AI dashboard · paid channels
Paid channels report, built by AI on your Data Model
Every paid platform's spend against the sessions, orders and net sales it brought, and against what each platform claims. Claude built it through OWOX MCP, and every number comes from a governed Data Mart, not from the AI's guess.
Four questions, one Data Model
Every view answers one question a marketing or sales lead asks. Between them they read four of the twelve Data Marts in the e-commerce Data Model below, and each view names the ones it uses, so you can open the definition behind any number.
Summary
What is paid media returning?
Opens with what the numbers mean: whether paid media pays for itself, how spend and its return stand against the budget plan, which platforms lose money, where the best return is and how far the claims run ahead. Then spend across seven platforms against paid sessions, conversion and the net sales the store recorded, per $1, next to what the platforms claim.
Platforms
Which platform earns its budget?
Spend, clicks, sessions, conversion, store orders, store net sales and profit after ad spend for every platform, with the platform's own CPA and a Scale, Hold or Cut call from a rule printed on the page.
Claims vs store
How far apart are the claims?
Conversions each platform reports against the orders the store recorded from its paid visits.
Trend
Where did the budget move?
Monthly spend stacked by platform since January 2025.
The metrics on this dashboard
Each one is defined once in the Data Model and read the same way in every view. Follow a chip for what the metric means and how to calculate it.
Return on ad spendCost per acquisitionConversion rateAd spend
An AI dashboard is only as true as what it reads
Most AI dashboards start from an exported file. That works until the second export.
On a CSV
The AI guesses what revenue means, every time
Each export is its own version of the truth, so two dashboards give two numbers. The file goes to whoever has it, and it is out of date the moment it is saved.
Which one goes to the board?
On your Data Model
Every platform on one grain, with one naming
Unified Ad Spend brings seven platforms to one daily grain and the source names the store's sessions use, so each platform's spend meets the revenue it bought.
One definition, every view.
Connecting the AI straight to the warehouse is not enough either. A warehouse rarely holds the business context, so the AI still guesses what the numbers mean. The Data Model is where that context lives.
From your warehouse to a dashboard in five steps
Your analyst does step two once. After that, anyone with access asks their AI for the dashboard they need.
- 01Your warehouseBigQuerySnowflakeDatabricksRedshiftAthenaYour data stays where it is. OWOX runs on it and keeps no copy.
- 02Data ModelData MartsjoinsmetricsYour analyst defines once what net sales, an order and a channel mean.
- 03OWOX MCP and APIgovernedpermissionsThe only door the AI gets: the Data Marts it may read, nothing raw.
- 04Claude or ChatGPTyour own AIAsk for the dashboard in plain words. It builds it on the Data Model.
- 05AI dashboardReportevery runKept as a Report, refreshed every time it runs, like Sheets or Excel.
Build this on your own data
Connect your warehouse, describe your business once, and let your AI build the dashboards your team asks for.