
Роман Гарбар
Октябрь 20, 2026
Befit, one of the fastest growing workout apps of 2025 to 2026, used Tenjin to expand beyond Meta, measure subscription revenue across every channel, and scale UA spend on a plan that grew with the business.
In this case study, we explore how Befit, an AI-powered strength training app from Fortaleza, Brazil, started on Tenjin’s free plan, measured revenue across new channels for the first time, and grew paid subscribers 4.7x in nine months.
Results Snapshot

- 4.7x increase in paid subscribers in 9 months
- ≈50% saved on analytics costs
- 6x growth in monthly spend on TikTok and Apple Ads in four months
About Befit

Founded in 2024 by a Brazilian startup, Befit is a strength training app that combines AI-personalized training plans with progress tracking. The app has reached 3.8 million downloads and 2.7 million logged workouts, with a 4.8 rating from more than 30,000 reviews and subscribers in 166 countries. According to Sensor Tower data, it is one of the fastest growing workout apps from 2025 to 2026.
Вызов
Befit’s early growth came from a single channel: Meta. To keep scaling, the team wanted to add Apple Ads and TikTok. That meant measuring subscription revenue across multiple channels for the first time.
But, Befit had never used a mobile measurement partner (MMP) before. The usual starting point, an annual contract, felt like a big commitment before the team knew what the data would show. They needed a way to test measurement on a plan that matched the size of their business, and to prove its value before investing more.

“We had never used an MMP at Befit before, so we didn’t want to sign a year-long contract to find out if it was worth it. We started on the free plan with Tenjin. Seeing ROAS at the ad set and keyword level let us scale monthly spend on TikTok and Apple Ads from about $10K to over $60K in four months. We moved up a tier when the volume justified it. Tenjin earned every upgrade.”
Claret Sabioni, Co-founder at Befit
Решение
Befit started on Tenjin’s all-inclusive free plan and moved up tiers as their volume grew. With no annual commitment at any tier and a lower cost per conversion as they scaled, the team kept measurement costs under control and put more of their budget into the UA channels that performed best.
Tenjin brought subscription revenue from every channel into one place, so Befit could see ROAS at the ad set and keyword level. Tenjin also sent early subscriber signals back to ad networks, helping Befit optimize campaigns toward users whose behavior pointed to recurring revenue.
With a clear view of where spend went and which sources drove the best outcomes, Befit scaled with confidence. After significant growth, Befit signed a custom annual contract at a lower rate per conversion than Tenjin’s published plans.
Tenjin Features Used by Befit

• Cohorts Report
Tracks how users from each channel and campaign convert and retain over time.
• Early Payer and Trial Analysis
Spoty sources bring in trial users and early payers most likely to become subscribers.
• Signal Engineering to Meta and TikTok
Sends early subscriber signals back to networks so campaigns optimize toward high-value users.
• Callbacks
Shares in-app events with ad partners to support campaign optimization.
• MCP Server
Daily reporting workflows through an AI assistant by querying Tenjin data directly.
• Dedicated Support over DMs
Fast, direct answers from team Tenjin for any question that comes up.
Результаты
In nine months, Befit grew paid subscribers 4.7x. Clear ROAS data gave the team the confidence to increase monthly spend on TikTok and Apple Ads 6x, from about $10K to over $60K, in four months. Flexible, all-inclusive pricing helped them save around 50% on analytics costs, so more budget could go toward growth.
Befit’s story shows what’s possible when measurement scales with the business: start small, prove the value with real data, and grow from there.
Ready to see your own subscription revenue across every channel? Start exploring what you can do with Tenjin with our free and paid plans.





























