--- title: "Google Ads Dashboard, Built by AI on Your Data Model | OWOX" canonical: "https://www.owox.com/dashboards/google-ads-dashboard-for-google-sheets" updated: "2026-10-26" --- AI dashboard · Google Ads # Google Ads dashboard, built by AI on your Data Model Google Ads spend against the orders and net sales the store recorded, campaign by campaign, and the CTR mistake most reports make. Claude built it through OWOX MCP on a governed Data Model; the same Data Marts can run as a Report in Google Sheets. 4 Data Marts · Built by Claude via OWOX MCP · Refreshed every Report run · [E-commerce data model](/data-models/e-commerce) [Get Started Free](https://www.owox.com/app-signup) · or [book a call](https://calendly.com/owox/book-a-call) ## The dashboard ## What each view answers ## Three questions, one [Data Model](/data-models/e-commerce) 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. ![The E-commerce data model: 12 Data Marts and how they join](https://cdn.owox.com/www/portfolio-management-data-model/e-commerce/diagram/10.svg/w=1600,fit=scale-down,format=auto) [E-commerce data model · 12 Data Marts · 15 relationships · open it →](/data-models/e-commerce) Summary ### Is Google Ads paying for itself? Opens with what the numbers mean: what Google Ads leaves after its spend, which campaign returns the most per $1 and which the least. Then spend, clicks and the conversions Google claims against the orders the store recorded. [Unified Ad Spend](/data-models/e-commerce#mart-unified-ad-spend) [Sessions](/data-models/e-commerce#mart-sessions) [Purchases](/data-models/e-commerce#mart-purchases) [ROAS](/metrics/return-on-ad-spend) Campaigns ### Which campaigns earn their budget? Every campaign's spend, store orders, net sales and profit after ad spend, with net sales per $1 next to what Google claims per $1. [Unified Ad Spend](/data-models/e-commerce#mart-unified-ad-spend) [Sessions](/data-models/e-commerce#mart-sessions) [Traffic Sources](/data-models/e-commerce#mart-traffic-sources) [Purchases](/data-models/e-commerce#mart-purchases) [CPC](/metrics/cost-per-click) CTR trap ### Is our click-through rate real? Click-through rate two ways, month by month: all clicks over all impressions, and the average of each day's rate. The average runs two points high once a low-volume campaign joins. [Unified Ad Spend](/data-models/e-commerce#mart-unified-ad-spend) [CTR](/metrics/click-through-rate) ## Metrics ## 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 spend](/metrics/return-on-ad-spend) [Click-through rate](/metrics/click-through-rate) [Cost per click](/metrics/cost-per-click) [Ad spend](/metrics/advertising-costs) ## Connected, not a snapshot ## 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. ![Two exported files showing 29 and 32 orders for the same days](https://cdn.owox.com/www/content-marketing-dashboard/net-sales-review/body-vector/1-7by02w.svg/public) Which one goes to the board? On your Data Model ### CTR is defined once, not averaged A Data Model computes click-through rate from summed clicks and impressions at whatever grain a report asks for, so a sheet, a dashboard and an AI answer cannot disagree on it. ![Purchases in the e-commerce Data Model, joined through Orders to Sessions and Customers](https://cdn.owox.com/www/portfolio-management-data-model/e-commerce/diagram/10.svg/w=1600,fit=scale-down,format=auto#mart-purchases) 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. ## How it is built ## 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. 1. 01 · Your warehouse · BigQuery · Snowflake · Databricks · Redshift · Athena · Your data stays where it is. OWOX runs on it and keeps no copy. 2. 02 · Data Model · Data Marts · joins · metrics · Your analyst defines once what net sales, an order and a channel mean. 3. 03 · OWOX MCP and API · governed · permissions · The only door the AI gets: the Data Marts it may read, nothing raw. 4. 04 · Claude or ChatGPT · your own AI · Ask for the dashboard in plain words. It builds it on the Data Model. 5. 05 · AI dashboard · Report · every run · Kept as a Report, refreshed every time it runs, like Sheets or Excel. ## Build yours ## Build this on your own data Connect your warehouse, describe your business once, and let your AI build the dashboards your team asks for. [Get Started Free](https://www.owox.com/app-signup) · or [book a call](https://calendly.com/owox/book-a-call) ## Links Pages this page links to, on this site and on docs.owox.com. Where the page has a Markdown twin, its address follows the link. - [E-commerce data model](https://www.owox.com/data-models/e-commerce) — /data-models/e-commerce.md - [Get Started Free](https://www.owox.com/app-signup) - [ROAS](https://www.owox.com/metrics/return-on-ad-spend) — /metrics/return-on-ad-spend.md - [CPC](https://www.owox.com/metrics/cost-per-click) — /metrics/cost-per-click.md - [CTR](https://www.owox.com/metrics/click-through-rate) — /metrics/click-through-rate.md - [Ad spend](https://www.owox.com/metrics/advertising-costs) — /metrics/advertising-costs.md