# Paid Media Budget Allocation > Split a paid media budget across multiple platforms (Google, Meta, LinkedIn, TikTok, Amazon) and funnel stages. Triggers on 'allocate my paid media budget', 'how much should I spend on each platform', 'rebalance my ad spend across channels', or 'cross-channel budget planning'. Cross-platform allocation — for budget/bid decisions within one platform, use that platform's -optimization skill. - Skill: `gmmh1/paid-media-budget-allocation` (Agent Skill) - Install (CLI): `npx skillmds@latest add gmmh1/paid-media-budget-allocation` - Raw SKILL.md: https://api.skillmd.com/api/skills/gmmh1/paid-media-budget-allocation/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Finance & Business - Author: gmmh1 (https://skillmd.com/u/gmmh1) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/gmmh1/paid-media-budget-allocation --- # Paid Media Budget Allocation > **Currency & scope note (last reviewed 2026-07-19):** Platform mechanics referenced here (character limits, campaign-type names, feature availability, thresholds, benchmark figures, policy specifics) reflect general practice as of the review date above and are **not guaranteed current** — ad platforms change quickly. Verify anything mechanical against the platform's live documentation before relying on it for a real launch, real spend, or a compliance-sensitive decision. This is an independent, unofficial resource, not affiliated with or endorsed by any platform named in it, and nothing here is legal, tax, or financial advice. ## Purpose Split a finite paid media budget across multiple platforms and funnel stages based on channel fit, funnel role, and observed performance — not equal division or gut feel. ## Trigger Conditions - "Allocate my paid media budget across platforms" - "How much should I spend on Google vs. Meta vs. TikTok" - "Rebalance my ad spend across channels" - "Cross-channel budget planning" ## Required Inputs - Total available budget - Channels already selected (from `paid-media-channel-selection`) or already in use, with performance history if available - Funnel role each channel is meant to play (awareness/demand-creation vs. intent-capture vs. retention) - Minimum viable spend thresholds per platform to exit learning phases meaningfully (each platform skill's own guidance applies here — e.g., Meta/TikTok Learning Phase conversion thresholds, Google Smart Bidding data requirements) ## Core Capabilities ### Allocation by Funnel Role - Demand-creation channels (Meta, TikTok, Display, Demand Gen) typically justified by their role feeding intent-capture channels, not judged purely on their own last-click ROAS - Intent-capture channels (Google Search, Shopping) typically deserve first-priority funding since they're converting existing demand most efficiently - Retention/retargeting spend (a slice within each platform, not a separate line item) should be funded before incremental cold-prospecting expansion on any channel, since it's typically the highest-ROI spend available ### Minimum Viable Spend Thresholds - Spreading budget too thin across too many channels means no single channel ever accumulates enough conversion data to optimize well — recommending concentration in fewer channels at meaningful spend over thin coverage everywhere - Each platform's own learning/data thresholds (flagged in `meta-ads-optimization`, `google-ads-optimization`, etc.) should inform the minimum allocation a channel needs to be worth running at all ### Performance-Informed Rebalancing - For an account with performance history, shifting budget toward channels/campaigns with proven, sustainable CAC/ROAS — while distinguishing genuine channel performance differences from measurement/attribution artifacts (a channel that looks weak on last-click may be underappreciated for its assist role) - Testing budget vs. scaling budget as separate pools — a portion reserved for testing new channels/campaigns even while scaling proven ones ### Attribution Awareness - Cross-channel attribution is inherently imperfect (each platform's own reporting tends to over-credit itself) — use a blended view (GA4, `looker-studio-reporting`, or a marketing mix approach) rather than summing each platform's self-reported conversions, which typically overstates total performance ## Workflow 1. Confirm total budget, channels in use/selected, and their intended funnel roles. 2. Check each channel against its minimum viable spend threshold; deprioritize channels that can't be funded meaningfully. 3. Allocate: retention/retargeting first, then intent-capture, then demand-creation, then a smaller testing pool for new channels/campaigns. 4. For accounts with performance history, rebalance based on genuine (attribution-aware) performance differences, not raw self-reported platform numbers. 5. Revisit allocation on a defined cadence rather than treating it as a one-time decision. ## Outputs - Budget split by channel and funnel role - Minimum-viable-spend deprioritization calls, if applicable - Testing budget allocation - Attribution caveat/blended-view recommendation for judging cross-channel performance ## Rules - Never split budget so thin across channels that none can exit its learning/optimization phase meaningfully. - Fund retention/retargeting spend before incremental cold-prospecting expansion. - Don't sum each platform's self-reported conversions as the total performance picture — flag the over-crediting problem and recommend a blended measurement view. - **Verification gate:** before this skill's output is used to spend real money, submit a compliance-sensitive claim, or go to a client as final, verify the mechanical specifics (limits, thresholds, policy rules) against current platform documentation and get explicit human sign-off — do not treat this skill's output as launch-ready without that check. ## Related Skills `paid-media-channel-selection` (upstream channel decision), `looker-studio-reporting` (blended cross-channel reporting), each platform's `-optimization` skill (within-channel budget/bid tuning).