Everyone has their own definition of success.
Editorial optimizes for pageviews, growth cares about subs, sales chases impressions—and the dashboards never fully agree.
Industry · Media, Publishing & Content Platforms
We help media, news, and content platforms build analytics that go beyond "traffic"—tying attention, subscriptions, and ad inventory together so product, editorial, and revenue teams make decisions from the same playbook.
Built for publishers, streaming platforms, membership communities, and content-led brands where audience attention and revenue both matter.
Example · Editorial & Revenue View
Editorial signals
Revenue signals
Result: a shared view where editors, product, and sales see the same story—what content grows audience, drives subs, and sells inventory.
Media Reality
We rebuild analytics around how media businesses really work: audience development, monetization, and editorial missions—not just generic "events" from an out-of-the-box GA4 setup.
Editorial optimizes for pageviews, growth cares about subs, sales chases impressions—and the dashboards never fully agree.
Anonymous visitors, registered users, and subscribers live in different tools. It’s hard to tell what content actually nudges people to sign up, subscribe, or stay.
New paywall rules, placements, and A/B tests go live, but tracking is partial. You learn about bad experiments weeks later in the revenue reports.
CMP changes, consent rules, and privacy regimes quietly drop events. Ad serving and analytics both degrade, and nobody has a clean view of the impact.
Foundations First
Strong dashboards are just the visible layer. Underneath is a measurement design that understands content types, reader states, monetization paths, and the constraints of privacy and consent.
Media Data Architecture
The goal is not just to see "what performed". It's to see why, for whom, and how it contributes to growing a resilient media business—audience and revenue together.
Content & Experience Layer
Typical tech
CMS, front-end frameworks, GA4, GTM, server-side tagging where needed, experimentation setup for layouts and modules.
Identity & Reader State Layer
Typical tech
Auth systems, subscription platforms, CRM/CDP, server-side events, GA4 user properties and audiences, downstream warehouse tables keyed by user/account.
Monetization & Inventory Layer
Typical tech
Ad server / SSP data, subscription billing, GA4 ecommerce events when relevant, server-side event pipelines, BI models for ARPU and yield.
Warehouse, Models & Decision Layer
Typical tech
BigQuery/Snowflake/Redshift, dbt or SQL models, Looker Studio / Power BI, content and cohort scorecards consumed by editorial, growth, and leadership.
Optimization & Experimentation
Typical tech
Experimentation platforms or in-house frameworks, GA4 experiments where appropriate, A/B tools integrated with analytics and BI models.
Selected Media Engagements
The pattern: define meaningful engagement, wire in subscriptions and revenue, and give each team a lens that matches their decisions—on top of one shared data foundation.
Subscription-led news publisher
Problem: Editorial and growth disagreed about what content converted subscribers. All sides relied on partial, channel-specific dashboards.
What we did: Redesigned event schema, wired paywall and subscription events into GA4 and the warehouse, and built content contribution models by topic, author, and section.
Impact: Clear view of which beats and formats actually drive subs and retention; editorial and growth could align coverage and promotion around high-leverage areas.
Ad-supported media network
Problem: High traffic but unclear yield per pageview. Ad ops, product, and editorial didn’t share a common view of inventory, viewability, or layout trade-offs.
What we did: Integrated ad server data with analytics, standardized page and slot metadata, and rolled out dashboards for RPM, viewability, and layout performance.
Impact: Teams could see which layouts and modules improved both UX and yield; experiments focused on profitable, reader-friendly inventory changes.
Membership content platform
Problem: Membership sign-ups were tracked in isolation from content and community behaviour. Leadership couldn’t tell what drove long-term member value.
What we did: Mapped content, engagement, and membership events into a unified model, and built cohort views by acquisition source, content mix, and engagement pattern.
Impact: Strategic decisions shifted from "more content" to "the right content and experiences" that actually improved retention and member ARPU.
For editorial & content
Understand which stories, beats, and formats grow loyal audience and subs—not just one-off traffic spikes—and get reporting that feels like a tool, not a scorecard.
For product & audience development
Clear funnels through registration, paywalls, and membership with the ability to test layouts, recommendation modules, and offers in a structured way.
For revenue, sales & leadership
Joined-up view of ad yield, subscription revenue, and member value by cohort—so you can steer the business, not just react to last month's numbers.
Next step
In 45–60 minutes, we’ll review your current tracking, paywall and ad measurement, and reporting, highlight the biggest gaps, and outline a pragmatic path to decision-ready media analytics.