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Copilot analytics

Copilot Analytics

Analyze Microsoft 365 Copilot usage from Viva Insights: adoption metrics, Power User and Habitual User segmentation, habit-based behavioral models, adoption journey and cohort analysis, usage-segment trends over time, and causal impact analysis with difference-in-differences and event-study methods in R, Python, and Power BI.

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.

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Advanced Analysis Scripts

Copilot Advanced Analysis

📄 copilot-analytics-examples.R

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

📄 copilot-analytics-examples.py

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

📓 copilot-analytics-examples.ipynb (Jupyter Notebook)

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

Copilot Adoption Journey and Ways of Working

Where the scripts above give you a tour of the core Copilot metrics, this notebook is the next step: an end-to-end assessment of how far adoption has travelled and what sustained use is associated with. Reach for it once you have at least 12 weeks of Copilot data and need to answer a leadership question rather than explore the data.

Start with When
Copilot Advanced Analysis (above) You are getting oriented, and you want the core metrics, segmentation, and standard visuals.
Copilot Adoption Journey (below) You have 12+ weeks of data and need cohorts, habit formation, conversion targeting, and associated ways of working.

📓 copilot-adoption-journey-analysis.ipynb (Jupyter Notebook)

  • Purpose: End-to-end assessment of Copilot reach, habit formation, conversion opportunity, and associated ways of working
  • Format: Jupyter Notebook
  • Prerequisites: vivainsights, pandas, NumPy, SciPy, statsmodels, matplotlib, and a Copilot-rich Person Query export with at least 12 weeks of data. Add pyarrow if you are loading a Parquet export
  • Key Features: 12-week Power/Habitual/Novice/Low/Non-user segmentation, entry-cohort and function analysis, native Viva Insights scans, adjusted associations, and same-person diagnostics
  • Interpretation: Observational associations only. Set INPUT_FILE to your own export before running; any metric or attribute your query lacks is reported as unavailable and skipped
  • Related: Copilot Usage Segments for the segment definitions, and Causal Inference when you need an effect rather than an association
  • 📥 Download

This example is currently available in Python only. Switch the toggle above to Python to see the notebook details, or use the R scripts in the other sections on this page.

Adoption Tracking & Causal Analysis

The examples in this section focus on measuring Copilot adoption credibly over time. 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

Sums individual Copilot-action columns, classifies each person-week with identify_usage_segments(version = "12w"), and visualises how the mix of Power, Habitual, Novice, Low, and Non-users evolves week by week.

📄 copilot-usage-segments-trend.Rmd

  • Purpose: Track how the mix of Copilot usage segments evolves week by week
  • 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

📄 copilot-usage-segments-trend.py

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

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.

📄 did-metric-scan.Rmd

  • Purpose: Run a within-person DiD per metric (Power vs Low Copilot users) into one sortable table
  • 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

📄 did-metric-scan.py

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

Event-Study & Difference-in-Differences

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.

📄 event-study-did.Rmd

  • Purpose: Measure within-person behaviour change around Copilot adoption with a TWFE event-study/DiD
  • 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

📄 event-study-did.py

  • Purpose: Measure within-person behaviour change around Copilot adoption with a TWFE event-study/DiD
  • 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

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

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

📖 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):

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

Sample Data

Example Datasets

📁 Example Data

Analysis Workflow

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

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Last updated: Aug 14, 2026 Edit this page on GitHub