Copilot Analytics Scripts

This page contains specialized scripts for analyzing Microsoft Copilot usage data from Viva Insights.

Beyond covering key analyses around Copilot usage volume and breadth (range of actions and applications), these scripts also include a method for measuring Copilot habituality based on behavioral research. This approach determines whether a user can be considered a habitual Copilot user, enabling visualization through user segmentation that identifies Power Users and Habitual Users within an organization. This segmentation provides a framework for continuously tracking Copilot adoption success and measuring the effectiveness of your deployment strategy.

See our DAX Calculated Columns page for detailed instructions on how to identify Copilot Usage Segments using Power BI templates and pre-built DAX formulas.

For more information on the Copilot Usage Segments, see this introduction.

For more inspiration on analyzing Copilot adoption and impact, have a look at our advanced examples playbook.

Advanced Analysis Scripts

Copilot Advanced Analysis (R)

📄 copilot-analytics-examples.R

Purpose: Comprehensive analysis of Copilot usage patterns and trends
Language: R
Prerequisites: vivainsights R package, Copilot usage data
Key Analysis: Usage segmentation, trend analysis, adoption metrics
📥 Download

Copilot Advanced Analysis (Python)

📄 copilot-analytics-examples.py

Purpose: Comprehensive analysis of Copilot usage patterns and trends
Language: Python
Prerequisites: vivainsights Python package, Copilot usage data
Key Analysis: Usage segmentation, trend analysis, adoption metrics
📥 Download

Copilot Analytics (Jupyter Notebook)

📓 copilot-analytics-examples.ipynb

Purpose: Interactive analysis of Copilot usage with visualizations
Language: Python
Format: Jupyter Notebook
Prerequisites: vivainsights Python package, Copilot usage data
Key Features: Step-by-step analysis, interactive visualizations
📥 Download

Adoption Tracking & Causal Analysis

The examples in this section focus on measuring Copilot adoption credibly over time. The Copilot usage segments over time scripts sum individual Copilot-action columns, classify each person-week with identify_usage_segments(version = "12w"), and visualise how the mix of Power, Habitual, Novice, Low, and Non-users evolves week by week. The difference-in-differences metric scan runs a within-person DiD per metric across two both-licensed groups (Power vs Low Copilot users) and assembles the effects, confidence intervals, and significance into one sortable table plus a forest plot, honestly surfacing the metrics that do not move. The event-study and difference-in-differences example aligns each adopter on their own event time, checks the parallel-trends assumption before trusting a single headline number, and reads the within-person change net of a non-adopting control. The two causal examples build small, clearly labelled seeded simulations so that the models have something to recover; swap the simulation block for your own export before drawing conclusions.

Copilot Usage Segments Over Time (Python)

📄 copilot-usage-segments-trend.py

Purpose: Track how the mix of Copilot usage segments evolves week by week
Language: Python
Prerequisites: vivainsights Python package, pandas, numpy, matplotlib
Key Features: identify_usage_segments (12-week rolling), stacked-area segment mix, action trend
📥 Download

Copilot Usage Segments Over Time (R)

📄 copilot-usage-segments-trend.Rmd

Purpose: Track how the mix of Copilot usage segments evolves week by week
Language: R
Format: R Markdown
Prerequisites: vivainsights R package, dplyr, tidyr, ggplot2, scales
Key Features: identify_usage_segments (12-week rolling), stacked-area segment mix, action trend
📥 Download
🌐 View HTML Output

Difference-in-Differences Metric Scan (Python)

📄 did-metric-scan.py

Purpose: Run a within-person DiD per metric (Power vs Low Copilot users) into one sortable table
Language: Python
Prerequisites: vivainsights Python package, linearmodels, pandas, numpy, matplotlib
Key Features: Per-metric TWFE DiD, significance stars, forest plot, honest reporting of null effects
📥 Download

Difference-in-Differences Metric Scan (R)

📄 did-metric-scan.Rmd

Purpose: Run a within-person DiD per metric (Power vs Low Copilot users) into one sortable table
Language: R
Format: R Markdown
Prerequisites: vivainsights R package, fixest, dplyr, tidyr, ggplot2, purrr, scales
Key Features: Per-metric TWFE DiD, significance stars, forest plot, honest reporting of null effects
📥 Download
🌐 View HTML Output

Event-Study & Difference-in-Differences (Python)

📄 event-study-did.py

Purpose: Measure within-person behaviour change around Copilot adoption with a TWFE event-study/DiD
Language: Python
Prerequisites: vivainsights Python package, linearmodels, pandas, numpy, matplotlib
Key Features: Event-time alignment, pre-trend check, person + week fixed effects, z-scored composite index
📥 Download

Event-Study & Difference-in-Differences (R)

📄 event-study-did.Rmd

Purpose: Measure within-person behaviour change around Copilot adoption with a TWFE event-study/DiD
Language: R
Format: R Markdown
Prerequisites: vivainsights R package, fixest, dplyr, tidyr, ggplot2, scales
Key Features: Event-time alignment, pre-trend check, person + week fixed effects, z-scored composite index
📥 Download
🌐 View HTML Output

Power BI Integration

DAX Calculated Columns

📁 DAX Calculated Columns

Purpose: Pre-built DAX formulas for Copilot usage segmentation in Power BI
Language: DAX
Format: Individual .dax files
Prerequisites: Power BI Desktop, Copilot usage data

Available Columns:

12-Week Rolling (RL12W) - Recommended for long-term analysis

📄 _Total Copilot actions_RL12W.dax: Average weekly actions over 12 weeks
📄 _IsHabit_RL12W.dax: Habit formation indicator (9+ weeks of usage)
📄 _CopilotUsageSegment_RL12W.dax: User segmentation (Power/Habitual/Novice/Low/Non-users)

4-Week Rolling (RL4W) - Recommended for short-term/pilot analysis

📄 _Total Copilot actions_RL4W.dax: Average weekly actions over 4 weeks
📄 _IsHabit_RL4W.dax: Habit formation indicator (4 weeks of usage)
📄 _CopilotUsageSegment_RL4W.dax: User segmentation (Power/Habitual/Novice/Low/Non-users)

📖 DAX Documentation

Usage Segmentation

User Segments Defined

These five segments form a single mutually-exclusive ladder, evaluated top-down so every user falls into exactly one tier (full definitions and decision tree on the Copilot Usage Segments page):

Power Users: Habitual and averaging 15+ weekly Copilot actions
Habitual Users: Habitual (9+ of 12 weeks in RL12W, all weeks in RL4W) but averaging < 15 weekly actions
Novice Users: Not habitual, averaging 1+ weekly Copilot actions
Low Users: Not habitual, some usage but averaging < 1 weekly action
Non-users: No Copilot usage in the measurement period

Sample Data

Example Datasets

📁 Example Data

📄 copilot-metrics-taxonomy.csv: Copilot metrics reference
📄 viva-insights-org-data-sample.xlsx: Sample organizational data

Getting Started

Export Copilot Usage Data from Viva Insights
Choose Your Analysis Method:
- R/Python scripts for detailed analysis
- DAX columns for Power BI dashboards
Select Time Frame:
- RL12W for long-term habit analysis
- RL4W for pilot programs or short-term analysis
Run Analysis using the appropriate script

Copilot Usage Segments — how Power, Habitual, and Novice segments are defined
DAX Calculated Columns — ready-to-use Power BI formulas for segmentation
Copilot Causal Toolkit — measure the causal impact of Copilot on business outcomes
Causal Inference in Copilot Analytics — methods for isolating Copilot’s true effect
Frontier Prompt Library — generate Copilot reports and dashboards with coding agents

Need Help?

Copilot Analytics Documentation: Viva Insights Copilot Guide
Power BI Integration: DAX Documentation
Sample Data: Example datasets