Lava Music is a music technology brand that has built genuine global recognition among young creators. Their product range spans professional-grade carbon fiber smart guitars to AI-powered beginner instruments, combining hardware innovation with digital learning tools.
The brand had strong awareness and a compelling product story. The challenge was making paid acquisition work efficiently across two fundamentally different audiences.
Professional players and zero-experience beginners have near-zero overlap in intent, vocabulary, and purchase triggers — but were being targeted under a single campaign structure.
Messaging tried to serve both audiences simultaneously. The result: creative that was too technical for beginners and not credible enough for professionals.
With blended targeting and mixed creative, spend was distributed inefficiently — driving up CAC while leaving high-intent segments underserved.
The core fix was architectural: rebuild the ad infrastructure so each product line operated as an independent campaign with its own targeting logic, creative system, and performance benchmarks. Mixing the two had been the root cause of inefficiency.
Professional Line
Lava Me Series campaigns
Targeted players with declared skill level, gear-adjacent interests, and music creation behaviours. Creative led with tech specs, material innovation, and sound quality — the language of serious players.
AI beginner Line
Blue Lava / Me Play campaigns
Targeted aspiring players, lifestyle-music adjacent audiences, and gift purchasers. Creative led with the learning journey, AI guidance, and low barrier to start — not hardware credentials.
Separate creative briefs per product line per market. Tested hooks, formats, and messaging frames until strong performers emerged in each segment.
Shifted spend toward highest-converting segment-market combinations. Eliminated waste from blended campaigns that had been diluting performance.
Rebuilt targeting from scratch per market — platform-specific signals, interest clusters, and lookalike modelling calibrated to each region's buyer behaviour.
With clean campaign architecture in place, we ran structured A/B testing across creative variables — iterating on the strongest performers and cutting underperformers quickly. The separated structure made attribution clean and optimisation fast.
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