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Copilot analytics

Copilot Analytics

Explore Copilot adoption, ways of working, and impact with interactive demo dashboards, R and Python analyses, and Power BI segmentation.

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.

Pick a card to jump straight to a section, or read on for the full picture.


Interactive Dashboards

Open a demo in your browser without installing anything. All three reports use synthetic data and are available as R templates to adapt. Select Start using for the demo, source, requirements and capability limits.

Business Leaders AI Usage

Synthetic data · R template

Explore four, eight or thirteen weeks by organisation, function and team. Reach counts distinct people across sources; recorded usage is per person per week across the full roster, including non-users.

Start using

  1. Open the synthetic demo in your browser. No installation is required.
  2. To render it locally, clone the whole repository so that the source, helpers and synthetic fixtures stay together. You will need Git, R and Pandoc (also supplied with RStudio).
git clone https://github.com/microsoft/viva-insights-sample-code.git
cd viva-insights-sample-code

R packages: dplyr, tidyr, jsonlite, knitr, rmarkdown. Install missing packages from your approved package source. For CRAN:

Rscript -e "install.packages(c('dplyr', 'tidyr', 'jsonlite', 'knitr', 'rmarkdown'), repos='https://cloud.r-project.org')"
Rscript -e "stopifnot(rmarkdown::pandoc_available())"

Run this from the repository root in a terminal:

New-Item -ItemType Directory -Force ..\copilot-dashboard-work\business-leaders
Rscript examples\utility-r\render-business-leaders-ai.R ..\copilot-dashboard-work\business-leaders\dashboard.html

Expected output: ..\copilot-dashboard-work\business-leaders\dashboard.html, outside the checkout so the tracked demo is not overwritten. It is a self-contained HTML file with four-, eight- and thirteen-week views, group comparisons and separate product credit rates.

Demo inputs: Person, M365 Copilot, Cowork, Agent, Consumption and GitHub Copilot fixtures.

Using real exports: Synthetic reference only. A real-data adaptation needs a separately implemented and verified mapping, coverage assessment and disclosure review.

Work with a coding agent

This report is not supported by the optional dashboard Skill runner. Ask your agent to use the dedicated renderer with this prompt:

Reproduce the synthetic Business Leaders AI Usage report from my local viva-insights-sample-code checkout at [path]. Read its rendering guide and use render-business-leaders-ai.R, not the dashboard Skill runner. Check dependencies first and ask before installing them. Save to a fresh output HTML path and report the command, validation and coverage limits.

Capability and interpretation: This is a synthetic reference, not a verified real-export adapter. Coverage is limited to observed usage within the Person Query roster. M365 Copilot credits and GitHub AI credits remain separate; descriptive comparisons do not establish causation.

Before sharing: review coverage and your organisation's privacy requirements, including data embedded in the HTML. Keep customer exports outside this repository.

Full dashboard details and caveats · Query and setup requirements · Query setup and joins

Copilot Consumption and Ways of Working

Synthetic data · R template

Separate credit volume from cost per session, explore concentration and heavy-user patterns, and compare services, organisations and functions in a seven-page report.

Start using

  1. Open the synthetic demo in your browser. No installation is required.
  2. To render it locally, clone the whole repository so that the source, helpers and synthetic fixtures stay together. You will need Git, R and Pandoc (also supplied with RStudio).
git clone https://github.com/microsoft/viva-insights-sample-code.git
cd viva-insights-sample-code

R packages: jsonlite, rmarkdown, flexdashboard, dplyr, tidyr, ggplot2, scales, knitr, stringr, vivainsights, ggrepel. Install missing packages from your approved package source. For CRAN:

Rscript -e "install.packages(c('jsonlite', 'rmarkdown', 'flexdashboard', 'dplyr', 'tidyr', 'ggplot2', 'scales', 'knitr', 'stringr', 'vivainsights', 'ggrepel'), repos='https://cloud.r-project.org')"
Rscript -e "stopifnot(rmarkdown::pandoc_available())"

Run this from the repository root in a terminal:

Rscript frontier-analytics\starter-kits\copilot-query-dashboard\dashboard.R reproduce frontier-analytics\starter-kits\copilot-query-dashboard\configs\demo-consumption.json

The demo configuration selects an output folder outside the checkout. Choose a fresh folder if it already exists. The runner refuses to overwrite a previous run.

Expected output: A seven-page synthetic HTML report, provenance.json and an isolated working copy under the configured output_dir\reproduce folder outside the repository.

Demo inputs: Five fixture files across Person, Consumption and GitHub queries; GitHub breakdown files are not used.

Using real exports: M365 credits by period and service, with optional grouping when the metadata supports it. Inspect the activity and matching people metadata first, approve exact mappings and confirm spending-policy additivity when required.

Work with a coding agent

Install the dashboard and analysis Skills from the same checkout. Keep the checkout because the Skill runs its R code.

From the parent folder containing your checkout:

gh skill install ./viva-insights-sample-code viva-insights-analysis --from-local --agent github-copilot --scope user
gh skill install ./viva-insights-sample-code viva-insights-copilot-dashboards --from-local --agent github-copilot --scope user

Reload the Skills in your agent, then replace [path] in this starter prompt:

Use the Copilot query dashboards Skill to reproduce the synthetic Consumption demo. My checkout is at [path]. Check dependencies before rendering and ask before installing them. Use a fresh private output directory, preserve the reference files, and return the HTML location, configuration, provenance and validation summary. Explain which demo panels are excluded from the real M365 credits-only adapter.

Capability and interpretation: The real-v1 runner supports only M365 credits from Consumption activity and matching people metadata, with explicit mapping approval. Sessions, Person Query associations and GitHub panels are not implemented by that adapter. Credit intensity does not measure value or quality.

Before sharing: review coverage and your organisation's privacy requirements, including data embedded in the HTML. Keep customer exports outside this repository.

Build or customise with AI

Full dashboard details and caveats · Query and setup requirements · Query setup and joins

Developer Experience and Copilot

Synthetic data · R template

Compare collaboration, focus, after-hours activity and network breadth across three GitHub-intensity groups. Explore joint M365 and GitHub use while keeping observation and eligibility gaps visible.

Start using

  1. Open the synthetic demo in your browser. No installation is required.
  2. To render it locally, clone the whole repository so that the source, helpers and synthetic fixtures stay together. You will need Git, R and Pandoc (also supplied with RStudio).
git clone https://github.com/microsoft/viva-insights-sample-code.git
cd viva-insights-sample-code

R packages: jsonlite, rmarkdown, flexdashboard, dplyr, tidyr, ggplot2, scales, knitr, stringr, vivainsights. Install missing packages from your approved package source. For CRAN:

Rscript -e "install.packages(c('jsonlite', 'rmarkdown', 'flexdashboard', 'dplyr', 'tidyr', 'ggplot2', 'scales', 'knitr', 'stringr', 'vivainsights'), repos='https://cloud.r-project.org')"
Rscript -e "stopifnot(rmarkdown::pandoc_available())"

Run this from the repository root in a terminal:

Rscript frontier-analytics\starter-kits\copilot-query-dashboard\dashboard.R reproduce frontier-analytics\starter-kits\copilot-query-dashboard\configs\demo-github.json

The demo configuration selects an output folder outside the checkout. Choose a fresh folder if it already exists. The runner refuses to overwrite a previous run.

Expected output: A self-contained synthetic HTML report with Overview, Collaboration, Focus, After-hours, Network and additional breakdowns, plus provenance.json and isolated work files under output_dir\reproduce.

Demo inputs: Person, GitHub and Consumption fixtures, including GitHub feature, model and language breakdowns.

Using real exports: Readiness inspection only. The runner does not build this dashboard from real GitHub exports. Missing observation must not be relabelled as zero use or as lack of provisioning.

Work with a coding agent

Install the dashboard and analysis Skills from the same checkout. Keep the checkout because the Skill runs its R code.

From the parent folder containing your checkout:

gh skill install ./viva-insights-sample-code viva-insights-analysis --from-local --agent github-copilot --scope user
gh skill install ./viva-insights-sample-code viva-insights-copilot-dashboards --from-local --agent github-copilot --scope user

Reload the Skills in your agent, then replace [path] in this starter prompt:

Use the Copilot query dashboards Skill to reproduce the synthetic Developer Experience demo. My checkout is at [path]. Check dependencies and ask before installing them. Use a fresh private output directory and return the HTML location, configuration, provenance and validation summary. Keep the GitHub observation limits and privacy gates visible. If I later supply real exports, inspect readiness only because no verified real GitHub build adapter is supplied.

Capability and interpretation: No verified real GitHub dashboard adapter is supplied. Synthetic breakdown allocations are not verified real-export semantics. Acceptance rate is not code quality, after-hours activity is not burnout, and calendar space does not establish coding time.

Before sharing: review coverage and your organisation's privacy requirements, including data embedded in the HTML. Keep customer exports outside this repository.

Build or customise with AI

Full dashboard details and caveats · Query and setup requirements · Query setup and joins

Browse dashboard templates for previews, required queries, source code, and setup guidance.

For an analysis that combines working patterns with AI activity or consumption, start with Joining Viva Insights queries. It explains how to prepare a person-week panel from Person, Consumption, GitHub and Agent exports.


Analysis Scripts

Show code in:
Applies to scripts below, not dashboards. Remembers your choice on this device.

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.


Track how Copilot adoption matures over time — a running adoption-health check that complements, rather than replaces, the causal analyses below.

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

Causal Analysis

These two 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.

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:

📖 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

Need Help?

Last updated: Nov 9, 2026 Edit this page on GitHub