Chapter 6: Platform-Specific MMT Implementation

Different advertising platforms present unique challenges and considerations for Matched Market Testing implementation. Understanding these platform-specific nuances is crucial for successful test execution and accurate results interpretation.

Performance Max (PMAX) Complications

When testing other Google tactics (Search, Shopping) while PMAX campaigns are active, Google’s algorithm may automatically shift budget to PMAX to fill spending gaps, invalidating test results.

The Problem: Google’s automated budget allocation can compensate for reduced spend in test markets by increasing PMAX delivery, masking the true impact of the tactic being tested.

Solution: Temporarily pause PMAX in both test and control markets when testing specific Google tactics

Implementation Requirements:

• Minimum Spend: $40,000+ monthly total Google spend recommended for PMAX isolation tests
• Coordination: PMAX pause must be synchronized across test and control markets
• Duration: PMAX should remain paused throughout entire test period
• Monitoring: Track total Google spend to ensure no unexpected reallocation

Brand vs. Non-Brand Testing

Google’s automated bidding can create interference between branded and non-branded campaigns during testing.

Challenges:

• Smart Bidding algorithms may compensate across campaign types
• Budget sharing between brand and non-brand can mask individual tactic performance
• Automated extensions may blur attribution between campaign types

Best Practices:

• Test brand and non-brand campaigns separately when possible
• Use manual bidding during test periods for more precise control
• Monitor search impression share for both branded and non-branded terms
• Document any automated bidding changes during test period

Geographic Targeting Verification

Critical Setup Requirements:

• Always verify that geo-targeting is properly configured before launching tests
• Confirm reporting is available at the market level (DMA, state, or custom geography)
• Test geographic exclusions to ensure proper implementation
• Validate that location targeting matches test market boundaries precisely

Common Issues:

• Location targeting vs. location of interest settings confusion
• Radius targeting overlapping test/control boundaries
• IP-based location detection inaccuracies
• Mobile location services variations

Advantage+ Shopping Campaigns (ASC)

ASC campaigns cannot be selectively grown in specific markets due to account-level targeting limitations.

Limitation: Growth tests not feasible for ASC campaigns due to Meta’s automated geographic optimization
Alternative: Holdout tests can measure existing ASC effectiveness by pausing campaigns in test markets
Workaround: Test traditional campaign types (manual Shopping, broad prospecting) instead of ASC for growth testing

Geographic Targeting Setup

Requirements for Reliable Testing:

• Use location-based targeting rather than interest-based geographic indicators
• Ensure audience definitions don’t overlap between test and control markets
• Verify that custom audiences can be segmented geographically
• Test location targeting accuracy with small budget campaigns before full test launch

Meta-Specific Challenges:

• Lookalike audiences may not respect geographic boundaries precisely
• Interest targeting can include users outside defined geographic areas
• Cross-border commuting and mobile users can blur geographic targeting
• Video view custom audiences may include views from outside target markets

Campaign Configuration Best Practices

Setup Validation:

• Create separate campaigns for test and control markets
• Use identical creative, targeting, and bidding strategies across markets
• Implement naming conventions to clearly identify test vs. control campaigns
• Set up separate conversion tracking for test and control markets when possible

Amazon Search

Cannot run MMTs due to geographic targeting limitations – use Media Mix Modeling instead

Why MMT Isn’t Feasible:

• Amazon’s advertising platform doesn’t support precise geographic targeting at DMA or state level
• Reporting limitations prevent market-level performance analysis
• Fulfillment and shipping patterns don’t align with advertising market boundaries
• Prime membership geographic distribution affects performance inconsistently

Alternative Approaches:

• Use MMM to evaluate Amazon’s contribution to overall marketing mix
• Leverage Amazon’s internal attribution tools for optimization
• Focus on product-level or category-level testing within Amazon
• Use time-based causal impact analysis for Amazon strategy changes

Pinterest

Geographic data limitations prevent reliable MMT execution

Technical Constraints:

• Limited geographic targeting precision for many campaign types
• Reporting doesn’t provide sufficient market-level granularity
• User behavior patterns don’t align well with traditional geographic boundaries
• Platform’s discovery-based nature makes geographic control challenging

Influencer Marketing

Typically lacks geo-targeting capabilities – better suited for Causal Impact Analysis

Why MMT Is Challenging:

• Influencers have national/international follower bases
• Content distribution doesn’t respect geographic boundaries
• Organic reach and sharing amplify beyond target markets
• Attribution windows and measurement differ significantly from paid media

Alternative Measurement:

• Use Causal Impact Analysis around influencer campaign launch dates
• Implement unique promo codes or URLs for attribution
• Survey-based brand lift studies in specific markets
• Social listening analysis for geographic mention patterns

During-Test Monitoring

Daily Checks:

• Geographic delivery verification
• Budget pacing and allocation
• Campaign status and performance
• Any platform-initiated changes or optimizations

Weekly Reviews:

• Cross-platform performance comparison
• Test vs. control market performance trends
• External factor identification (competitive activity, etc.)
• Statistical significance progress tracking

Common Troubleshooting Issues

Budget Allocation Problems

Symptoms: Uneven spend distribution between test and control markets
Solutions: Adjust bid strategies, budget caps, or targeting parameters
Prevention: Daily budget monitoring and automated alerts

Geographic Leakage

Symptoms: Delivery outside intended test markets
Solutions: Tighten geographic targeting, exclude border areas
Prevention: Conservative geographic boundary definition

Platform Algorithm Interference

Symptoms: Unexpected performance changes not related to test variables
Solutions: Document changes, adjust analysis for platform updates
Prevention: Monitor platform announcements and algorithm updates

Attribution Timing Issues

Symptoms: Conversion attribution doesn’t align with test period
Solutions: Extend measurement window, adjust attribution settings
Prevention: Pre-define attribution windows based on platform capabilities

Understanding these platform-specific considerations ensures your MMT implementation accounts for technical constraints and provides reliable, actionable results across your marketing channels.

Next Steps: Learn about Test Duration and Statistical Rigor

Next Chapter