building trust
The Challenge: Stakeholders are skeptics by nature
When we first began implementing Marketing Mix Models with clients, we encountered a consistent problem. The moment we presented MMM results, stakeholders would compare them to their Google Analytics data and respond with immediate skepticism.
"These numbers look nothing like what we've been seeing for years. Why should we trust this model over our existing analytics?"
This question derailed implementation efforts repeatedly. Without trust in the model's outputs, budget reallocation recommendations went ignored, and ROI improvement opportunities disappeared.
A Step-by-Step Process to Build Measurement Trust
After working with hundreds of companies, we developed this structured implementation framework to overcome stakeholder resistance and establish MMM as the trusted source for marketing measurement.
Step 1: Begin Data Collection While Planning Validation Tests
Implementation steps:
Extract sales data at geographical level immediately (Week 1)
Set up data connectors for marketing channel spend data (Weeks 1-4)
Map out test and control regions for geo experiments (Week 1)
Identify top 3 marketing channels by spend for initial testing (Week 1)
Problem this solves: MMM implementations often stall during extended data collection periods. By working with available sales data immediately while setting up validation tests, you create parallel workstreams that maintain momentum.
Step 2: Design and Execute Geo Incrementality Tests
Implementation steps:
Select demographically similar geographical regions (Week 2)
Designate test regions (marketing activity continues) and control regions (marketing paused) for each major channel (Week 2)
Implement spend adjustments across regions (Week 2)
Collect performance data for 4 weeks (Weeks 3-6)
Calculate true incremental lift for each tested channel (Week 7)
Problem this solves: Stakeholders need tangible proof that traditional attribution underreports or misattributes marketing impact. Controlled experiments provide scientific validation that becomes your "ground truth" reference point.
Step 3: Complete MMM Data Integration
Implementation steps:
Finish connecting all marketing data sources (Weeks 1-4)
Clean and normalize spend data across channels (Week 5)
Add external factors data (seasonality, pricing, competitor activity) (Week 5)
Validate data completeness and quality (Week 6)
Problem this solves: Comprehensive data integration ensures your model accounts for all variables affecting performance. This completeness helps prevent unexplained variance that could undermine trust in results.
Step 4: Build and Calibrate Initial MMM
Implementation steps:
Train preliminary MMM model using historical data (Week 7)
Compare initial MMM channel performance estimates with geo test results (Week 8)
Adjust model parameters to align with experimental outcomes (Week 8)
Validate calibration through backtesting against known performance periods (Week 8)
Problem this solves: Uncalibrated models often produce implausible results. Using incrementality tests to calibrate your model grounds it in experimental reality, creating defensible outputs.
Step 5: Test Model Stability and Predictive Power
Implementation steps:
Perform stability assessments across multiple model iterations (Week 9)
Conduct train-test splits to evaluate predictive accuracy (Week 9)
Document model limitations and boundary conditions (Week 9)
Create confidence intervals for all model outputs (Week 9)
Problem this solves: Hidden model weaknesses undermine trust when discovered later. Proactive identification and documentation of limitations establishes transparency and prevents unexpected disagreements during implementation.
Step 6: Present Comparative Results to Stakeholders
Implementation steps:
Prepare side-by-side comparison of:
Platform-reported metrics
Incrementality test results
Calibrated MMM output
Present findings in stakeholder workshop (Week 10)
Address discrepancies with experimental evidence (Week 10)
Document consensus on measurement approach (Week 10)
Problem this solves: Direct comparison between traditional metrics, experimental results, and calibrated MMM bridges the perception gap for stakeholders. This builds confidence in the new measurement framework through empirical evidence rather than theoretical explanations.
Step 7: Implement Monthly Refresh Cycle
Implementation steps:
Establish automated data pipeline for monthly model updates (Week 11)
Schedule additional incrementality tests on quarterly basis (Week 11)
Create dashboard comparing model predictions vs. actual results (Week 12)
Document narrowing of confidence intervals over time (Ongoing)
Problem this solves: Static models become less relevant as market conditions change. Regular refreshes with validation against new incrementality tests create a self-correcting system that builds trust through demonstrated accuracy over time.
Implementation Timeline
<table>
<thead>
<tr>
<th>Week</th>
<th>Primary Activities</th>
<th>Outputs</th>
</tr>
</thead>
<tbody>
<tr>
<td>1-2</td>
<td>Data collection begins, geo test design</td>
<td>Test plan, initial data extract</td>
</tr>
<tr>
<td>3-6</td>
<td>Geo tests running, complete data integration</td>
<td>Data pipeline, ongoing experiment</td>
</tr>
<tr>
<td>7-8</td>
<td>Tests conclude, model building, calibration</td>
<td>Validated incrementality results, calibrated model</td>
</tr>
<tr>
<td>9</td>
<td>Model testing and validation</td>
<td>Stability assessment, confidence intervals</td>
</tr>
<tr>
<td>10</td>
<td>Stakeholder presentation</td>
<td>Documented consensus, implementation plan</td>
</tr>
<tr>
<td>11-12</td>
<td>Automation setup, dashboard creation</td>
<td>Ongoing measurement system</td>
</tr>
</tbody>
</table>
Common Implementation Pitfalls
These specific challenges frequently derail MMM implementations:
Missing incrementality validation
Problem: Without experimental validation, stakeholders question model outputs that contradict their established metrics
Solution: Always run parallel validation tests before presenting MMM results
Incomplete data integration
Problem: Unexplained variance undermines result credibility
Solution: Document all marketing activities and external factors affecting performance
Single model reliance
Problem: One model version creates binary "right/wrong" stakeholder reactions
Solution: Use ensemble approaches with confidence intervals to acknowledge measurement uncertainty
Delayed implementation
Problem: Extended setup periods create pressure for immediate results without validation
Solution: Use the parallel workflow with geographic sales data to show early progress
Concealed limitations
Problem: Undisclosed model weaknesses destroy trust when discovered
Solution: Document all limitations transparently during implementation
Case Example: Trust Building In Action
A D2C brand implemented this process with these specific results:
Initial geo incrementality tests showed Facebook ROAS at 3.2x vs. platform-reported 8.7x
Google Search true ROAS measured at 5.1x vs. reported 10.3x
Initial MMM showed Facebook at 2.9x and Google at 4.8x
After calibration, model outputs aligned with experimental results within 0.2x
Stakeholders accepted MMM findings when supported by experimental data
Monthly refreshes narrowed confidence intervals by 37% over first quarter
Implemented budget shifts based on MMM recommendations improved overall ROAS by 21%
Through this methodical process, the brand transitioned from resistance to adoption, and ultimately to measured performance improvements through data-driven budget allocation.
Getting Started
To implement this trust-building framework:
Begin with geographical sales data extraction
Design incrementality tests for your top channels
Build data integration pathways while tests run
Use experimental results to calibrate your model
Present comparative findings to build stakeholder consensus
Establish monthly refresh cycles with ongoing validation
Document accuracy improvements over time
This process transforms skepticism into confidence through methodical validation rather than theoretical persuasion.
