data.
What happened
D0 ROAS went from 20 percent to a weekly record of 43 percent. Spend scaled past $200K a month. The portfolio reached monthly revenue in the seven figures, and along the way the team caught a viral moment that the paid function was ready to carry.
In the client’s words
I worked closely with Samet while he led the paid marketing transformation at our company, and the impact was substantial and lasting. We built our marketing function from the ground up together: the team, the processes, the way we think about growth. Together we caught a viral moment that helped carry us to seven-figure monthly revenue. If you are looking to build or scale a serious paid marketing engine, Samet is someone I would recommend without hesitation.
Questions people ask
Which channels ran?
Meta, Google Ads, TikTok and Apple Search Ads, with Meta carrying most of the testing because creative moves fastest there.
What was the single biggest lever?
The purchase signal. Once Meta could optimize toward payers rather than trials, the same budget found a different population. Predicted LTV in bidding and the country framework compounded on that.