Exporting Decomposition Data

Overview

Decomposition data can be exported to Excel for further analysis, custom visualizations, and stakeholder reporting. MixModeler includes decomposition sheets automatically in model exports.

Purpose: Access decomposition data outside MixModeler for advanced analysis, custom reporting, and integration with other tools.

How to Export

Step 1: Navigate to Model Library

  • Click "Model Library" in the left sidebar

  • View all your saved models

Step 2: Locate Your Model

  • Find the model you want to export in the table

  • Ensure it's the model you've run decomposition on

Step 3: Click Export Button

  • Click the 📁 Export icon for your model

  • Excel file downloads automatically to your browser's download folder

Step 4: Open Excel File

  • Locate downloaded file (usually named: ModelName_export.xlsx)

  • Open in Microsoft Excel or Google Sheets

Excel File Structure

The export includes multiple sheets:

Group Decomposition Sheet

Purpose: Time-series of group-level contributions

Columns:

  • Date: Time period (week, month, etc.)

  • Actual: Real KPI values from your data

  • Predicted: Model's predicted KPI values

  • Base: Base group contribution

  • Media: Media group contribution (if exists)

  • Price: Price group contribution (if exists)

  • Promotions: Promotions group contribution (if exists)

  • Seasonality: Seasonality group contribution (if exists)

  • [Other Groups]: Additional groups you've defined

Structure:

Date       | Actual  | Predicted | Base   | Media  | Price  | Promotions | Seasonality
2024-01-01 | 50000   | 48500     | 20000  | 18000  | -2000  | 8000       | 4500
2024-01-08 | 52000   | 51000     | 20000  | 20000  | -2000  | 9000       | 4000
2024-01-15 | 48000   | 47500     | 20000  | 17000  | -2000  | 8500       | 4000
...

Each row: One time period Each column: One contribution group

Variable Decomposition Sheet

Purpose: Time-series of individual variable contributions

Columns:

  • Date: Time period

  • Actual: Real KPI values

  • Predicted: Model predictions

  • const: Intercept/constant term

  • TV_Spend: TV variable contribution

  • Digital_Spend: Digital variable contribution

  • Price_Index: Price variable contribution

  • [All Variables]: Each variable in your model

Structure:

Date       | Actual  | Predicted | const  | TV_Spend | Digital_Spend | Price_Index | Holiday
2024-01-01 | 50000   | 48500     | 20000  | 12000    | 6000          | -2000       | 4500
2024-01-08 | 52000   | 51000     | 20000  | 13000    | 7000          | -2000       | 4000
...

Use: Most granular view, variable-level ROI calculation

Other Sheets Included

The export also contains:

  • Model Info: Model name, KPI, features, date range

  • Coefficients: OLS or Bayesian coefficient estimates

  • Model Statistics: R², Adjusted R², diagnostics

  • Residuals: Actual vs. predicted with residuals

  • Variable Details: Transformations, groups, adjustments

Using Exported Data

ROI Calculation in Excel

Step 1: Sum Contributions

In Excel:
=SUM(D2:D53)  // Sum TV_Spend column for 52 weeks
Result: $600,000

Step 2: Get Total Spend

From your marketing data:
TV total spend: $300,000

Step 3: Calculate ROI

In Excel:
=(Contribution - Spend) / Spend
=($600,000 - $300,000) / $300,000
=100% or 1.0

Creating Pivot Tables

Summarize by Time Period:

Step 1: Select Data

  • Highlight decomposition data

  • Insert > PivotTable

Step 2: Configure

  • Rows: Date (grouped by month/quarter)

  • Values: Sum of each group contribution

  • Show contribution by period

Step 3: Analyze

  • Monthly trends

  • Quarterly summaries

  • YoY comparisons

Example Output:

Quarter | Base    | Media   | Total
Q1      | 60000   | 50000   | 110000
Q2      | 60000   | 65000   | 125000
Q3      | 60000   | 55000   | 115000
Q4      | 60000   | 80000   | 140000

Custom Visualizations

Beyond MixModeler Charts:

Waterfall Charts:

  • Show contribution build-up

  • Period-over-period changes

  • Group contributions

Area Charts:

  • Stacked area showing trends

  • Proportion changes over time

  • Smoother than stacked bars

Combo Charts:

  • Bars for contributions

  • Line for actual/predicted

  • Multiple Y-axes

Heatmaps:

  • Contribution intensity by period

  • Identify patterns

  • Visual correlation

Time Series Analysis

Moving Averages:

In Excel:
=AVERAGE(B2:B14)  // 13-week moving average

Growth Calculations:

Week-over-week growth:
=(This Week - Last Week) / Last Week

Month-over-month:
=(This Month - Last Month) / Last Month

Trend Analysis:

Use Excel's TREND() function
Or add trendline to chart
Forecast future performance

Combining with Other Data

Join with Marketing Data:

Match by Date:

VLOOKUP or INDEX/MATCH to join
Combine decomposition with:
- Detailed spend data
- Campaign calendars
- External factors (weather, events)

Enhanced Analysis:

Spend by subchannel vs. contribution
Campaign-specific performance
Attribution by campaign type

Statistical Analysis

Correlation Analysis:

Correlation between groups:
=CORREL(MediaRange, PriceRange)

Identify relationships

Contribution Distribution:

Descriptive statistics:
- Mean, median contribution
- Standard deviation (variability)
- Min, max values

Sharing with Stakeholders

Executive Summary

Create One-Page Dashboard:

Key Metrics:

  • Total KPI for period

  • Marketing contribution ($, %)

  • Top 3 contributors

  • ROI by channel

Visuals:

  • Main decomposition chart (screenshot or recreate)

  • ROI bar chart

  • Trend line

Recommendations:

  • Top 2-3 actions

  • Expected impact

Detailed Reports

For Marketing Teams:

Include:

  • Variable-level decomposition

  • Channel performance details

  • Period-over-period changes

  • Optimization opportunities

Format:

  • Multi-tab Excel workbook

  • Charts and tables

  • Commentary and insights

Presentation Decks

PowerPoint/Google Slides:

Slide 1: Overview

  • Decomposition chart

  • Key findings

Slide 2: Channel Performance

  • ROI comparison

  • Recommendations

Slide 3: Trends

  • Time series analysis

  • Seasonal patterns

Slide 4: Next Steps

  • Action items

  • Timeline

Advanced Excel Techniques

Dynamic Charts with Slicers

Create Interactive Reports:

Step 1: Convert to Table

  • Format data as Excel Table

  • Enables filtering

Step 2: Add Slicers

  • Insert > Slicer

  • Add Date, Group filters

Step 3: Link to Charts

  • Charts update automatically

  • Interactive exploration

Scenario Analysis

Model "What If" Scenarios:

Create Scenario Table:

Scenario | TV Budget | Digital Budget | Expected Contribution
Current  | $300K     | $200K          | $1,000K
Shift 1  | $250K     | $250K          | $1,050K
Shift 2  | $200K     | $300K          | $1,100K

Use Historical ROI:

  • Apply average ROI to new spend levels

  • Estimate contribution changes

  • Compare scenarios

Dashboard Creation

Build Excel Dashboard:

Components:

  • Summary KPIs (total, YoY growth)

  • Contribution breakdown chart

  • ROI table

  • Trend sparklines

  • Top performers

Features:

  • Update with new data

  • Filter by period

  • Drill-down capability

File Management Best Practices

Naming Convention:

ModelName_Export_YYYYMMDD.xlsx

Example:
Q4_2024_Model_Export_20241215.xlsx

Organization:

  • Folder by model or time period

  • Keep historical exports

  • Version control

Documentation:

  • Note export date

  • Model version

  • Any special considerations

Data Validation

Check Exported Data:

Verify Totals:

Sum of group contributions = Predicted
Predicted ≈ Actual (within model fit)

Check for Missing Data:

  • All expected columns present?

  • No unexpected blank rows?

  • Date ranges complete?

Validate Calculations:

Spot-check a few periods:
Manual calculation = Excel values?

Integration with Other Tools

Google Sheets

Import Excel File:

  • Upload to Google Drive

  • Open with Google Sheets

  • Same analysis capabilities

Collaboration:

  • Share with team

  • Real-time editing

  • Comments and suggestions

BI Tools (Tableau, Power BI)

Import Decomposition Data:

  • Load Excel file as data source

  • Create custom dashboards

  • Advanced visualizations

Use Cases:

  • Executive dashboards

  • Interactive exploration

  • Automated reporting

Python/R Analysis

Load Excel Data:

Python:

import pandas as pd
df = pd.read_excel('model_export.xlsx', sheet_name='Group Decomposition')

R:

library(readxl)
df <- read_excel('model_export.xlsx', sheet='Group Decomposition')

Advanced Analysis:

  • Statistical modeling

  • Machine learning

  • Custom algorithms

Troubleshooting Export Issues

File Won't Download:

  • Check browser settings

  • Disable pop-up blockers

  • Try different browser

File Won't Open:

  • Ensure Excel/compatible software installed

  • Check file isn't corrupted

  • Re-download

Missing Data:

  • Verify decomposition was run

  • Check groups are configured

  • Ensure model is fitted

Columns Look Wrong:

  • Column names = your group names

  • Variable names = your variable names

  • Verify grouping is saved

Summary

Exporting Enables:

Further Analysis:

  • Custom calculations

  • Advanced visualizations

  • Statistical tests

Reporting:

  • Stakeholder presentations

  • Executive summaries

  • Detailed documentation

Integration:

  • Combine with other data

  • Feed into BI tools

  • Support decision-making

Best Practices:

Always Export:

  • After finalizing model

  • For important analyses

  • For documentation

Organize Files:

  • Clear naming

  • Version control

  • Archive historical exports

Validate Data:

  • Check totals

  • Verify calculations

  • Spot-check accuracy

Share Appropriately:

  • Right level of detail for audience

  • Clear documentation

  • Actionable insights

Next Steps:

  • Export your model

  • Explore the data

  • Create custom analysis

  • Share insights with team

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