Axioma, newsroom audience analytics for WNYC
A Google Analytics plugin piloted with New York Public Radio over twelve months. 20M users, 136M lifecycle events, and a richer picture of who reads which authors and tags.
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Walkthrough.
What we built and how.
Axioma is a Google Analytics plugin designed to empower media institutions with data-driven insights, optimising content strategies, enhancing audience engagement, and supporting revenue growth. The team conducted a pilot programme with New York Public Radio to analyse audience interaction, focusing on demographics, article-category preferences, and author performance, providing data-driven insights to the newsroom so they can better understand and serve their audience.
The project involves an in-depth analysis of WNYC's Google Analytics data from January 2023 to January 2024 to understand audience dynamics and inform content strategy. The analysis provides descriptive statistics on user engagement, including total users (20 million), returning users (2.9 million), and lifecycle events (136.4 million), with an average session time of 50 seconds. Top-performing tags by unique views were “new-york-city,” “public safety,” and “manhattan,” with Google, Salesforce Marketing Cloud, and Facebook as the most significant referral sources.
The study delves into visitor-story ratios across tag categories, identifying content with high per-story engagement (Art, Food, MTA) versus broader topics with lower engagement per story (New York City). Audience demographics and retention show the 35–44 cohort with the highest retention rate (63%), while older audiences (65+) spend the most time on the site despite fewer sessions. The 18–24 cohort shows seasonal engagement patterns.
The project also investigates author performance and impact, identifying authors instrumental in driving views, while acknowledging that visitor numbers can be skewed by a few breakout stories. The analysis compares engagement across newsroom tags (Public-Safety vs. Politics, Transportation vs. Health and Science) to understand audience interest and content resonance. The methodology for calculating author overperformance based on average views per tag is documented inside the plugin.
