Data to AI™ / ChatGPT/Amazon Ads
Check budget pacing, placement results and product sales from Amazon Ads in a ChatGPT conversation, with every data lookup confirmed by you before it runs.
Less tokens, better answers.
ChatGPT
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.
Compare month-to-date Spend with Campaign Budget Amount for every Sponsored Products campaign and flag the ones likely to run out before month end.
On the 15th a marketplace manager approves the tool calls and gets a pacing table. The confirmations show which Amazon report ChatGPT read before any number appears.
Using Campaign Placement, compare Sales 14d per Spend for each Placement Classification this quarter, and add Top Of Search Impression Share.
Before raising a top of search bid adjustment, ask for the placement split and paste the summary into the change ticket, so the decision has numbers attached.
Run data_status first, then list the ten Advertised Sku values with the most Units Sold Clicks 14d last month and their Spend.
Starting with data_status shows whether yesterday's report has arrived. Then follow up in the same chat on whether Spend grew faster than units for those SKUs.
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| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Late Amazon reports | A report downloaded early in the morning can miss yesterday, because Amazon builds reports asynchronously, and nobody notices the gap. | ChatGPT can call data_status to show when each Amazon Ads flow last received data before it answers your question. | Gaps are visible |
| Several marketplaces | Switching profiles in the console and exporting each marketplace by hand produces a pile of files to merge before ChatGPT sees them. | Each Dataddo source reads one profile, meaning one account and marketplace, and every source attached to the destination is reachable from one app. | Every marketplace in one chat |
| Control over each lookup | An uploaded file is read in full, and there is no record of which report a given figure came from. | Developer mode asks you to confirm every tool call, so you see which Amazon Ads report is queried before any data is read. | Every lookup approved |
| Token consumption | ChatGPT 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. ChatGPT 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 →Extract Amazon Ads reports such as Sponsored Products Campaign and Advertised Product with Dataddo, then attach them to a ChatGPT destination or AI Model. In ChatGPT on the web, switch on developer mode, create an app with the MCP server URL https://headless.dataddo.com/mcp-data, choose OAuth and sign in to Dataddo.
Developer mode is available on Pro, Plus, Business, Enterprise and Education, on the web. On Business and Enterprise a workspace admin may need to allow it first.
ChatGPT asks for confirmation before each tool call in developer mode. You see which Dataddo tool runs, for example query on the Search Term report, before any data is read.
Yes. Each report type is a separate Dataddo source because Amazon serves each from its own endpoint, but all sources attached to the ChatGPT destination are reachable from one app.
Amazon builds reports asynchronously, so data can arrive after the scheduled run. Ask ChatGPT to call data_status to see the freshness of each flow.
Yes. In the app settings you can turn individual tools on or off, for example keep query and describe_entity but switch off refresh.