campaign-optimization

Campaign optimization uses a phase-driven framework from learning through scale to improve CPA and ROAS. It enforces guardrails like minimum data thresholds and a 70/20/10 budget allocation across Meta, Google, TikTok, and LinkedIn to reduce manual guesswork.

Start Phase 1 by setting up 2-3 ad sets with varied audiences at $20-30/day. The structured learning phase enforces minimum data thresholds and weekly review checkpoints before scaling budgets to ensure algorithmic stability.

Yes, this framework applies LATAM-aware optimization nuances to paid acquisition campaigns. It structures cross-channel expansion across Meta, Google, TikTok, and LinkedIn specifically accommodating LATAM market dynamics during scaling.

Apply a repeatable 70/20/10 budget distribution for creative testing and scaling. This disciplined allocation routine pairs with a scheduled creative refresh cadence and weekly review checkpoints to maintain ROAS.

CPA spikes often occur when learning phase guardrails and minimum data thresholds are bypassed during cross-channel expansion. This framework prescribes phase-based playbooks with routine weekly checkpoints to stabilize CPA before scaling budgets.

Avoid scaling paid campaigns when minimum data thresholds are unmet or learning phase rules are violated. This framework enforces strict guardrails and escalation rules, requiring stable CPA and ROAS metrics before expanding budget allocations.