Why This Playbook Exists

Problem: When 70%+ of your customer acquisition comes from a single advertising channel, you're one algorithm change, policy update, or platform outage away from catastrophic revenue loss.

Solution: Use Marketing Mix Modeling (MMM) combined with systematic incrementality testing to identify, validate, and scale diversification opportunities without sacrificing efficiency.

Outcome: Reduce channel concentration risk from 0.78 to <0.40 (Gini coefficient) while maintaining or improving overall CAC.

For: Growth teams, marketing leaders, and data teams at companies with $100K+ monthly ad spend concentrated in 1-2 channels.

When to Use This Playbook

Prerequisites

• Minimum 3-6 months of historical spend and conversion data

• Ability to track conversions across multiple channels

• Budget flexibility to test new channels (10-20% of total)

• MMM capability or vendor (internal or external)

• Executive buy-in for temporary efficiency trade-offs

Step-by-Step Framework

Phase 1: Diagnose Your Concentration Risk (Week 1-2)

Goal: Quantify your vulnerability and establish baseline metrics.

Actions:

1. Calculate channel concentration using Gini coefficient

   • Pull last 6 months of spend by channel

   • Calculate cumulative spend percentages

   • Plot Lorenz curve and derive Gini score

   • Benchmark: >0.6 = high risk, 0.4-0.6 = moderate, <0.4 = healthy

   
   

2. Map your current channel mix

   • Document spend, conversions, and CPA by channel

   • Identify top 3 channels by volume

   • Calculate percentage of total conversions per channel
     
     

     

     This visualization shows the correlation between different marketing channels and your primary KPI over time, helping identify which channels move together and which provide true diversification.

   
   

3. Assess platform-specific risks

   • Review recent policy changes

   • Analyze historical volatility (CPM/CPC trends)

   • Document any account warnings or issues

     
     
     

     

     A “Gini Coefficient chart” displays concentration risk over time, with clear zones for high risk (>0.6) and moderate risk (0.4-0.6), helping stakeholders immediately understand the urgency of diversification.

Phase 2: Build Your Measurement Foundation (Week 2-4)

Goal: Deploy MMM to understand true channel contribution beyond last-click attribution.

Actions:

1. Set up Marketing Mix Model

   • Define primary KPI (first-time conversions recommended)

   • Aggregate weekly spend data by channel

   • Include external factors (seasonality, promotions)

   • Run initial model with 6+ months of data

2. Identify incrementality testing opportunities

   • Rank channels by MMM-attributed contribution

   • Flag channels showing high last-click but low MMM attribution

   • Prioritize 3-5 channels for testing

3. Design test roadmap

   • Map out 4-6 week testing windows

   • Calculate minimum detectable effects (aim for 10-15% lift detection)

   • Allocate 10-20% of budget for testing

MMM response curves showing diminishing returns on primary channel and opportunity areas in emerging channels.

Phase 3: Systematic Testing & Validation (Week 4-12)

Goal: Validate diversification opportunities through controlled experiments.

Actions:

1. Run geo-based incrementality tests
   
   

   

   
   

2. Execute channel-specific tests

   Real-world example progression (Klover case study):

   • Week 1-4: TikTok geo-lift (discovered 48% incrementality)

   • Week 5-8: Meta reduction test (found plateau at $60K/week)

   • Week 9-12: Apple Search scale test (validated efficiency at higher spend)

   
   

3. Document learnings systematically

   • Record actual vs. predicted lift

   • Calculate incremental CPA by funnel stage

   • Update MMM priors with test results
     
     

     

     A waterfall chart showing incremental contribution discovered through testing, broken down by channel.

Phase 4: Scale & Optimize (Week 12+)

Goal: Reallocate budget based on validated incrementality while monitoring concentration metrics.

Actions:

1. Implement phased reallocation
   
   

   

   
   

2. Optimize new channels for efficiency

   • Test creative formats native to each platform

   • Adjust bidding strategies based on platform algorithms

   • Implement platform-specific conversion tracking

   
   

3. Establish channel portfolio targets

   • Primary channel: Max 50% of spend

   • Secondary channels: 20-30% each

   • Testing budget: Always maintain 10%

   • Gini coefficient target: <0.45
     
     

     

     A portfolio allocation chart will show your recommended channel mix based on risk tolerance and efficiency goals.

Metrics & Monitoring Dashboard

Primary Metrics

• Gini Coefficient: Track weekly, alert if >0.6

• Incremental CPA by Channel: Compare to blended goal

• Channel Revenue Contribution: MMM-attributed vs last-click

Secondary Metrics

• Platform health scores (account warnings, policy violations)

• Creative fatigue indicators by channel

• Audience overlap percentage between channels

Reporting Cadence

• Weekly: Channel performance and concentration metrics

• Monthly: MMM refresh and incrementality test results

• Quarterly: Strategic channel portfolio review

  
  

Common Pitfalls to Avoid

❌ Testing too many channels simultaneously

• Dilutes budget below statistical significance thresholds

• Solution: Prioritize 2-3 channels maximum per quarter
  
  

❌ Ignoring platform-native best practices

• Copying Google Ads strategies to TikTok/Meta

• Solution: Invest in platform-specific creative and targeting
  
  

❌ Pulling back too quickly on underperforming tests

• Platform learning phases require 2-3 weeks minimum

• Solution: Commit to full test duration before decisions
  
  

❌ Over-correcting based on single test results

• One test doesn't account for seasonality/external factors

• Solution: Validate with follow-up tests or longer duration

Decision Framework

Resources & Templates

Benchmark Data

• Healthy channel mix by vertical:

  • SaaS: No channel >40%, 4+ active channels

  • E-commerce: No channel >50%, 3+ active channels

  • Mobile apps: No channel >45%, 5+ active channels

Success Story Snapshot

Klover’s growth team reduced their Google Ads dependency from 74% to 45% over 16 weeks using this framework. Despite initial concerns about efficiency loss, their blended CAC actually improved by 12% as they discovered undervalued channels through systematic testing. Their Gini coefficient dropped from 0.78 to 0.42, significantly reducing platform risk.

Next Steps Checklist

Week 1

☐ Calculate current Gini coefficient
☐ Document channel concentration percentages
☐ Identify top 3 diversification candidates

Week 2

☐ Set up MMM or engage vendor
☐ Design first incrementality test
☐ Secure budget approval for testing

Week 4

☐ Launch first geo-based test
☐ Establish monitoring dashboard
☐ Schedule weekly review cadence

Week 8

☐ Analyze test results
☐ Design follow-up validation tests
☐ Present initial findings to stakeholders

Week 12

☐ Implement budget reallocation
☐ Document playbook customizations
☐ Set quarterly review schedule