Sharing Results with Stakeholders
Overview
Effectively communicating marketing mix modeling results requires tailoring your presentation to your audience's technical background, business priorities, and decision-making needs. This guide provides strategies for sharing MixModeler outputs with different stakeholder groups.
Know Your Audience
Executive Leadership
Priorities: Bottom-line impact, strategic direction, budget allocation
Preferred Format: High-level summary, visual charts, clear recommendations
Technical Level: Minimal statistics, focus on business implications
Key Questions:
Which channels drive the most revenue?
Where should we invest more/less?
What's the expected ROI of budget reallocation?
Marketing Teams
Priorities: Channel performance, campaign optimization, tactical decisions
Preferred Format: Detailed attribution, channel comparisons, time-series trends
Technical Level: Moderate - understand marketing metrics, some statistics
Key Questions:
How do my channels compare to each other?
Which campaigns performed best?
What's the optimal budget split?
Finance Teams
Priorities: ROI, cost efficiency, budget justification
Preferred Format: Numerical tables, cost-benefit analysis, variance explanations
Technical Level: High numerical literacy, less marketing context
Key Questions:
What's the ROI of each channel?
Can we justify current marketing spend levels?
Where can we cut costs with minimal impact?
Data Science / Analytics Teams
Priorities: Methodology, statistical validity, model quality
Preferred Format: Full technical details, diagnostics, assumptions, limitations
Technical Level: High - understands statistics, modeling, diagnostics
Key Questions:
Did the model pass diagnostic tests?
What are the assumptions and limitations?
How robust are the results?
Preparing Exports for Different Audiences
For Executives
Create Summary Excel:
Export full model to Excel
Add new "Executive Summary" sheet at the beginning
Include only:
Top 5-10 most important coefficients
Simple ROI calculations
Clear recommendations
Use visual formatting (colors, bold, conditional formatting)
Add 1-2 key charts (channel contribution pie chart, ROI bar chart)
Example Summary Sheet:
TV
$50,000
3.25
6.5x
Increase
Digital
$30,000
2.18
7.3x
Increase
$20,000
0.45
2.3x
Maintain
Radio
$15,000
0.12
0.8x
Reduce
Talking Points:
"Our analysis shows digital and TV deliver the highest ROI"
"We recommend shifting $5K from radio to digital for 15% revenue increase"
"Model explains 85% of revenue variation, very strong fit"
For Marketing Teams
Create Marketing Dashboard:
Export with decomposition included
Focus on Group Decomposition sheet
Create time-series charts showing:
Each channel's contribution over time
Seasonal patterns
Performance trends
Add channel comparison tables
Include variable-level details for campaign analysis
Example Marketing View:
Stacked area chart of contributions over time
Table of average weekly contribution by channel
Campaign-level performance (if using campaign variables)
Optimization recommendations with specific spend allocations
Talking Points:
"TV drives 35% of attributed revenue on average"
"Digital performance improved 20% in Q4 vs Q3"
"We see clear seasonality in December - 40% higher effectiveness"
For Finance Teams
Create Finance Package:
Export full model
Highlight Model Statistics sheet
Create ROI calculation sheet:
Coefficient × average spend / average KPI
Cost per incremental unit (revenue, conversion, etc.)
Marginal ROI calculations
Add variance analysis
Include sensitivity scenarios
Example ROI Sheet:
TV
$50,000
3.25
$162,500
$0.31
325%
Digital
$30,000
2.18
$65,400
$0.46
218%
Talking Points:
"Every dollar in TV generates $3.25 in revenue"
"Model R² of 0.85 means highly reliable estimates"
"Reallocating 20% of print budget to digital projects +$12K revenue/month"
For Technical Teams
Provide Complete Package:
Full Excel export (all sheets)
PDF diagnostic reports for all tests
Documentation of:
Data sources and preparation
Variable transformations applied
Prior specifications (if Bayesian)
Model selection rationale
Code/methodology notes
Include:
Diagnostic test results
Convergence metrics (Bayesian)
Residual analysis
Multicollinearity assessment
Assumptions and limitations
Talking Points:
"Model passes all diagnostic tests (normality, autocorrelation, heteroscedasticity)"
"VIF values all below 5, no multicollinearity concerns"
"Bayesian model converged (R-hat < 1.01, ESS > 1000)"
Creating Effective Presentations
PowerPoint Structure
Slide 1: Executive Summary
Key findings (3-5 bullets)
Top recommendation
Expected business impact
Slide 2: Methodology Overview (1 slide only)
What is MMM (2-3 sentences)
Data used (time period, channels included)
Model quality (R², sample size)
Slide 3-4: Channel Performance
Bar chart of coefficients or ROI by channel
Table with key metrics
Performance ranking
Slide 5-6: Attribution Over Time
Stacked area chart from decomposition
Trend insights
Seasonal patterns
Slide 7: Recommendations
Specific actions (increase X, decrease Y)
Expected impact with numbers
Implementation timeline
Slide 8: Q&A / Appendix
Technical details
Methodology notes
Diagnostic results
Visualization Best Practices
Use Bar Charts for comparing channels:
Simple, clear comparison
Easy to rank performance
Intuitive for non-technical audiences
Use Stacked Area Charts for attribution over time:
Shows contribution dynamics
Reveals seasonal patterns
Communicates total and breakdown simultaneously
Use Pie Charts sparingly:
Overall attribution share
Budget allocation
Only when 3-7 categories
Avoid:
Complex scatter plots (unless technical audience)
Statistical diagnostic charts (appendix only)
3D charts (harder to read)
Color Coding Strategy
Consistent Channel Colors:
Assign each channel a color
Use same colors across all charts
Match decomposition group colors in MixModeler
Example Color Scheme:
TV: Blue
Digital: Orange
Print: Green
Radio: Red
Base/Other: Gray
Performance Indicators:
Green highlight: High performers, increase budget
Yellow highlight: Moderate performers, maintain
Red highlight: Low performers, reduce budget
Storytelling with Data
Structure Your Narrative
1. Set Context
"We analyzed 2 years of data across 8 marketing channels"
"Goal: Understand which channels drive revenue most effectively"
2. Present Findings
"Model shows 85% of revenue variation explained by marketing activities"
"Top 3 channels: Digital (7.3x ROI), TV (6.5x ROI), Events (5.8x ROI)"
3. Reveal Insights
"Digital effectiveness increased 40% after campaign refresh in Q3"
"TV has strong holiday seasonality - 60% more effective in Q4"
4. Make Recommendations
"Shift $50K from low-ROI print to high-ROI digital"
"Increase TV spend 20% during Q4 holiday season"
5. Quantify Impact
"Expected revenue increase: $180K annually"
"Improves overall marketing ROI from 4.2x to 5.1x"
Use Analogies and Metaphors
For Coefficients:
"For every dollar spent on TV, we generate $3.25 in revenue - like a 225% return on investment"
For Adstock:
"TV advertising is like a wave - the effect builds over 3-4 weeks, then gradually fades"
For Saturation:
"Digital ads show diminishing returns - doubling spend doesn't double results"
For Model Fit:
"The model explains 85% of revenue changes - like having an 85% accurate crystal ball"
Addressing Common Questions
"How accurate is this?"
Answer:
"The model R² of 0.85 means it explains 85% of revenue variation"
"We validated results with statistical tests - all passed"
"Typical prediction error is ±8%, well within acceptable range"
"Why didn't you include [X channel]?"
Answer:
"We included all channels with consistent, reliable data"
"Channels with limited data or recent launches analyzed separately"
"Can add new channels in next model iteration as data accumulates"
"These ROI numbers seem high/low"
Answer:
"ROI reflects incremental impact, not total impact"
"Numbers align with industry benchmarks for [sector]"
"Results validated against actual spend and revenue data"
"Can we trust the recommendations?"
Answer:
"Model passed all diagnostic tests for statistical validity"
"Results consistent across multiple model specifications"
"Recommend testing with gradual budget shifts, monitor results"
"What about external factors?"
Answer:
"Model includes seasonality, trends, and control variables"
"Isolates marketing impact from other business drivers"
"Regular updates will capture changing market conditions"
Handling Sensitive Information
What to Share Externally
Safe to Share:
Relative channel performance (rankings)
ROI ratios and multiples
Model methodology and approach
Directional recommendations
Redact Before Sharing:
Absolute spend amounts
Actual revenue numbers
Specific coefficients (if proprietary)
Competitive intelligence
Creating Anonymized Versions
Technique 1: Percentages
Convert absolute spend to % of total
Report relative contributions
Use indexed values (base year = 100)
Technique 2: Ratios Only
Report ROI multiples
Share efficiency metrics
Provide performance rankings
Technique 3: Illustrative Scenarios
"If we shift 10% of budget from Channel A to Channel B..."
Use hypothetical numbers that preserve insights
Follow-Up and Action
Schedule Review Meeting
Within 1 Week: Present findings
Within 2 Weeks: Finalize recommendations
Within 1 Month: Begin implementation
Quarterly: Review results, update model
Document Decisions
Create Decision Log:
What was recommended
What was decided
Rationale for any deviations
Expected vs actual results (track over time)
Enable Self-Service
For Ongoing Questions:
Share annotated Excel export
Provide one-page summary
Create FAQ document
Offer follow-up session
Best Practices Summary
Tailor Content: Match detail level to audience technical sophistication
Lead with Insights: Business implications first, methodology second
Use Visuals: Charts communicate faster than tables
Tell a Story: Context → Findings → Insights → Recommendations → Impact
Be Transparent: Share limitations, assumptions, and uncertainty
Quantify Impact: Always translate findings into business metrics
Enable Action: Clear, specific, prioritized recommendations
Follow Through: Track implementation, measure results, iterate
Next Steps: Explore Model Reimport to reload models for future analysis, or review Excel Export Features for creating custom stakeholder reports.
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