Content Performance Analytics

Part of the worlds-biggest-software-project initiative.

Article-level engagement, author performance, and topic trending analytics built for editorial teams rather than data engineers.

Content Performance Analytics is an open-source platform for publishers, media organisations, and content-led businesses that connects content effort to business outcomes. It tracks engagement quality, reach, and conversion at the level of individual articles, authors, and topics — and surfaces emerging themes before they peak, so editorial teams can commission ahead of demand.

Why Content Performance Analytics?

Standard web analytics report pageviews and sessions but do not attribute revenue, subscriber acquisition, or audience retention to individual pieces of content, specific authors, or topic clusters.
Incumbent tools each cover only part of the stack: Chartbeat is real-time but weak on long-term cohorts and paywall conversion; Parsely lags on real-time and predictive trending; BuzzSumo has no on-site engagement data and limited author-level attribution on the publisher side.
HubSpot's content analytics is only meaningful when the full HubSpot suite is deployed and is shallow on engaged time and scroll depth compared with newsroom-focused tools.
Author-level attribution is consistently underdeveloped — most tools aggregate by page rather than by writer, and collaborative or editorially reassigned content is poorly modelled.
Predictive trending is largely retrospective across the category; there is space for a unified, AI-native model joining search velocity, social signal, on-site engagement, and subscription conversions in one queryable layer.

Key Features

Article-Level Engagement

Pageviews, unique visitors, scroll depth, and engaged time per article
Traffic source attribution: search, social, direct, and referral
Real-time active-reader display for editorial teams
Historical benchmarking of an article against similar past content
Configurable date ranges and period-over-period comparison

Author & Topic Performance

Per-byline engagement and traffic metrics across an author's publishing history
Author performance leaderboard with configurable metric weighting
Topic performance grouping via automated content tagging
NLP-based automated topic tagging with configurable taxonomy management
Return-visit and loyalty scoring per reader segment

Conversion & Revenue Attribution

Paywall conversion attribution linking article engagement to email sign-ups and subscription starts
Recirculation analysis: which articles most effectively drive readers to additional content
Editorial ROI calculator: cost-per-article versus revenue and audience attribution

Topic Trending & Commissioning

Predictive topic trending engine combining search velocity and social signal data
Social share data ingestion from BuzzSumo API or direct platform APIs
Google Search Console integration for organic impression and click-through data
AI-generated editorial briefing highlighting top content opportunities of the week

Editorial UX

Dashboards designed for editorial editors and content strategists without data science skills
Post-performance reports automatically generated after configurable publication windows
A/B testing for headlines and content variants with engaged-time as the success metric

AI-Native Advantage

AI moves the platform from retrospective reporting to forward-looking commissioning. NLP models maintain a consistent topic taxonomy across a large catalogue, where automated tagging has historically drifted over time. A predictive trending signal blends search-impression velocity, social-share rate, and historical pattern matching to surface themes before they peak. Natural-language editorial briefings translate the underlying analytics into specific weekly recommendations, and content-optimisation suggestions correlate concrete edits — headline, structure, internal links — with higher engaged time.

Tech Stack & Deployment

The architecture is expected to combine a lightweight, CMS-agnostic JavaScript tracking tag with a warehouse-backed analytical layer. Open-source components likely to feature include spaCy and Hugging Face Transformers for content classification, Elasticsearch for catalogue search and faceted filtering, dbt for joining content metadata with engagement and subscription data, and Apache Airflow for scheduled digest jobs. Warehouse targets include Snowflake and BigQuery. External data sources include the Google Search Console API for organic impressions, GA4 / Plausible / Fathom for engagement capture, and the BuzzSumo API for social signal ingestion. Deployment is intended to be self-hostable for publishers with data-residency requirements.

Market Context

The content analytics category spans purpose-built editorial tools (Chartbeat, Parsely) and broader marketing platforms adapted for content (BuzzSumo, Brandwatch / Sprout Social, HubSpot). All major incumbents are proprietary SaaS, with primary buyers in newsrooms, digital media organisations, and content-marketing teams at content-led businesses. The candidate-projects table rates this project's complexity at 5/10, with High domain availability and Medium demand.

Project Status

This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.