ad-spend-optimizer

guia-matthieu

Aktualisiert 2 days ago

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Testenaitesting

Über

Diese Fähigkeit analysiert Werbeleistungsdaten über Plattformen wie Google Ads und Meta hinweg, um Budgetumverteilungen zu empfehlen, die die Rendite auf Werbeausgaben (ROAS) maximieren und die Kosten für Kundengewinnung (CAC) minimieren. Entwickler können sie für die Quartalsplanung, die Diagnose unterdurchschnittlicher Kanäle und die Neujustierung der Medienmischung nach Leistungsverschiebungen nutzen. Ihre Kernfähigkeit ist die Berechnung des marginalen ROI, um datengestützte Entscheidungen zur Ausweitung oder Reduzierung des Budgets zu treffen.

Schnellinstallation

Claude Code

Dokumentation

Ad Spend Optimizer

Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC.

When to Use This Skill

Quarterly budget planning — reallocate spend based on performance data
Channel mix optimization — find the right balance across platforms
Performance troubleshooting — diagnose why CAC is rising or ROAS declining
Scaling decisions — determine if a channel has headroom to scale
New channel testing — structure test budgets with clear success criteria

Methodology Foundation

Aspect	Details
Source	Marginal ROI optimization + portfolio theory for marketing
Core Principle	Allocate each dollar where the marginal return is highest — shift spend from diminishing-returns channels to underspent ones
Framework	70/20/10 — 70% proven channels, 20% optimization tests, 10% new channel experiments

What Claude Does vs What You Decide

Claude Does	You Decide
Calculates ROAS, CAC, and CPL per channel and campaign	Total budget constraints
Identifies diminishing returns and reallocation opportunities	Risk tolerance for new channels
Models projected outcomes for different allocation scenarios	Business priorities and brand considerations
Creates monitoring dashboards and alert thresholds	Platform selection and creative direction

Instructions

Step 1: Audit Current Performance

Collect these metrics per channel and campaign:

Metric	Formula	Healthy Range
ROAS	Revenue ÷ Ad Spend	>3:1 for most B2B/B2C
CAC	Ad Spend ÷ New Customers	<LTV ÷ 3
CPL	Ad Spend ÷ Leads	Varies by industry
CTR	Clicks ÷ Impressions	>1% search, >0.5% social
Conv Rate	Conversions ÷ Clicks	>2% landing pages

Validation checkpoint: If data is missing for any channel, flag it — incomplete data leads to wrong reallocations.

Step 2: Attribution Analysis

Choose the model that matches the business:

Model	Best For	Trade-off
Last Click	Direct response, short cycles	Ignores awareness
First Click	Awareness campaigns	Ignores conversion assist
Linear	Balanced multi-touch view	Dilutes signal
Time Decay	Shorter sales cycles	Biases toward bottom-funnel
Position-Based	Balanced with emphasis	May miss mid-funnel
Data-Driven	Sophisticated, enough data	Requires volume

Step 3: Calculate Marginal ROI

For each channel, answer: Where does the next $1 produce the most return?

Signal	Meaning	Action
CAC well below target	Headroom to scale	Increase spend 50%, monitor weekly
CAC at target	Optimized	Maintain, test creative
CAC above target	Diminishing returns	Reduce spend, reallocate
Low volume, good CAC	Underinvested	Scale cautiously (2x)
High volume, rising CAC	Hitting ceiling	Cap spend, diversify

Step 4: Model Reallocation Scenarios

Build 3 scenarios (conservative, moderate, aggressive) showing projected leads, CAC, and ROAS at each budget level. Include:

Per-channel breakdowns with expected performance
Warning thresholds — CAC levels that trigger spend cuts
Implementation timeline — weekly changes, not all at once

Step 5: Implement and Monitor

Weekly monitoring checklist:

Spend pacing vs. plan
CAC by channel vs. target
Lead volume vs. forecast
Any channel crossing warning threshold?

Scaling rule: If CAC stays 15%+ below target for 2 consecutive weeks, increase spend by 25%. If CAC exceeds target for 2 weeks, reduce by 25%.

Examples

Example: B2B SaaS Budget Reallocation

Input: $100K/month — Google ($50K), Meta ($30K), LinkedIn ($15K), Other ($5K). Target: $200 CAC, 500 leads/month. Current: 395 leads, $253 CAC.

Diagnosis:

Google Display ($15K → 30 leads, $500 CAC) — cut entirely
Meta Lookalike ($15K → 85 leads, $176 CAC) — star performer, scale
LinkedIn Lead Gen ($5K → 10 leads, $500 CAC) — cut

Proposed reallocation:

Channel	Current	Proposed	Expected CAC
Google Ads	$50K	$35K	$206
Meta	$30K	$50K	$196
LinkedIn	$15K	$8K	$286
Testing	$5K	$7K	Variable

Projected result: 473 leads (+20%), $211 CAC (-17%).

Skill Boundaries

What This Skill Does Well

Analyzing multi-channel ad performance from provided data
Recommending budget shifts based on marginal ROI
Modeling reallocation scenarios with projected outcomes
Creating monitoring frameworks with alert thresholds

What This Skill Cannot Do

Access ad platform accounts or pull live data
Make real-time bid adjustments or campaign changes
Evaluate creative quality (headlines, images, video)
Account for brand lift or offline conversion effects

References

Google Ads Optimization Guide
Meta Business Suite Best Practices
LinkedIn Marketing Solutions
Common Thread Collective — ad spend allocation methodology

Related Skills

google-ads-expert — Google-specific campaign optimization
aarrr-metrics — Full funnel view beyond paid acquisition
growth-loops — Sustainable growth beyond paid channels

GitHub Repository

guia-matthieu/clawfu-skills

Pfad: skills/acquisition/ad-spend-optimizer

ai-skillsanthropicclaude-codeclaude-skillsmarketingmcp-server