Last week, I was chatting with a Developer Advocate at Gitbook, and he asked me what metrics we looked at for docs at OpenSauced. The honest answer is that we didn’t have KPIs or OKRs or metrics that determined our success. Ultimately, our success was based on users being able to use the software and understand the value in the data we were exposing. Now, that doesn’t mean that I didn’t measure anything. It just means that we weren’t building dashboards to track progress. Ultimately, the metrics will depend on your team’s goals, but here are some of the things I think are valuable in identifying whether or not the docs I’m working on are successful.
Beyond Vanity Metrics: The True Impact of Developer Content
Traditional content metrics like page views and social shares can be useful, but they tell an incomplete story. There are three dimensions to Developer Content Value that I like to think of as we connect documentation to business outcomes:
1. Engagement: Quality of Interaction
Surface-level engagement metrics tell you if developers are finding your content, but deeper metrics will show you if they’re actually succeeding with it.
Basic Metrics:
- Page views
- Average time on page
- Bounce rates
Advanced Metrics:
- Content completion rates: What percentage of developers complete multi-step tutorials?
- Code example copy events: How often do developers copy sample code (indicates intent to implement)?
- Documentation-to-implementation time: How quickly do developers move from reading docs to successful implementation?
- Return visit patterns: How frequently do developers reference the same documentation (may indicate confusion or high value)?
2. Support Deflection: Reducing Operational Costs
Well-crafted developer content should impact support volume and complexity.
Key Metrics:
- Support ticket reduction: Decrease in tickets related to documented topics
- Question sophistication: Shift from basic “how to” questions to more advanced inquiries
- Self-service resolution rates: Percentage of developers who resolve issues through documentation rather than support channels
- Documentation referrals: How often support agents can resolve issues by linking to existing documentation
3. Product Adoption: Driving Business Outcomes
Ultimately, developer content should allow for successful product adoption and usage.
Key Metrics:
- Documentation-influenced conversion: Conversion rates for users who engage with documentation vs. those who don’t
- Feature adoption correlation: Relationship between documentation engagement and feature usage
- Time-to-value: How quickly new developers reach their first success milestone
- Retention impact: Differences in retention between developers who use documentation vs. those who don’t
Documentation Instrumentation To gather meaningful data, your documentation needs appropriate tracking:
- Event-based analytics: Track specific interactions beyond page views (code copying, example expansion, etc.)
- Documentation-to-product pathing: Follow user journeys from documentation to product implementation
- Feedback mechanisms: Implement contextual feedback options ( “Was this helpful?” with follow-up options)
- Search analytics: Track what developers search for, what they find, and what they do next
Connecting Data Sources
To understand whether or not your documentation is successful, you should consider how to connect it to other systems you’re tracking. For instance:
- Support ticket integration: Tag tickets with related documentation to identify gaps
- Product usage correlation: Connect documentation engagement with feature adoption
- Developer journey mapping: Track developer progression from documentation to implementation
Not every team has the resources for comprehensive analytics, but you can start with minimal investment:
- Implement basic document instrumentation
- Add “Was this helpful?” widgets to key pages
- Track code copying events
- Monitor documentation search queries
- Sample qualitative data
- Review a subset of support tickets weekly to identify documentation gaps
- Conduct monthly documentation interviews with developers
- Add optional exit surveys when developers leave documentation
- Use proxy metrics
- Monitor support volume for topics with new/updated documentation
- Track time spent in documentation vs. successful API calls
- Compare onboarding completion rates before and after documentation changes
The Contributor Journey Metrics
For open source projects, documentation is both an onboarding tool and a contributor pipeline. At OpenSauced, we partially looked at our documentation effectiveness through the “contribution ladder” metrics:
- First-time contributor conversion: Percentage of documentation readers who make their first contribution
- Contributor retention: Rate at which contributors return to make additional contributions
- Contribution progression: Movement of contributors through increasingly complex contribution types
At OpenSauced, we structured our documentation to create a clear progression path for contributors. New community members typically followed this journey:
- Education path: Learning about open source and its purpose
- Documentation contributions: Making their first contributions by improving our docs
- App contributions: Eventually contributing to the core application
When we look at this ladder, we can also see it from this perspective:
- Awareness → Interest: How many people who discovered the project engaged with documentation
- Interest → First Contribution: Conversion rate from students to first-time contributors
- First Contribution → Repeat Contribution: Retention rate of contributors
- Repeat → Core Contributor: Progression rate to becoming regular contributors
Last Thoughts
Effective measurement isn’t about proving documentation’s worth in the abstract. You have to try to connect content directly to the metrics that matter to your organization. Whether that’s reducing support costs, accelerating adoption, or improving developer satisfaction, the right metrics framework can change the perception and value of your documentation.