How CANN & Spokes Digital Revolutionized TikTok Performance for Cannabis-Infused Beverages
Result:
Hours of Watch Time
+
0
New Customer Acquisitions
0
+
ROAS (Return on Ad Spend)
0
x
Lower CAC (Customer Acquisition Cost)
0
%
Problem Statement
Creative Friction
CANN’s legacy high-production assets were "too polished" for TikTok, resulting in high skip rates and low engagement.
Policy Complexity
Navigating TikTok’s shadow-banning and strict ad policies regarding cannabis-related products led to frequent ad rejections.
Platform Saturation
High competition on Instagram and Google (where CPCs were skyrocketing) made it difficult to scale customer acquisition profitably.
Community Distrust
The need to move away from "Brand-Speak" and find an authentic "Human-Face" that the TikTok community would trust.
Spokes Solutions
We moved away from anonymous brand ads and identified key creators (like Jaz Melody) to serve as the brand's primary voice. This built immediate trust and bypassed the "corporate" feel that TikTok users reject.
To circumvent algorithmic censorship, we developed a "Brand Lingo Book," using coded but culturally relevant terminology (e.g., "gardening") to maintain high delivery and account safety.
We implemented a rapid-fire creative testing cycle, prioritizing iPhone-shot, raw content over expensive studio shoots. This allowed us to rotate 10-15 fresh assets monthly, preventing creative fatigue.
Instead of standard "In-Feed" ads, we utilized Spark Ads to promote the creators' organic posts. This gave the ads the "Social Proof" (comments, likes, shares) needed to lower CPCs and improve the algorithm’s quality score.
Used TikTok as a "top-of-funnel" engine to feed high-intent audiences into our retargeting loops on other platforms, effectively lowering the blended CAC across the entire marketing mix.
By shifting the focus from “clicks” to “contribution margin,” Spokes Digital didn’t just manage Aspen Green’s ads; we engineered a scalable growth engine. The result is a brand that now dominates the premium CBD space with a predictable, data-backed acquisition model.
