🤔 What Is Multicollinearity?
Simple Definition: When your marketing channels move together, making it impossible to tell which one actually drives sales.
🌧️ The Umbrella Problem:
When it rains, both umbrella sales AND raincoat sales increase. Which product keeps people dry? You can't tell - they both go up because of the rain!
In Marketing: During Black Friday, TV ads, Facebook ads, and Google ads all increase. Sales go up 300%. Which channel worked? You can't tell - they all increased together!
The Technical Bit (Keep It Simple)
Multicollinearity happens when the correlation between marketing channels is too high:
TV ↔️ Radio
Correlation: 0.3
✅ Good
Facebook ↔️ Instagram
Correlation: 0.95
❌ Problem!
📈 See It In Your Data
This is what multicollinearity looks like in real marketing data:
Marketing Spend Over Time - Spot the Problem!
What You're Seeing:
• TV and Radio move together almost perfectly (correlation = 0.92)
• Facebook and Instagram are practically identical (correlation = 0.95)
• Search has its own pattern (correlation < 0.5 with others)
The Problem: Your model can't tell if TV or Radio drives sales - they're too similar!
❌ Without Fixing (Standard Regression)
What happens:
- TV gets credit: $5 ROI
- Radio gets credit: -$2 ROI (negative!)
- Makes no sense!
✅ After Fixing (With Solutions)
What happens:
- TV gets credit: $2.5 ROI
- Radio gets credit: $1.8 ROI
- Both positive and reasonable!
🎯 Why Does This Happen in Marketing?
| Scenario |
What Happens |
Correlation Level |
| Holiday Seasons |
All channels increase for Black Friday, Christmas |
Very High (90%+) |
| Product Launch |
TV, Digital, PR all activate together |
High (80%+) |
| Budget Cycles |
Q4 budget = everything increases |
Medium-High (70%+) |
| Platform Bundles |
Facebook & Instagram bought together |
Very High (95%+) |
| Agency Packages |
TV + Radio in same media plan |
High (75%+) |
⚠️ Problems This Causes
1. Wrong Attribution
Your model might say: "TV drives 80% of sales, Facebook drives -10%"
Reality: Both probably help, but the model can't separate them!
2. Unstable Results
Monday's model: "Google Ads ROI = $5"
Tuesday's model: "Google Ads ROI = $0.50"
(Same data, just added one week!)
3. Bad Decisions
Model says: "Cut Facebook, it's not working"
You cut Facebook → Sales drop 40% 😱
(Facebook and Instagram were grouped in the data!)
4. Wasted Budget
You keep investing in channels that seem good in the model but aren't actually driving incremental sales.
🔍 How to Detect It
Quick Checks (No Math Needed)
- 📈 Look at spending graphs: Do channels move together like synchronized swimmers?
- 🎲 Check model stability: Do results change wildly with small data updates?
- ❌ Spot nonsense: Any negative ROI for obviously good channels?
- 📊 Compare models: Different models giving completely different answers?
Simple Metrics
| Metric |
What It Means |
Good |
Warning |
Bad |
| Correlation |
How similar two channels are |
< 0.7 |
0.7 - 0.85 |
> 0.85 |
| VIF Score |
Multicollinearity measure |
< 5 |
5 - 10 |
> 10 |
💡 Quick Tip: If you can predict one channel's spending by looking at another (like "TV is always 2x Digital"), you have multicollinearity!
✅ Simple Solutions
Solution 1: Remove Similar Channels
What: Keep Facebook, remove Instagram (they're 95% similar)
When: Channels are nearly identical
Pros: Simple, immediate fix
Cons: Lose some information
Solution 2: Combine Into Groups
What: Create "Social Media" = Facebook + Instagram + TikTok
When: Channels naturally belong together
Pros: Keeps all data, logical grouping
Cons: Less granular insights
Example Groupings:
• "Traditional" = TV + Radio + Print
• "Digital Performance" = Search + Shopping
• "Social" = Facebook + Instagram + TikTok
• "Video" = YouTube + Connected TV
Solution 3: Run Tests (Break the Correlation)
What: Turn off TV in half your markets for 4 weeks
When: Need to know true incremental impact
Pros: Gets true causal effect
Cons: Requires testing, might lose sales
Testing Ideas:
• Geo tests: Different spend by region
• Time tests: Stagger campaign launches
• On/Off tests: Pulse channels on and off
Solution 4: Stagger Campaigns
What: Launch TV week 1, Digital week 3, Email week 5
When: Planning future campaigns
Pros: Creates natural variation
Cons: May not be optimal for business
🚀 Advanced Solutions (Still Simple!)
Regularization - The "Sharing Credit" Approach
🎯 What is Regularization?
Simple Explanation: Instead of giving all credit to one channel, regularization forces the model to share credit fairly among correlated channels.
Think of it like this:
Without Regularization
Like having 3 kids who cleaned the room together, but only one gets all the allowance money.
- TV: $10 (gets everything)
- Radio: -$2 (negative!)
- Print: $0 (nothing)
With Regularization
Like fairly splitting the allowance among all kids who helped.
- TV: $4 (fair share)
- Radio: $3 (fair share)
- Print: $1 (fair share)
Ridge Regression (L2 Regularization)
What it does: "Shrinks" all coefficients toward each other
When to use: When you want to keep all channels but make results stable
Real example: Netflix uses this because TV and streaming ads correlate highly
Before Ridge: TV = $8 ROI, YouTube = -$1 ROI
After Ridge: TV = $3.5 ROI, YouTube = $2.8 ROI
Both positive and reasonable!
Lasso Regression (L1 Regularization)
What it does: Automatically removes redundant channels (sets them to zero)
When to use: When you have many channels and want automatic selection
Real example: Amazon uses this to pick from 50+ marketing channels
Before Lasso: 15 channels with confusing coefficients
After Lasso: 6 main channels identified, others set to zero
Cleaner and easier to interpret!
Elastic Net (Best of Both)
What it does: Combines Ridge and Lasso - shares credit AND removes redundant channels
When to use: When you're not sure which approach is better
Real example: Uber uses this for their global marketing mix
Residualization - The "Step-by-Step" Approach
🔄 What is Residualization?
Simple Explanation: Like peeling an onion - you analyze one channel first, remove its effect, then analyze what's left.
How it works:
- Step 1: Measure TV's impact on sales
- Step 2: Remove TV's effect from the data
- Step 3: Now measure Radio's impact on what's left
- Step 4: Continue for other channels
Restaurant Example:
1. TV brings people to the restaurant (awareness)
2. After removing TV effect, Email drives repeat visits (retention)
3. After removing Email effect, Social drives word-of-mouth (advocacy)
Each channel's unique contribution becomes clear!
When to Use Residualization
Perfect for: Channels with clear hierarchy or sequence
Example hierarchy:
• TV/Radio → General awareness
• Search/Social → Consideration
• Email/Retargeting → Conversion
⚠️ Important: The order matters! Analyze broader channels first, then specific ones.
Quick Comparison Guide
| Method |
Best For |
Pros |
Cons |
| Ridge |
Keeping all channels |
Stable, fair credit sharing |
Keeps redundant channels |
| Lasso |
Many channels (20+) |
Auto-selects important ones |
Might drop useful channels |
| Elastic Net |
Unsure which to use |
Best of both worlds |
More complex to tune |
| Residualization |
Clear channel hierarchy |
Shows unique contribution |
Order dependent |
💼 Real-World Examples
🛍️ E-commerce Company
Problem: Black Friday - all channels spiked 400%
Solution: Used Ridge regression + grouped digital channels
Result: Found email was 2x more effective than model showed
🥤 Beverage Brand
Problem: TV and YouTube ads had 0.92 correlation
Solution: Residualization - analyzed TV first, then YouTube on remainder
Result: TV drove awareness (upper funnel), YouTube drove purchase (lower funnel)
🚗 Auto Company
Problem: 15 digital channels all correlated
Solution: Lasso regression automatically selected 5 key channels
Result: Simplified from 15 to 5 channels, improved ROI by 30%
🏨 Hotel Chain
Problem: Seasonal patterns made everything correlate
Solution: Elastic Net + seasonality adjustment
Result: Separated true channel effects from seasonal patterns
📋 Quick Reference Guide
If You See This...
| Symptom |
Likely Cause |
Quick Fix |
| Negative ROI for email |
Email correlates with another channel |
Use Ridge regression |
| TV gets all the credit |
TV correlates with everything |
Use regularization or residualization |
| Results change daily |
Severe multicollinearity |
Use Elastic Net |
| Too many channels (20+) |
Information overload |
Use Lasso to auto-select |
| Clear funnel stages |
Sequential customer journey |
Use residualization |
Decision Tree
Is correlation > 0.9?
↓ YES → Try Ridge first, then remove if needed
↓ NO → Do you have 10+ channels?
↓ YES → Use Lasso or Elastic Net
↓ NO → Clear channel hierarchy?
↓ YES → Use residualization
↓ NO → Use Ridge regression
🎯 Best Practices
Prevention is Better Than Cure
- 📅 Plan variety: Don't always increase all channels together
- 🧪 Build in tests: Regular on/off periods for channels
- 📊 Track unique metrics: Each channel should have its own KPI
- 🔄 Rotate emphasis: Focus on different channels each quarter
- 📈 Vary spend levels: Not everything at 0% or 100%
When Building Models
- ✅ Always check correlation matrix first
- ✅ Calculate VIF before modeling
- ✅ Try multiple approaches (Ridge, Lasso, Groups)
- ✅ Compare regularized vs non-regularized results
- ✅ Validate with holdout tests
- ✅ Use business logic to sense-check results
⚠️ Remember: A model that says "TV drives everything" or "Digital has negative ROI" is probably suffering from multicollinearity. Don't make big decisions based on these results!
🔑 Key Takeaways
The 7 Things to Remember:
- It's Common: Every company faces this with holiday campaigns, launches, etc.
- It's Dangerous: Can lead to completely wrong budget decisions
- It's Detectable: Look for channels that move together
- It's Fixable: Multiple solutions from simple to advanced
- Regularization Helps: Ridge/Lasso share credit fairly
- Residualization Clarifies: Shows each channel's unique contribution
- It's Preventable: Design campaigns with variation in mind
Your Action Plan
Step 1: Check your last campaign - did all channels increase together?
Step 2: Calculate correlation between your top channels
Step 3: If correlation > 0.8, try Ridge regression first
Step 4: If you have many channels, try Lasso to simplify
Step 5: Plan your next campaign with staggered launches
Step 6: Set up a test to validate your model results