Data to AI™ / ChatGPT/Google Ads
Review search terms, budget pacing and keyword research from Google Ads in a ChatGPT chat, and confirm each data lookup before ChatGPT runs it.
Less tokens, better answers.
ChatGPT
Which search campaigns lost the most impression share to budget last month?
Competitor terms lost 44% of its eligible search impressions to budget and Generic - Running shoes lost 31%. Brand - Exact lost only 2%.
| Campaign - Name | Cost Micros (Spend) | Search Impression Share | Search Budget Lost Impression Share |
|---|---|---|---|
| Competitor terms Campaign Report | 5,410 | 38% | 44% |
| Generic - Running shoes Campaign Report | 12,880 | 54% | 31% |
| Brand - Exact Campaign Report | 3,240 | 91% | 2% |
Cost Micros (Spend) was divided by 1,000,000 to match the amounts in the Google Ads interface.
Google Ads data comes as Reports with Metrics, Reports without Metrics and Keyword Planner Historical Metrics. You pick a report type such as campaign, keyword, search term or shopping, then the attributes, segments and metrics to extract.
Which Search Term View - Search Term values spent the most Cost Micros (Spend) in the last 30 days with zero Conversions? Convert micros to currency.
A PPC specialist approves each query on the Search Term View Report, and the answer becomes the negative keyword proposal sent to the account owner.
Call data_status for the Google Ads flows, then compare month-to-date spend with Campaign Budget - Amount Micros for each budget.
In the last week of the month, the freshness check confirms yesterday's data is in before anyone cuts or raises a budget based on the pacing table.
From Keyword Planner Historical Metrics, list keywords with Avg Monthly Searches above 1,000, low Competition and their top of page bid range.
Before building a new ad group, the shortlist combines search volume with Low and High Top Of Page Bid Micros, converted to currency in the answer.
Data to AI™ is free while in private pre-release. We onboard teams in waves and shape the product with their feedback. At launch, it will be priced like the AI assistants your team already uses. Need tailored contracts or volume pricing? Talk to us about Enterprise.
Serve governed business data to Claude, ChatGPT and other AI tools over MCP.
Free during pre-release
Flexibly deploy Dataddo in any cloud or hybrid environment.
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| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Combining report types | Campaign, keyword and search term exports are built separately and merged by hand before ChatGPT can compare them. | Each report type is its own Dataddo source, and all of them are reachable from one ChatGPT developer mode app. | One chat, every report |
| Field combinations | A hand-written API query with an incompatible mix of metrics, attributes and segments fails the whole report. | The Dataddo wizard flags combinations that cannot be queried together, and Test on Sample Data previews the report before you save it. | Reports that run |
| Seeing each lookup | Answers come from an attached file, with no record of which report or date range they were built on. | Developer mode asks you to approve each tool call, so you see whether ChatGPT runs query, describe_entity or data_status. | Every call approved |
| Token consumption | ChatGPT spends tokens exploring raw Google 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 Google Ads datasets marketing teams use most, with their real field names. ChatGPT queries them by name through the Dataddo semantic layer.
Reports with Metrics read a relative date range, for example yesterday, that moves forward on every scheduled run; older periods are loaded with a full data re-sync. Reports without Metrics pull all current data on each run.
Google 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 Google Ads with Dataddo, for example Search Term View, Campaign Budget and Keyword Planner Historical Metrics, and attach the sources to a ChatGPT destination or AI Model. Then turn on developer mode in ChatGPT on the web, create an app with https://headless.dataddo.com/mcp-data using OAuth and sign in.
Plans with developer mode: Pro, Plus, Business, Enterprise and Education, on the web. Business and Enterprise admins may need to allow developer mode for the workspace.
Google Ads reports costs in micros. Ask ChatGPT to divide fields such as Cost Micros (Spend) or Campaign Budget - Amount Micros by 1,000,000 before comparing.
ChatGPT asks you to confirm each tool call in developer mode. You see whether it calls query, describe_entity or data_status before any Google Ads data is read.
The Google Ads API reports Performance Max only at campaign level. Attach a campaign level source and ask ChatGPT to use it for Performance Max questions.
No. ChatGPT only reads the data Dataddo extracted. You apply the negative keywords or bid changes in Google Ads yourself.