Allocate ad budget across platforms
ads-budgetskillsetup L3★20
naveedharri/benai-skills ↗Causal-lift measurements
ad-account-auditing-4pp vs no-skill baselinewith-skill 80% · baseline 84%
Measured by running the task with and without this artifact, K=5, graded by deterministic checks — no LLM judging.
What it does
Allocate ad budget across platforms and determine optimal bidding strategies
Best for
Performance marketing teams when you need a budget reallocation framework that follows the 70/20/10 rule and matches bidding strategy to learning phase.
Inputs
- · Active platforms and current spend
- · Performance data (ROAS, CPA, CTR, conversion volume)
- · Learning phase status per platform
- · Budget constraints
Outputs
- · Budget allocation by platform (70/20/10 rule)
- · Bidding strategy recommendation (Maximize Clicks → Conversions → Target CPA → ROAS)
- · Scaling readiness assessment
- · Kill list and scale list
Requires
- · Google Ads
- · Meta Ads Manager
- · LinkedIn Campaign Manager
- · TikTok Ads Manager
- · Analytics integration
Preconditions
- · Performance data collection running (≥2 weeks per platform)
- · Conversion tracking implemented
Failure modes
- · Scaling too fast (>20% increase at once)
- · Bidding strategy mismatch to learning phase
- · Insufficient budget for platform learning (minimum daily spend not met)
Trust signals
- · 70/20/10 allocation rule (proven/scaling/testing split)
- · Platform selection matrix by business type (SaaS B2B, E-commerce, Local, Enterprise, etc.)
- · Minimum daily budget rules per platform (Google Search $20, Meta $20/ad set, TikTok $50 campaign)
- · Bidding strategy decision trees (Google: <30 conv/month → Maximize Clicks; >30 → Maximize Conversions; >50 → Target CPA; revenue tracking → Target ROAS)
- · Learning phase exit criteria (≥50 conversions/week, CPA stable, CTR stable)
- · 20% scaling rule with timing guidance (monitor 3-5 days after each increase)