Ad Spend Optimizer

Systematically optimize paid advertising budget allocation across channels based on performance data, attribution analysis, and ROI targets.

When to Use This Skill

• Quarterly budget planning

• Channel mix optimization

• Performance troubleshooting

• Scaling paid acquisition

• ROI analysis and reporting

Methodology Foundation

Based on marginal ROI optimization and portfolio theory for marketing, combining:

• Channel performance analysis

• Attribution modeling

• Diminishing returns curves

• Test and scale frameworks

What Claude Does vs What You Decide

Claude Does You Decide

Analyzes channel performance Budget constraints

Calculates ROI by channel Risk tolerance

Recommends allocation shifts Testing budgets

Identifies optimization opportunities Business priorities

Creates performance dashboards Platform selection

Instructions

Step 1: Audit Current Performance

Key Metrics by Channel:

Metric Definition Target

ROAS Revenue / Ad Spend

3:1

CAC Cost to Acquire Customer <LTV/3

CPA Cost per Acquisition Varies

CTR Clicks / Impressions Benchmark

Conv Rate Conversions / Clicks Benchmark

Step 2: Attribution Analysis

Attribution Models:

Model Logic Best For

Last Click 100% to final touchpoint Direct response

First Click 100% to first touchpoint Awareness campaigns

Linear Equal across all touchpoints Balanced view

Time Decay More to recent touchpoints Shorter cycles

Position 40% first, 40% last, 20% middle Balanced with emphasis

Data-Driven ML-based attribution Sophisticated analysis

Step 3: Identify Optimization Opportunities

Optimization Levers:

Lever Impact Effort

Budget reallocation High Low

Audience targeting High Medium

Creative refresh Medium Medium

Bid optimization Medium Low

Landing page High High

New channel test Variable Medium

Step 4: Calculate Optimal Allocation

Marginal ROI Analysis:

• Where is next $1 best spent?

• Identify diminishing returns

• Find underinvested channels

• Balance efficiency vs. scale

Step 5: Implement and Monitor

Test Framework:

• 70% proven channels

• 20% optimization tests

• 10% new channel experiments

Examples

Example 1: Full Budget Optimization

Input:

Optimize this ad spend:

• Monthly budget: $100,000
• Channels: Google Ads (50K), Meta (30K), LinkedIn (15K), Other (5K)
• Goals: B2B SaaS, $200 target CAC, need 500 leads/month

Output:

Ad Spend Optimization Analysis

Current Performance Audit

Current Status:

• Missing target: 395 leads vs 500 goal (79%)
• Above CAC target: $253 vs $200 goal

Channel Analysis

Google Ads ($50K)

Findings:

• Brand is highly efficient but limited scale
• Non-brand has high CAC but scalable
• Display not performing—recommend pause

Recommendations:

• Increase brand budget (limited by search volume)
• Optimize non-brand (better audiences, negatives)
• Pause display, reallocate to Meta

Meta ($30K)

Findings:

• Lookalike audiences are star performers
• Interest targeting has room to scale
• Strong overall channel

Recommendations:

• Increase lookalike budget significantly
• Test new lookalike seeds
• Shift underperforming Google budget here

LinkedIn ($15K)

Findings:

• Highest CAC channel
• But: LinkedIn leads often higher quality (enterprise)
• Lead Gen Forms underperforming

Recommendations:

• Reduce overall LinkedIn spend
• Shift to sponsored content only
• Test LinkedIn for enterprise segment specifically

Other ($5K)

Breakdown: Reddit, Quora, programmatic Performance: Mixed, small sample sizes

Recommendation: Continue testing but don't scale yet

Recommended Budget Reallocation

Before vs After

Detailed Allocation

Google Ads ($35K)

Meta ($50K)

LinkedIn ($8K)

Testing ($7K)

Projected Results

Implementation Roadmap

Week 1:

• Pause Google Display campaigns
• Reduce LinkedIn Lead Gen to $0
• Increase Meta Lookalike by $5K

Week 2:

• Complete Google to Meta shift
• Launch TikTok test campaign
• Set up new Lookalike audiences

Week 3-4:

• Monitor and optimize
• Scale what's working
• Report on progress

Monitoring Dashboard

┌────────────────────────────────────────────────────────┐ │ AD SPEND DASHBOARD Week 1 │ ├────────────────────────────────────────────────────────┤ │ │ │ SPEND PACING │ │ Google: ████████░░░░ $8,200/$8,750 (94%) │ │ Meta: █████████░░░ $11,800/$12,500 (94%) │ │ LinkedIn:██████░░░░░░ $1,500/$2,000 (75%) │ │ Testing: █████░░░░░░░ $900/$1,750 (51%) │ │ │ │ LEADS THIS WEEK │ │ Target: 118 | Actual: 124 | Pacing: +5% │ │ │ │ CAC THIS WEEK │ │ Target: $211 | Actual: $195 | Status: ✅ │ │ │ │ ALERTS: │ │ ⚠️ LinkedIn CPC up 15% - monitor │ │ ✅ Meta Lookalike exceeding expectations │ │ │ └────────────────────────────────────────────────────────┘

Example 2: Quick Channel Assessment

Input:

Should we increase spend on this channel?

• Channel: Facebook
• Current spend: $10K/month
• CAC: $150
• Target CAC: $200
• Leads: 67
• Need: More leads

Output:

Channel Scale Assessment: Facebook

Current Performance

Scale Recommendation: YES, but carefully

Why scale:

• CAC ($150) is 25% below target ($200)
• Indicates efficiency headroom
• Leads are needed

How to scale:

Recommendation: Start with moderate (+$10K)

Scaling Checklist

• Expand Lookalike audiences
• Test new interest targets
• Increase frequency caps gradually
• Monitor CAC weekly
• Set alert at $185 CAC

Warning Signs (Stop Scaling)

• CAC exceeds $200
• CTR drops >20%
• Frequency >3.0
• Negative ROI on increment

Skill Boundaries

What This Skill Does Well

• Analyzing channel performance

• Recommending budget shifts

• Calculating ROI projections

• Creating optimization frameworks

What This Skill Cannot Do

• Access your ad accounts

• Make real-time bid changes

• Know your specific creative

• Guarantee performance

Iteration Guide

Follow-up Prompts:

• "Analyze [specific channel] performance"

• "How should we test [new channel]?"

• "Create a pacing dashboard for [budget]"

• "What's causing [performance issue]?"

References

• Google Ads Optimization Guide

• Meta Business Suite Best Practices

• LinkedIn Marketing Solutions

• AdEspresso Budget Allocation

Related Skills

• google-ads-expert

• Google-specific

• aarrr-metrics

• Full funnel view

• growth-loops

• Sustainable growth

Skill Metadata

• Domain: Acquisition

• Complexity: Intermediate-Advanced

• Mode: centaur

• Time to Value: 2-3 hours per analysis

• Prerequisites: Ad account access, performance data