Split by Date creates separate variables for different time periods, allowing you to model how effectiveness changes between periods or isolate specific campaigns.
What is Split by Date?
Creating Time-Specific Variables
Takes one variable and splits it into two (or more):
Original:
TV_Spend: Values for all 104 weeks
After Split:
TV_Spend_2023: Values for 2023 only (zeros elsewhere)
TV_Spend_2024: Values for 2024 only (zeros elsewhere)
Use Case: Test if TV effectiveness changed between years
When to Use Split by Date
Scenario 1: Campaign Period Isolation
Problem: Want to measure specific campaign separately from baseline
Example:
Model:
Result: Compare holiday vs. baseline TV ROI
Scenario 2: Structural Changes
Problem: Business changed significantly mid-period
Examples:
Product reformulation
Market expansion
New competitor entry
Pricing strategy shift
Solution:
Model: Test if digital effectiveness changed
Scenario 3: Test vs. Control Periods
Problem: Ran experiment during specific period
Example:
Analysis: Did test period show different effectiveness?
Scenario 4: Seasonal Effectiveness
Problem: Channel may work differently by season
Example:
Model: Compare seasonal ROI
Creating Split Variables
In Variable Workshop
Step 1: Select base variable to split
Step 2: Choose "Split by Date"
Step 3: Define split criteria:
Option A: Date Range
Option B: Custom Conditions
Step 4: Preview split (see which periods included)
Step 5: Create variable
Multiple Splits
Create complementary variables:
First split:TV_Q4 (Oct-Dec)
Second split:TV_NonQ4 (Jan-Sep)
Together: Cover entire time period
Model: Compare coefficients
Naming Convention
Recommended formats:
Interpretation
Separate Coefficients
Model:
Coefficients:
β₁ = 0.8 → TV in Q4 generates $0.80 per $1 spent
β₂ = 0.5 → TV in other quarters generates $0.50 per $1
Insight: Q4 TV is 60% more effective (seasonal boost)
Common Patterns
Before/After Analysis
Test structural change impact:
If β_after > β_before: Rebrand improved TV effectiveness
Seasonal Quarters
Create 4 variables:
Model all simultaneously: See which quarter has highest ROI
Campaign Flight Isolation
Isolate 6-week campaign:
Compare ROI: Was campaign more effective than baseline?
Best Practices
✅ Clear rationale for split - don't split arbitrarily
✅ Meaningful periods - business-driven boundaries (campaigns, seasons, events)
✅ Complementary splits - ensure full coverage (Period A + Period B = Total)
✅ Sufficient data in each split - at least 10-15 observations per period
✅ Test one split at a time - don't over-complicate initially
❌ Don't split too granularly - avoid 1-2 week periods (too sparse)
❌ Don't create too many splits - limit to 2-4 per variable
Combining with Other Transformations
Split THEN Transform
Example: Holiday TV with Adstock
Captures: Holiday campaign with proper carryover, isolated from baseline
Summary
Key Points:
📅 Splits variable by time period - creates period-specific variables
🎯 Use for campaigns, seasons, structural changes - test effectiveness differences
✂️ Complementary splits - Period A + Period B = complete coverage
📊 Separate coefficients - compare ROI across periods
💡 Business-driven boundaries - align with real campaigns or events
Powerful tool for understanding how marketing effectiveness varies across time!