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CHABOT.DEV — A FIELD JOURNAL — VOLUME I, NO. 4

08    TOOLS   ✣

Analytics Tools.

DevRel teams measure two distinct things and need different tooling for each:

ENTRY · 4 MIN · 815 WORDS

DevRel teams measure two distinct things and need different tooling for each:

  • Product analytics — what developers do inside the product (signup, activation, retention).
  • Community analytics — what developers do in spaces around the product (GitHub, Discord, social, conferences).

This file covers the major tools in each lane.

Product analytics

Mixpanel

  • Founded. 2009.
  • Positioning. Product analytics for tracking events, funnels, cohorts, retention.
  • Strengths. Mature funnel and retention analysis; strong cohort segmentation.
  • Used by. Many SaaS products including developer products for onboarding-funnel analysis.

Amplitude

  • Founded. 2012.
  • Positioning. Product analytics with stronger emphasis on experimentation and behavioural cohorts.
  • Strengths. Big-data scale; AI-assisted analysis; experimentation tooling.
  • Used by. Many large product-led-growth companies.

Heap

  • Founded. 2013.
  • Positioning. Auto-capture analytics — track everything and define events retroactively.
  • Strengths. No upfront instrumentation; flexible event definition.
  • Used by. Companies prioritising agility over schema discipline.

PostHog

  • Founded. 2020.
  • Open source. Yes — self-hostable.
  • Positioning. All-in-one product analytics, session replay, feature flags, A/B testing, surveys.
  • Strengths. Open source friendly to developer-product companies; strong default privacy posture; growing rapidly in 2024–2026 among developer-first companies.
  • Used by. Many YC-stage and developer-product startups.

June, Mixpanel-alternatives

  • June, Fathom (analytics), Plausible, Umami. Smaller alternatives; some privacy-focused.

Snowplow

  • Open-source event-data pipeline. Used by larger orgs to own their own event-collection layer rather than outsourcing.

Web analytics (for content)

  • Google Analytics 4 (GA4). Default for many docs sites; non-trivial migration from older Universal Analytics.
  • Plausible. Privacy-first, simple metrics.
  • Fathom. Similar.
  • Cloudflare Web Analytics. Free, privacy-respecting, simple.
  • Vercel Analytics, Netlify Analytics. Platform-bundled analytics.

Search and SEO tools

  • Ahrefs, Semrush, Moz. SEO tools for understanding how developers find content.
  • Google Search Console. Free, essential for any docs site.
  • Algolia DocSearch logs. What developers search inside your docs.

Community analytics

CNCF DevStats

  • Premise. Tracks all CNCF projects’ contribution activity (PRs, issues, comments, reviews) by contributor, country, organisation, over time.
  • Free, open-source. Dashboards at devstats.cncf.io.
  • Significance. The reference standard for measuring open-source health in a foundation-led project.

LFX Community Data Platform

CHAOSS (Community Health Analytics for Open Source Software)

  • A Linux Foundation project. Defines metric standards for open-source community health.
  • Practitioner-led; provides metrics models that DevStats and other tools implement.

GrimoireLab

  • Open-source analytics platform for software-development data; underpins DevStats and Bitergia services.

Bitergia Analytics

  • Commercial. Analytics services built on GrimoireLab; used by many large open-source foundations.

Orbit (model) implementations

Common Room, Savio

GitHub Octoverse / GitHub Insights

  • Octoverse is GitHub’s annual state-of-software report; built on GitHub’s internal analytics.
  • GitHub Insights (formerly part of Pull Panda; integrated into GitHub Enterprise) provides repo-level analytics for individual repositories.

Discord and Slack analytics

  • Native Discord analytics are limited; many teams supplement with custom bots writing to a warehouse.
  • Slack Analytics is limited at the Free tier and substantial at Enterprise Grid.
  • Common Room and Orbit-derived tools sit on top of these to provide cross-channel views.

Stack Overflow / Reddit / dev.to analytics

  • Stack Overflow tag-watching and search-data; modest in 2026 given platform decline.
  • Reddit’s official analytics for subreddit moderators; less useful for content discovery.
  • dev.to dashboard for authors and organisations.

Data warehouse + BI stack (for serious DevRel orgs)

The most sophisticated DevRel teams stop relying on individual SaaS dashboards and pull data into a warehouse:

  • Warehouse. Snowflake, BigQuery, Databricks, Redshift, PostgreSQL.
  • Ingestion / ELT. Fivetran, Airbyte, Stitch; raw GitHub / Discord / Slack / Discourse / Postman / Salesforce API pulls.
  • Transformation. dbt for SQL modelling.
  • BI / visualisation. Looker, Mode, Hex, Metabase, Superset, Tableau, Power BI.

This approach scales indefinitely and gives the team full control of definitions (what counts as “active contributor,” “qualified developer lead,” etc.).

The trade-off is that it requires data-engineering capability the team may not have.

What to track

The metrics most often tracked by mature DevRel teams (see ../04-metrics/ for detail):

  • Funnel: awareness → signup → first hello world → activation → retention.
  • Community: active users, contributor return rate, sentiment, NPS.
  • Content: views, completion rate, search queries, AI-assistant retrieval frequency.
  • Events: registration vs. attendance, post-event activation, share-rate.
  • Pipeline / revenue: DevRel-influenced revenue, DQL pipeline.

A common failure mode is to instrument everything possible and report on nothing. Pick the 10–20 metrics that connect cleanly to AAARRRP goals and refresh weekly. Everything else is for ad-hoc analysis only.

See also