Variable Workshop Overview

Creating and Transforming Variables for Better Models

The Variable Workshop is your central hub for engineering new variables from your raw data. This powerful feature lets you create transformations that capture real-world marketing behaviors like carryover effects, diminishing returns, and time-shifted impacts—all without writing code.

What is Variable Engineering?

Beyond Raw Data

Raw marketing data often needs transformation to capture:

  • Carryover effects: TV ads continue influencing sales for weeks

  • Diminishing returns: First $10K has more impact than next $10K

  • Time delays: Marketing in Week 1 affects sales in Week 2-3

  • Interaction effects: TV + Digital together more effective than sum of each

  • Seasonal adjustments: Normalize for time-of-year patterns

Variable Workshop creates these transformations automatically.

Why Transform Variables?

Problem: Linear Models Miss Reality

If you model raw spend:

Sales = β₀ + β₁ × TV_Spend

You're assuming: ❌ Impact is immediate (no carryover) ❌ Returns are constant (no saturation) ❌ Each dollar has same effect (linear)

Reality: ✅ Impact persists over time ✅ Returns diminish with more spend ✅ Relationship is non-linear

Solution: Transformed Variables

With transformations:

Sales = β₀ + β₁ × f(TV_Spend_adstocked_saturated)

Now capturing: ✅ Carryover effects (adstock) ✅ Diminishing returns (saturation) ✅ Realistic marketing response

Result: More accurate attribution, better ROI estimates, actionable insights

Variable Workshop Interface

Three Main Sections

1. Available Variables (Left Panel)

  • All variables from your uploaded data

  • Previously created transformations

  • Search and filter functionality

2. Transformation Builder (Center Panel)

  • Select transformation type

  • Configure parameters

  • Preview transformation effect

  • Name new variable

3. Variable Library (Right Panel)

  • All created variables

  • Edit or delete transformations

  • Add directly to models

  • Export list

Transformation Types Available

Time-Based Transformations

Lead/Lag Transformations Shift variables forward or backward in time

  • Use case: Model delayed effects

  • Example: TV_Spend_lag1 (last week's TV spend)

Split by Date Create separate variables for different time periods

  • Use case: Isolate campaign periods or seasonal changes

  • Example: TV_Spend_Q4_Only

Mathematical Transformations

Multiply Variables Combine two variables into one

  • Use case: Create interaction terms

  • Example: TV_x_Digital (synergy effect)

Weighted Variables (WGTD) Apply custom weights to variable values

  • Use case: Emphasize certain periods or adjust for inflation

  • Example: Sales_Weighted

Marketing-Specific Transformations

Adstock (Carryover Effects) Model how marketing impact decays over time

  • Use case: Capture lasting effects of TV, Radio, Print

  • Example: TV_Spend_ads60 (60% carryover rate)

Saturation Curves Model diminishing returns using S-shape or concave curves

  • Use case: Realistic ROI at different spend levels

  • Example: TV_Spend_ATAN_curve

Statistical Transformations

AVO (Average Value Optimization) Smooth volatile data using moving averages

  • Use case: Reduce noise in erratic variables

  • Example: Sales_AVO_4week

Standardization (STA) Scale variables to mean=0, std=1

  • Use case: Compare effect sizes across different units

  • Example: TV_Spend_Standardized

Basic Workflow

Step 1: Select Base Variable

Choose the variable you want to transform from your uploaded data

Step 2: Choose Transformation Type

Pick from the transformation menu (Lead/Lag, Adstock, Saturation, etc.)

Step 3: Configure Parameters

Set transformation settings:

  • Lag periods (1, 2, 3 weeks)

  • Adstock rate (30%, 50%, 70%)

  • Curve parameters (alpha, beta, gamma)

Step 4: Preview Transformation

Interactive chart shows:

  • Original variable (before)

  • Transformed variable (after)

  • Side-by-side comparison

Step 5: Create Variable

Click "Create" - new variable added to your library

Step 6: Use in Models

Add transformed variable to your models in Model Builder

Preview and Testing

Interactive Preview

Before creating a variable, see:

Visual Comparison:

  • Line chart: Original vs. Transformed values

  • Scatter plot: Transformation function shape

  • Statistics: Mean, range, variance changes

Example Preview:

Original TV_Spend: Mean = $18,500
Adstocked (60%):   Mean = $27,300 (accumulated effect)

Chart shows: Smoother line with peak values extended

Test Before Committing

Preview lets you:

  • Experiment with different parameters

  • See immediate visual feedback

  • Understand transformation impact

  • Avoid creating unusable variables

Iterate until satisfied, then create

Combining Transformations

Chained Transformations

You can apply multiple transformations sequentially:

Example: TV with Adstock AND Saturation

  1. Start: TV_Spend (raw)

  2. Apply adstock: TV_Spend_ads60

  3. Apply saturation: TV_Spend_ads60_ATAN

Order matters:

  • Usually: Adstock FIRST, then Saturation

  • Captures: Marketing accumulates over time, then saturates

Creating Complex Variables

Interaction Terms:

  1. Create adstocked TV: TV_ads60

  2. Create adstocked Digital: Digital_ads40

  3. Multiply them: TV_ads60_x_Digital_ads40

Result: Captures synergy between channels with proper carryover

Best Practices

Start Simple

Build Complexity Gradually:

  1. First: Use raw variables in OLS model (baseline)

  2. Then: Add adstock to media channels

  3. Next: Apply saturation curves

  4. Finally: Test interaction terms if needed

Don't over-engineer initially

Name Variables Clearly

Good Naming Convention:

{Base}_{Transformation}_{Parameter}

Examples:
TV_Spend_ads60
Digital_Display_lag2
Radio_ATAN_a15_p12
Sales_AVO_4week

Benefits:

  • Easy to identify transformations

  • Understand at a glance

  • Avoid confusion

Document Your Transformations

Keep Track:

  • Why transformation was applied

  • Parameter choices and rationale

  • Which models use which transformations

Use Variable Notes: Each created variable has optional notes field - use it!

Test Impact on Model

After creating transformation:

  1. Add to model in Model Builder

  2. Check coefficient significance (t-stat)

  3. Compare R² with/without transformation

  4. Verify makes business sense

If transformation doesn't improve model, don't use it

Variable Management

Editing Variables

Modify existing transformations:

  • Click variable in library

  • Adjust parameters

  • Preview changes

  • Update (replaces in all models using it)

Deleting Variables

Remove unused transformations:

  • Right-click variable → Delete

  • Confirmation dialog (can't undo)

  • Models using it will show warning

Best Practice: Export models before deleting variables

Exporting Variable List

Download Documentation:

  • All created variables

  • Transformation types

  • Parameters used

  • Creation dates

Format: Excel or CSV

Use Case: Documentation, sharing with team, reproducibility

Common Use Cases

Use Case 1: TV Campaign with Carryover

Scenario: TV ads continue influencing sales for 4-6 weeks

Solution:

  1. Create TV_Spend_ads60 (60% carryover)

  2. Test in model

  3. Compare with raw TV spend

  4. Use adstocked version (higher significance)

Use Case 2: Digital Diminishing Returns

Scenario: First $5K in digital has high ROI, next $5K less effective

Solution:

  1. Create Digital_ATAN_concave saturation curve

  2. Test parameters in Curve Testing

  3. Use saturated variable in model

  4. Calculate ROI at different spend levels

Use Case 3: Seasonal Campaign Isolation

Scenario: Holiday campaign Q4 only, want to measure separately

Solution:

  1. Create TV_Spend_Q4_Only (Split by Date)

  2. Use in model alongside TV_Spend_NonQ4

  3. Compare coefficients

  4. Understand seasonal vs. baseline effectiveness

Use Case 4: Lagged Price Effect

Scenario: Price changes take 2 weeks to affect sales

Solution:

  1. Create Price_lag2 (2-week lag)

  2. Test in model

  3. If significant, use instead of current price

  4. Better captures actual price-sales relationship

Advanced Features

Batch Creation

Create multiple transformations at once:

  • Select multiple base variables

  • Apply same transformation type

  • Configure once, create all

Example: Transform all media channels with 50% adstock in one step

Template Library

Save transformation configurations:

  • Store commonly used parameter sets

  • Apply to new variables quickly

  • Ensure consistency across models

Example Template: "Standard Media Adstock" = 60% rate, applied to TV/Radio/Print

Variable Dependencies

Track relationships:

  • See which variables are based on others

  • Understand transformation chains

  • Avoid circular dependencies

Visual Dependency Graph (coming soon)

Summary

Key Takeaways:

🛠️ Variable Workshop = Transformation Hub - create all variable engineering here

📊 Multiple transformation types - time-based, mathematical, marketing-specific

👁️ Preview before creating - see transformations visually before committing

🔗 Combine transformations - adstock + saturation for realistic models

📝 Clear naming - use descriptive names with transformation details

✅ Test in models - verify transformations improve fit and make sense

🎯 Start simple, add complexity - don't over-engineer initially

Next Steps: Explore each transformation type in detail in the following pages!

PreviousSubscription Limits & Variable CapsNextTransformation Types

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