$83 → $1.35
Cost Per User
98.4%
Cost Per User Reduction
66.7×
User Growth
93%
Attribution Accuracy
Results at a glance
A VC-backed CannaTech startup blocked from all major ad platforms cut cost per download from $83 to $1.35 - and scaled 66.7× from 3,000 users using AI attribution.
The Situation & Approach
Context & Challenge
Jointly couldn't advertise on any major platform without getting flagged - the word 'cannabis' appeared throughout their website, iOS listing, and Android Play listing. This led to extremely high and unstable cost per new user. The company had tried multiple tactics but none were sustainable. Jointly had maxed out at just 3,000 users with a $83 cost per download. Without raising millions more in funding, the startup was at a crossroads - how to scale without being able to advertise.
Our Solution
Within 3 months, Revenue Institute decreased the acquisition cost of a new user from $83 down to $1.35. The solution: ad-clean microsites on a new domain where the word 'cannabis' was absent - allowing ads to run at scale without being flagged. The next challenge was attribution. Running ads from Meta to a landing page and then to the iOS store broke traditional attribution entirely. Revenue Institute's data team developed an AI inference-based attribution model using digital fingerprinting - tracking visitors through to app download with 93% accuracy, enabling Jointly to scale ad spend with full performance visibility.
The Results
Reduced Cost Per User by 98.4%
Due to the new ad infrastructure and attribution model, Jointly decreased cost per user from $83 down to $1.35 - a 98.4% reduction that made scalable paid acquisition viable for the first time.
66.7× More Users
With $1.35 cost per user and working attribution, Jointly scaled from 3,000 users to 66.7× that volume - growth that the old $83 acquisition cost made impossible.
AI Attribution at 93% Accuracy
Revenue Institute built an inference-based attribution model using digital fingerprinting that tracked users from Meta ad through to app signup with 93% accuracy - solving a problem no standard analytics tool could.
What this means for an operator
Jointly was a VC-funded consumer app blocked from advertising like the rest of its category - not a professional-services or contract-manufacturing firm. But the problem underneath is a common one: when the tools you already pay for can't tell you which dollar produced which customer, you are spending blind no matter how good the product is. The fix here was not more headcount watching dashboards - it was a system built to answer the one question standard analytics couldn't. Any operator staring at broken or unreliable attribution across multiple properties or channels is looking at the same problem, just with a different label.
Outcomes
Key Results Achieved
Reduced Cost Per User by 98.4%
66.7× More Users
AI Attribution at 93% Accuracy
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