One warehouse for content and subscriber data
and an AI analyst who answers in numbers
A publisher usually has article analytics in one tool, subscription and billing data in another, and churn that nobody has connected to which content a subscriber actually read before leaving. We put it into one warehouse, build dashboards on content performance and subscriber retention, and give you an AI analyst who answers editorial and business questions with real numbers.
The problem in media and publishers
A publisher generates data everywhere and a single picture nowhere: the CMS tracks article performance, the subscription platform tracks billing and plan changes, ad platforms track acquisition campaigns, and none of them talk to each other by default. A subscriber who churns leaves a record in the subscription platform, with nothing connecting that churn to what they actually read, or stopped reading, beforehand.
The second problem is that content performance gets measured by page views and time on page, metrics that say little about which stories actually drive someone toward subscribing or staying subscribed. A story can be widely read and contribute nothing to retention, while a less-viewed piece in a reader’s specific area of interest quietly does most of the retention work.
The third is that churn analysis, when it happens at all, usually means looking at a cancellation rate in isolation, without the context of what content, support interactions or pricing changes preceded it, which turns “why are subscribers leaving” into a guess rather than an answer grounded in data.
A fourth problem shows up when a publisher runs more than one brand or publication: without a shared schema, comparing subscriber retention or content performance across brands means someone manually reconciling several differently structured exports, a task that gets skipped more often than it gets done.
What we build for media and publishers
A single PostgreSQL warehouse that joins your CMS, subscription platform, ad analytics and email data into one place, with a daily sync and backfill so history is never lost. Each source gets a raw layer first, then marts built around the questions that actually matter: content performance tied to subscription outcomes, subscriber cohorts, churn patterns, and revenue per subscriber.
Dashboards surface what an editorial lead or a subscription manager needs at a glance: which content categories and specific pieces actually drive subscriptions and retention, retention trends by signup cohort, and churn patterns where your data supports tracing them, a content engagement drop before cancellation, a support ticket left unresolved. An AI analyst sits on top in Telegram, answering questions like “which content category has the best retention at 90 days” with the SQL query it ran shown alongside the answer, so nothing is a black box.
Typical integrations: your CMS, subscription and billing platform, Meta and Google ad platforms for acquisition data, email platform for engagement data, and Telegram for alerts and the AI analyst interface.
How it works in 3 to 6 weeks
- Audit. What exists across CMS, subscription and ad systems, what is already tracked, and what the real data quality looks like. One week.
- Model. Which editorial and business questions matter most; the marts are designed around content-to-subscription and retention from the start.
- Connectors and sync. Raw layer for each source, daily jobs, backfill, reconciliation checks against what the subscription platform actually reports.
- Dashboards and alerts. Built with the editorial and subscription teams who will use them day to day, with anomaly alerts to Telegram.
- AI analyst. A read-only database role, guarded SQL with forced limits and timeouts, tested on recorded real questions before going live.
What it costs
| Package | Price | What it covers | Timeline |
|---|---|---|---|
| Tracking audit | from $800 | What your current systems actually see and miss, with a fix list | 1 week |
| Warehouse + dashboards | from $2,500 | Connectors, daily sync, marts, dashboards on content and retention | 3 to 6 weeks |
| AI analyst + monitoring | from $5,000 | Everything above plus the AI analyst in Telegram and churn anomaly alerts | 6 to 10 weeks |
Typical results
Publishers that join content and subscription data for the first time typically find that a meaningful share of churn drivers were invisible to existing dashboards, since no single system had the full picture, an industry-wide pattern in the 15 to 30 percent range reported across comparable media operations. Teams with a working content-to-subscription dashboard commonly identify which categories deserve more editorial investment within weeks instead of relying on impression alone. These are typical ranges reported across the sector, not a guarantee, since existing data quality and system count both move the number. Our own numbers are in the case studies linked below: a two-brand analytics warehouse with 1,025 automated tests and an AI analyst catching anomalies with zero false alarms is detailed in the analytics hub case study, and a content agent producing 11 content types across two brands and four funnel stages with a compliance gate is in the content agent case study.
Why Senator Media
We build these warehouses the way we build our own: a guarded, read-only SQL layer for the AI analyst, forced limits and timeouts, and an audit of our own guard before trusting it with live data. The price is fixed once the plan is agreed, you get a working demo every week during the build, and the warehouse and all access stay in your own account.
If nobody can currently say which content categories actually drive subscriber retention, that gap compounds every editorial decision made without it. We would rather start with the tracking audit and show you exactly where the blind spots are than sell a full warehouse build before either of us knows what the real data actually looks like.
Pair this with an AI agent for media and publishers so subscriber-facing support questions get answered from the same clean data. See the full package breakdown on the analytics service page, or get a written plan with a fixed price for your organization.
FAQ
What does it cost to start?
A Warehouse plus dashboards build starts at $2,500 and takes 3 to 6 weeks: connectors to your CMS, subscription and ad platforms, marts and dashboards on content performance and retention. A lighter tracking audit alone starts at $800 and takes a week if you want to see the gaps first.
How long until we see real dashboards?
Three to six weeks for the full warehouse and dashboards, depending on how many systems need connecting and how clean the existing data is. We tell you honestly if a source system's data quality will slow things down.
Can the AI analyst answer which content drives the most subscriptions?
Yes, as long as that data exists in your CMS and subscription platform. It runs guarded, read-only SQL against a prepared data mart, one query at a time, and shows the query it ran so you can verify the answer.
Does this work with our existing CMS and subscription platform?
We connect to most CMS and subscription platforms that expose an API or a reliable export, including custom and self-built systems. We tell you upfront if a system genuinely has no reliable way to connect.
Is subscriber data kept private?
Yes. The warehouse lives in your own database, under your organization's account, and the AI analyst runs on a read-only role with no write access and no way to export raw subscriber data outside the guarded query layer.
Can it tell us why subscribers are churning, not just that they are?
To the extent your data captures it, yes, by joining content engagement and support history against churn events, so a pattern like low engagement before cancellation becomes visible rather than anecdotal.