Data to AI™ / Copilot/Amazon Ads
Give the retail media team an agent in Teams that answers questions on Amazon campaign returns, brand searches and DSP delivery from scheduled report data.
Less tokens, better answers.
Microsoft 365 Copilot
Which Sponsored Products campaigns had the highest ACOS on 14-day sales last month?
Generic - Kitchen ran the highest ACOS at 42.0%, more than four times Brand - Exact at 10.0%. Competitor ASINs came second at 33.0%.
| Campaign Name | Spend | Sales 14d | Acos Clicks 14d |
|---|---|---|---|
| Generic - Kitchen Sponsored Products: Campaign | 4,120 | 9,810 | 42.0% |
| Competitor ASINs Sponsored Products: Campaign | 2,460 | 7,450 | 33.0% |
| Brand - Exact Sponsored Products: Campaign | 1,350 | 13,500 | 10.0% |
Spend and Sales 14d use the same 14-day attribution window; Sales 7d is never mixed in.
Amazon Ads reports cover Sponsored Products, Sponsored Brands, Sponsored Display and Amazon DSP. Each source reads one report type for the profile you pick, daily or as a summary for the date range.
@Retail Media Desk Which Sponsored Products campaigns had Roas Clicks 14d below 2 last week, and how much Spend did they use?
Account managers mention the agent in the retail media channel during stand-up instead of waiting for the weekly deck, and agree on budget moves in the same thread.
@Retail Media Desk Line up weekly DSP Total Cost with Sponsored Brands Branded Searches for the last eight weeks.
In Microsoft 365 Copilot the brand team checks whether awareness spend on DSP orders is followed by more shoppers searching for the brand on Amazon.
Use the dataddo tools to describe the Sponsored Products Advertised Product entity and generate a TypeScript type for our dashboard loader.
A developer uses GitHub Copilot in Agent mode to read the field list through Dataddo, so the internal retail media dashboard matches the real Advertised Asin and Spend columns.
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| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Numbers at stand-up | Each account manager brings a different Amazon export to the meeting, so discussions in Teams start by reconciling numbers. | The same Copilot Studio agent answers everyone in Teams and Microsoft 365 Copilot from the same Amazon Ads flows. | One set of figures |
| When the agent looks things up | A generic assistant has no idea that Amazon campaign data exists anywhere it can reach, so it answers from general knowledge. | The server description you write in Copilot Studio tells the agent to call Dataddo for Amazon Ads campaign, search term and DSP questions. | Right tool for the question |
| Developer access | Engineers request Amazon reports through the API and work out column names by trial before building a dashboard. | GitHub Copilot in VS Code reads the same entities through .vscode/mcp.json, so field names come from Dataddo instead of guesswork. | Dashboards match the source |
| Token consumption | Copilot spends tokens exploring raw Amazon Ads columns, sampling rows and retrying until it understands the data. | Field definitions and relations are supplied up front, so the model skips the exploration and goes straight to the answer. | Less tokens per answer |
| Answer quality | Prone to wrong joins, invented fields and numbers that are hard to check. | Answers grounded in defined fields. In the Dataddo benchmark, 78.5% of questions were answered correctly, against 65.5% with plain CSV files. | Better answers |
The Amazon Ads datasets marketing teams use most, with their real field names. Copilot queries them by name through the Dataddo semantic layer.
Runs on the schedule you set over a relative date range, for example yesterday, that moves forward on every run. Older periods are loaded with a full data re-sync, and new data can lag while Amazon generates the report.
Amazon Ads connector →We asked 43 pre-registered questions of three real datasets - Google Search Console, HubSpot deals and Google Ads - and delivered the same rows to the same model in four ways. Only the data layer changed.
of questions answered correctly - the best of the four delivery methods tested
fewer confidently wrong answers - 14.0% of runs against 24.0% and 26.3%
more correct answers on messy CRM data - better on 8 questions, worse on none
Same model, prompts and rows in every condition; 759 graded runs, ground truth frozen before any run. On Google Search Console alone, documented CSV files did as well as Data to AI™ - the advantage comes from messier data such as HubSpot deals.
Read the benchmark →Send Amazon Ads reports such as Sponsored Products Campaign and Sponsored Brands Campaign to a Copilot destination or AI Model in Dataddo. In Copilot Studio, add a Model Context Protocol tool with https://headless.dataddo.com/mcp-data, use OAuth 2.0 with Dynamic discovery, then publish the agent to Teams and Microsoft 365 Copilot.
Yes. Publish the Copilot Studio agent through Channels > Teams and Microsoft Copilot. People mention it with @ and it queries the Amazon Ads flows attached to your Copilot destination.
From the server description you enter in Copilot Studio. Describe it as the source for Amazon Ads campaign, search term and DSP results, so those questions trigger a Dataddo call.
Yes. Add the server to .vscode/mcp.json, start it, sign in and use Copilot Chat in Agent mode. On Business and Enterprise an org admin must enable the MCP servers in Copilot policy.
Amazon builds reports asynchronously and busy API traffic slows them down. The agent can call data_status to report when each Amazon Ads flow last received data.
No. It reads extracted report data by entity and field. Budget, bid and targeting changes stay in the Amazon Ads console.