Group-Level Analysis & Variable-Level Drilldown

Group Decomposition Overview

Purpose: Drill into specific contribution groups to see individual variable performance within that group.

Use Case: After seeing "Media" is a major contributor in main decomposition, drill down to compare TV vs. Digital vs. Radio performance.

Running Group Decomposition

Step 1: Select Group

• In Decomposition page, below main charts

• Dropdown shows all groups from main decomposition

• Choose group to analyze (e.g., "Media")

Step 2: Click "Run Group Decomposition"

• Calculates individual variable contributions within selected group

• Generates variable-level chart

• Shows only variables assigned to that group

Step 3: Analyze Results

• Stacked bars show individual variables

• Black line shows total group contribution

• Each variable has distinct color/shade

Group Decomposition Chart

Structure:

• Similar to main decomposition but variable-level

• Each variable within the group shown separately

• Sum of variable contributions = total group contribution

Chart Elements:

Stacked Bars:

• One bar per time period

• Each segment = one variable's contribution

• Height of bar = total group contribution

Total Line (Black):

• Shows overall group contribution from main decomposition

• Bars should sum to this line

• Validates calculation

Colors:

• Each variable has unique color

• Distinct from other variables in group

• Auto-assigned or customizable

Interpreting Variable-Level Results

Identify Top Performers:

Media Group Drill-Down:
├── TV: $50,000 (50% of Media)
├── Digital: $30,000 (30% of Media)
├── Radio: $15,000 (15% of Media)
└── Print: $5,000 (5% of Media)
Total Media: $100,000

Insights:

• TV dominates media contribution (50%)

• Digital is second (30%)

• Print contributes minimally (5%)

Performance Comparison:

• Which specific channels drive the group?

• Are all channels contributing proportionally?

• Any underperformers to cut or optimize?

Timing Analysis:

• Do all variables spike together?

• Or do they have different timing?

• Campaign coordination visible?

Common Group Drill-Down Scenarios

Scenario 1: Media Group Analysis

Question: "Within Media, which channels perform best?"

Process:

1. Run main decomposition → Media is top contributor

2. Select "Media" group in dropdown

3. Run group decomposition

4. Compare TV, Digital, Radio, Print, etc.

Typical Findings:

• TV often largest absolute contributor

• Digital often highest ROI

• Some channels underperform

• Reallocation opportunities identified

Scenario 2: Digital Channel Mix

Question: "Within Digital, is Search or Social more effective?"

Process:

1. Ensure Search, Social, Display assigned to "Digital" group

2. Select "Digital" in group dropdown

3. Run group decomposition

4. Compare individual digital channels

Typical Findings:

• Search drives consistent baseline

• Social creates spikes during campaigns

• Display supports other channels

• Retargeting has high efficiency

Scenario 3: Promotional Analysis

Question: "Which types of promotions drive most lift?"

Process:

1. Group promotional variables together

2. Select "Promotions" group

3. Run group decomposition

4. Compare different promotion types

Typical Findings:

• Deep discounts drive volume but hurt margins

• Limited-time offers create urgency

• Free shipping has consistent impact

• BOGO has highest incremental lift

Variable-Level ROI Calculation

From group decomposition data:

Get variable contributions:

• Sum each variable's contribution across time

• From group decomposition chart or export

Match with spend:

• Get actual spend for each variable

• Same time periods

Calculate individual ROI:

TV ROI = (Total TV Contribution / Total TV Spend) - 1
Digital ROI = (Total Digital Contribution / Total Digital Spend) - 1

Rank by performance:

• Sort channels by ROI

• Identify best and worst performers

• Guide budget reallocation

Example:

Channel | Contribution | Spend | ROI
TV      | $600,000     | $300,000 | 100%
Digital | $400,000     | $150,000 | 167%
Radio   | $200,000     | $100,000 | 100%
Print   | $50,000      | $50,000  | 0%

Insight: Digital has highest ROI (167%), Print breaks even
Recommendation: Shift budget from Print to Digital

Multi-Level Drill-Down Strategy

Level 1: Overall Model

• Total KPI decomposition

• All groups visible

• Strategic overview

Level 2: Group Analysis

• Drill into major groups

• Compare related channels

• Tactical insights

Level 3: Individual Campaigns (if modeled)

• Specific campaign performance

• Campaign vs. always-on

• Granular optimization

Workflow:

1. Start broad (all groups)

2. Identify top contributors

3. Drill into those groups

4. Analyze variable-level

5. Make specific recommendations

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OLS vs Bayesian Decomposition

Conceptual Difference

OLS Decomposition:

• Uses point estimates from OLS regression

• Single coefficient value per variable

• Deterministic contributions

• Frequentist approach

Bayesian Decomposition:

• Uses posterior mean from Bayesian inference

• Based on MCMC samples

• Incorporates prior information

• Bayesian approach

Both produce the same chart format - the difference is which coefficient values are used.

When to Use Each

Use OLS Decomposition When:

Standard Analysis:

• Default approach for most MMM

• Quick and straightforward

• Well-understood by stakeholders

Sufficient Data:

• Large sample size (50+ observations)

• Strong signal-to-noise ratio

• Clear relationships

Speed Matters:

• Need results quickly

• Iterative model building

• Exploratory analysis

Simple Reporting:

• Stakeholders prefer point estimates

• Less need to explain methodology

• Focus on actionable insights

Use Bayesian Decomposition When:

Limited Data:

• Small sample sizes

• Noisy data

• Weak signals

Prior Knowledge:

• Strong beliefs about effects

• Industry benchmarks available

• Historical estimates informative

Uncertainty Quantification:

• Need credible intervals

• Risk assessment important

• Conservative estimates preferred

Hierarchical Effects:

• Regional models

• Multi-product models

• Partial pooling beneficial

Comparing OLS and Bayesian Results

Expected Differences:

With Non-Informative Priors:

• Results very similar

• Bayesian often slightly more conservative

• Practical differences minimal

With Informative Priors:

• Bayesian pulls estimates toward priors

• More stable coefficients

• Reduced extreme values

With Limited Data:

• Bayesian handles better

• OLS may overfit

• Bayesian shrinkage helps

Running Both for Comparison

Process:

1. Fit both OLS and Bayesian models

2. Run decomposition with OLS coefficients

3. Run decomposition with Bayesian coefficients

4. Compare results

What to Check:

Contribution Magnitudes:

• Are they similar?

• Which variables show largest differences?

• Do business conclusions change?

ROI Rankings:

• Same channel ranking?

• Or different priorities?

• Material impact on decisions?

Temporal Patterns:

• Same spikes and valleys?

• Timing aligned?

• Seasonal patterns consistent?

Interpreting Differences

Small Differences (<10%):

• Both methods agree

• Robust findings

• Proceed with confidence

Moderate Differences (10-30%):

• Some uncertainty present

• Investigate why (data, priors)

• Consider both estimates

• Report range

Large Differences (>30%):

• Significant disagreement

• Check model specification

• Review priors if Bayesian

• Investigate data quality

• May need more data

Reporting Considerations

For OLS:

• "Point estimate decomposition"

• "Based on OLS regression"

• Standard confidence intervals

For Bayesian:

• "Bayesian posterior mean decomposition"

• "Incorporating prior information"

• Credible intervals available

When Showing Both:

• "Range of estimates"

• "OLS vs. Bayesian comparison"

• "Sensitivity analysis"

Technical Details

OLS Contribution Calculation:

Contribution_OLS = β_OLS × X
Where β_OLS is from OLS regression

Bayesian Contribution Calculation:

Contribution_Bayesian = E[β|Data] × X
Where E[β|Data] is posterior mean from MCMC

Both sum to predicted value:

• ∑Contributions_OLS = Predicted_OLS

• ∑Contributions_Bayesian = Predicted_Bayesian

Best Practices

Start with OLS:

• Faster iteration

• Build intuition

• Establish baseline

Add Bayesian When:

• OLS results unstable

• Need uncertainty quantification

• Have strong priors

• Data is limited

Compare Both:

• Validate findings

• Understand sensitivity

• Build confidence

• Inform decisions

Document Choice:

• Explain why OLS or Bayesian

• Note key differences

• Justify for stakeholders

Common Questions

Q: Which is more accurate? A: Neither is universally more accurate - depends on data and context. Bayesian can be more accurate with good priors and limited data.

Q: Can I use both? A: Yes, running both provides sensitivity analysis and validates findings.

Q: Do they give different ROI? A: Yes, potentially. But with sufficient data and non-informative priors, differences are usually small.

Q: Which should I present to executives? A: OLS is typically simpler to explain. Use Bayesian if you need to quantify uncertainty.

Q: How do I know if differences matter? A: If business decisions would change, differences matter. If same actions result, differences are academic.

Summary

Key Takeaways:

Group Decomposition:

• Essential for understanding channel-level performance

• Enables detailed ROI calculation

• Supports tactical optimization

• Drill-down reveals variable-level insights

OLS vs. Bayesian:

• Both valid approaches

• OLS is default, Bayesian for special cases

• Small differences usually don't matter

• Large differences warrant investigation

• Document and justify choice

Workflow Integration:

1. Run main decomposition (OLS or Bayesian)

2. Identify top contributing groups

3. Drill into those groups

4. Calculate variable-level ROI

5. Compare across methods if needed

6. Make data-driven decisions