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When building developer portals and content, decision-making speed often matters more than perfectionism. You can spend months developing a feature, go through iterations, invest resources, and still, after release, see that your target audience is not interested enough or simply is not using it enough.<\/p>\n
Start with a concrete hypothesis, not a wish<\/h2>\n
The hardest part of a product sprint is identifying the right issue and a hypothesis you can actually test.<\/p>\n
\u201cWe want to improve UX documentation\u201d is not a real issue. It should be more concrete and measurable, for example:<\/p>\n
- \n
- Half of users drop after the \u201cFirst API Call\u201d step in the conversion funnel: Doc Visit -> OpenAPI Download\/Copy -> First API Call -> Sustained API Calls.<\/li>\n
- Time-to-completion increases by 20 minutes during a specific Learning Lab or tutorial session.<\/li>\n
- Average session duration in the Cloud IDE is under 10 seconds.<\/li>\n<\/ul>\n
Each of these can be measured, improved, and checked again after the release.<\/p>\n
Measure what matters: Product-market fit indicators for developer portals<\/h2>\n
After each release, it is important to measure success and consolidate relevant business and product data into a single dashboard for key stakeholders and for the next sprint. That is where product-market fit (PMF) indicators become important.<\/p>\n
Possible key product-market fit indicators for developer portals:<\/p>\n
- \n
- Growth in usage and registration among individual and enterprise customers, with an emphasis on Activation Rate<\/strong> and Return Usage<\/strong>.<\/li>\n
- For education content or guides, Time-to-Completion<\/strong> should match the estimated time. If a lab is designed for 30 minutes but averages an hour, there is too much friction.<\/li>\n
- Unique visits to documentation pages and downloads or copies of OpenAPI, SDK, and MCP documentation correlated with an increase in API requests.<\/li>\n
- Low support tickets per 100 active developers<\/strong> (or per API request volume).<\/li>\n
- A low 4xx error ratio after a docs update or release, alongside a strong API usage success rate.<\/li>\n
- Time to First Hello World (TTFHW)<\/strong> \u2013 first app, integration, or API call \u2013 under 10 minutes.<\/li>\n<\/ul>\n
Product analytics events we track or recommend<\/h2>\n
Product analytics and user experience sessions can give you the information you need to make product decisions. Analytics can also enrich your user stories and feature requests with real data.<\/p>\n
Here are examples of Google Analytics events that help explain how users interact with developer-oriented content. We already use some of them in practice, while others are suggestions that may be useful for teams building developer portals and content.<\/p>\n
- \n
sign_up<\/code>,login<\/code> \u2013 for portals that require login.<\/li>\ntutorial_begin<\/code> \u2013 a tutorial was opened, and the user spent 10+ seconds on the page.<\/li>\ntutorial_complete<\/code> \u2013 triggered by multiple signals, such as time on page, scroll depth, or executing or copying related commands.<\/li>\nsearch<\/code>,view_search_results<\/code> \u2013 to understand search patterns and how users interact with results.<\/li>\n<\/ul>\nThere is also a specific set of events that helps us understand how content is consumed by users and AI coding agents or assistants:<\/p>\n
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copy_for_ai<\/code> \u2013 how many times and on which page users copy Markdown to continue work in AI agents.<\/li>\ntext_select<\/code> \/text_copy<\/code> \u2013 triggered when the user interacts with 500+ characters; useful as a \u201cCopy for AI\u201d proxy even on pages without an explicit button.<\/li>\ndownload_openapi_doc<\/code>,download_mcp_doc<\/code>,download_sdk_doc<\/code> \u2013 how many times each full document is downloaded for local use or AI-agent workflows.<\/li>\n<\/ul>\nValidating decisions: analytics + user feedback + business impact<\/h2>\n
A feature or change is a strong fit when you can confirm the hypothesis from three angles:<\/p>\n
- \n
- Product analytics<\/li>\n
- User feedback<\/li>\n
- Business impact<\/li>\n<\/ul>\n\n
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- For education content or guides, Time-to-Completion<\/strong> should match the estimated time. If a lab is designed for 30 minutes but averages an hour, there is too much friction.<\/li>\n
- Growth in usage and registration among individual and enterprise customers, with an emphasis on Activation Rate<\/strong> and Return Usage<\/strong>.<\/li>\n