# Split by Date

### Isolating Specific Time Periods

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:**

```
TV_Spend_Holiday_Campaign (Nov-Dec only)
TV_Spend_Baseline (all other months)
```

**Model:**

```
Sales ~ TV_Holiday + TV_Baseline
```

**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:**

```
Digital_Pre_Change (before event)
Digital_Post_Change (after event)
```

**Model:** Test if digital effectiveness changed

***

#### Scenario 3: Test vs. Control Periods

**Problem:** Ran experiment during specific period

**Example:**

```
TV_Test_Period (Weeks 20-32)
TV_Control_Period (all other weeks)
```

**Analysis:** Did test period show different effectiveness?

***

#### Scenario 4: Seasonal Effectiveness

**Problem:** Channel may work differently by season

**Example:**

```
Radio_Summer
Radio_Winter
```

**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**

```
Start Date: 2023-11-01
End Date: 2023-12-31
Name: TV_Spend_Holiday_2023
```

**Option B: Custom Conditions**

```
Include weeks where: Holiday = 1
Name: TV_Spend_Holiday_Weeks
```

**Step 4:** Preview split (see which periods included)

**Step 5:** Create variable

***

#### Multiple Splits

**Create complementary variables:**

1. **First split:** `TV_Q4` (Oct-Dec)
2. **Second split:** `TV_NonQ4` (Jan-Sep)

**Together:** Cover entire time period

**Model:** Compare coefficients

***

### Naming Convention

**Recommended formats:**

```
{Variable}_{Period}
{Variable}_{Condition}

Examples:
TV_Spend_Q4
TV_Spend_2024
Digital_Holiday_Campaign
Radio_Summer_Months
TV_Post_Rebrand
```

***

### Interpretation

#### Separate Coefficients

**Model:**

```
Sales = β₀ + β₁×TV_Q4 + β₂×TV_NonQ4
```

**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:**

```
TV_Before_Rebrand
TV_After_Rebrand
```

**If β\_after > β\_before:** Rebrand improved TV effectiveness

***

#### Seasonal Quarters

**Create 4 variables:**

```
TV_Q1, TV_Q2, TV_Q3, TV_Q4
```

**Model all simultaneously:** See which quarter has highest ROI

***

#### Campaign Flight Isolation

**Isolate 6-week campaign:**

```
TV_Campaign_Flight (Weeks 15-20)
TV_Baseline (all other weeks)
```

**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**

```
1. Split: TV_Holiday (Nov-Dec only)
2. Apply adstock: TV_Holiday_ads60
3. Use in model
```

**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!