One warehouse for ads, checkout and cohorts,
and real unit economics per channel
A course business usually has ad spend in one dashboard, checkout numbers in a second, and completion or retention nowhere at all, which makes it impossible to say which channel actually produces students who finish and come back. We connect it all into one warehouse and give you the real unit economics per cohort and channel.
The problem in online courses and coaching
A typical course or coaching business tracks ad spend in the ad platform’s own dashboard, checkout and revenue in the payment processor, and enrollment or completion in the course platform, three systems that were never designed to agree with each other. Platform-reported attribution for digital products commonly overstates or understates true conversion by 20 to 40 percent once cross-device behavior, ad blockers and multi-touch journeys are accounted for, which is an industry pattern rather than a flaw unique to any one platform, and it means a founder deciding where to spend the next ad dollar is often deciding on numbers that do not reflect reality.
The deeper gap is retention and completion, which almost never makes it into any dashboard at all. A channel that produces cheap enrollments but low completion and high refund rates can look like the best-performing channel in an ads dashboard while actually destroying margin once support time, refunds and reputation are counted. Without a single warehouse joining spend to checkout to what actually happens inside the course, a business is optimizing for the wrong number without knowing it, and the decision that matters most, which channel and which offer to scale, gets made on the metric that is easiest to see rather than the one that is correct.
Cohort-level blindness compounds the problem. A course business that looks at lifetime totals, total revenue, total refunds, total completions, averages away exactly the signal that matters: whether the offer, the price point or the audience changed between one launch and the next. A founder comparing this quarter’s launch to last quarter’s on gut feeling alone is comparing two different audiences, two different ad accounts and sometimes two different offers, without any of the underlying numbers broken out cleanly enough to say which change actually caused which result.
What we build for online courses and coaching
A warehouse joining ad spend, checkout and course data. Meta, Google and TikTok Ads on the spend side, Stripe or your payment processor for checkout and refunds, Kajabi, Teachable, Thinkific or a custom platform for enrollment, progress and completion, all reconciled into one PostgreSQL database with a daily sync and backfill so history is not lost.
A full funnel from click to completion. Ad click, landing page, checkout start, checkout completion, module progress, course completion, mapped as one funnel instead of three disconnected numbers, so you can see exactly where a cohort’s attention or money is being lost.
Unit economics per cohort, channel and offer. CAC, LTV, refund rate and completion rate computed per cohort and per channel rather than averaged across your whole history, since a launch two months ago and the one running now rarely look the same.
Dashboards built around the decisions you actually make. Which channel to scale, which offer to retire, which cohort’s completion rate signals a content problem rather than a marketing one.
An AI analyst for ongoing questions. A read-only role on your warehouse with a guarded SQL layer, answering questions like “which channel brought the highest-completion cohort last quarter” with the query it ran, in Telegram, instead of waiting for a report.
Alerts for the numbers that need a fast reaction. Spend spiking against a stale budget, checkout conversion dropping mid-launch, or a refund rate climbing on a specific cohort all trigger an alert rather than waiting to be noticed in a weekly review, which matters most during the exact window a launch is actually running.
How it works in 3 to 6 weeks
- Audit. What exists across your ad accounts, checkout and course platform, what is tracked correctly, and what is wrong or missing. About one week.
- Model. Which questions the business must be able to answer drive the design of the data marts, not the other way around.
- Connectors and sync. Raw data pulled from each platform with a daily job and historical backfill, reconciled against what you actually see in your bank account.
- Dashboards and alerts. Built with the people who will actually use them, with alerts to Telegram for anomalies in spend, conversion or refund rate.
- AI analyst. A read-only role, a guarded SQL layer, tested on a set of real recorded questions before being trusted with live data.
What it costs
| Package | Price | Best for |
|---|---|---|
| Tracking audit | from $800 | Finding out what your current tracking actually sees and misses, with a fix list |
| Warehouse and dashboards | from $2,500 | Ad spend, checkout and course platform in one database, with cohort and channel economics |
| AI analyst and monitoring | from $5,000 | Everything in the warehouse package plus an AI analyst in Telegram and anomaly alerts |
Final price depends on how many platforms you connect and how far back the history goes.
Typical results
Course businesses that build a proper warehouse typically discover that platform-reported attribution missed 20 to 40 percent of real enrollments or misattributed them to the wrong channel, an industry pattern rather than a number specific to any one case, which routinely changes which channel looks like the winner. Unit economics computed per cohort instead of averaged commonly reveal that one or two cohorts were unprofitable once refunds and support time are counted, a signal invisible in a simple revenue-minus-spend view. Completion and retention data, once connected to acquisition channel, typically shows that the cheapest channel is not always the one producing students who finish and refer others. Refund rate, tracked per channel and per offer rather than as one blended figure, commonly turns out to concentrate heavily in a single segment, which is a far more actionable finding than an average that hides it. Our own numbers are in the case studies linked below.
Why Senator Media
We build this warehouse the same way we build one for our own businesses: the questions come first, the data model follows, and the AI analyst only gets access to a mart we have already audited for mistakes, not raw platform exports. Pricing is fixed before work starts, not billed by the hour, and you see a working connector or dashboard every week during the build rather than a single reveal at the end. After launch, the warehouse keeps syncing daily and we remain available for tuning as your funnel and offers change. We have built unit-economics models for other businesses where the honest answer was that the numbers did not work yet, and reporting that clearly, with the specific lever that would fix it, is the same standard we hold a course business’s numbers to.
If the funnel itself, not just the numbers behind it, needs work, the AI agent for online courses handles sales qualification and checkout recovery on the same data, and the full analytics and data service covers everything else we build the same way. For an example of a warehouse joining ad platforms, a store and a CRM into one picture, see the analytics hub built for two brands.
Tell us which platforms you run ads, checkout and your course on, and we will send back a fixed price and a three-to-six-week plan: get in touch.
FAQ
How much does this cost and what do we get for it?
From $2,500 for a warehouse connecting your ad platforms, checkout and course platform, with dashboards covering spend, conversion and cohort retention. An AI analyst and ongoing monitoring layer on top is $5,000 and up. A simpler tracking audit alone starts around $800 if you want to know what is broken before committing to the full build.
How long does it take?
3 to 6 weeks for the warehouse and dashboards. Adding the AI analyst and monitoring extends that to 6 to 10 weeks. You see working pieces, a connector live, a first dashboard, every week rather than waiting for one big reveal.
Which ad platforms, checkout and course platforms do you connect?
Meta Ads, Google Ads and TikTok Ads on the spend side, Stripe, PayPal or your payment processor for checkout, and Kajabi, Teachable, Thinkific or a custom platform for enrollment and completion data, plus GA4 or PostHog for site behavior.
Can an AI analyst really be trusted with real business numbers?
It runs read-only SQL on a prepared data mart, one query at a time, with forced limits, and it shows the query it ran rather than a black-box answer. We audit our own SQL guard before trusting it with live data; the answer is only as good as the mart, which is why the mart comes first.
We are a solo coach, not a big course business. Is this overkill?
Start with a tracking audit rather than the full warehouse. For a business running one or two cohorts a year, the audit alone usually surfaces where attribution is lying before you spend on the bigger build.
Can it show economics per cohort, not just overall?
Yes, that is the main point. A launch two cohorts ago and the one running now can have very different CAC, completion and refund rates, and averaging them together hides exactly the signal you need to decide what to change next.