Product Analysis · Source connector
ChatGPT Ads ELT Connector
Extract brings ChatGPT Ads product analysis data into data warehouses and databases for analysis with the rest of your business data. Teams use it to measure product usage, retention, and feature adoption. Flexible scheduling keeps reporting current for routine analysis and time-sensitive decisions.
View setup guide
Use cases
What teams use this connector for
Product adoption analysis
Use ChatGPT Ads data to measure product usage, feature adoption, and changes in engagement.
Cohort and retention reporting
Combine ChatGPT Ads behavior data with customer records to compare cohorts and retention trends.
Experience optimization
Identify ChatGPT Ads usage patterns and friction points that can guide product improvements.
Flexible reporting cadence
Refresh ChatGPT Ads reporting every 15 minutes, hourly, daily, or on a custom schedule as business needs change.
Warehouse and database analysis
Use Extract to make ChatGPT Ads product analysis data available in data warehouses and databases such as Snowflake, BigQuery, Redshift, and PostgreSQL for governed analysis.
Connector data
Available streams
| Stream | Description | Sync mode |
|---|---|---|
ad_account | Fields include id, currency_code, name, preview_url. | Full refresh |
ad_account_insights | Fields include end_time, id, start_time, ad_account_id. | Incremental |
ad_group_insights | Fields include end_time, id, start_time, ad_account_id. | Incremental |
ad_groups | Fields include campaign_id, id, bidding_config.billing_event_type, bidding_config.max_bid_micros. | Full refresh |
ad_insights | Fields include end_time, id, start_time, ad_account_id. | Incremental |
ads | Fields include ad_group_id, id, created_at, creative.body. | Full refresh |
campaign_insights | Fields include end_time, id, start_time, ad_account_id. | Incremental |
campaigns | Fields include id, budget.daily_spend_limit_micros, budget.lifetime_spend_limit_micros, created_at. | Full refresh |
Schema
Data model at a glance
ad_account
idstringcurrency_codestringnamestringpreview_urlstringtimezonestringurlstring
ad_account_insights
end_timeintegeridstringstart_timeintegerad_account_idstringad_account_budget_dailynumberad_account_budget_lifetimenumberad_account_namestringad_account_urlstring
ad_group_insights
end_timeintegeridstringstart_timeintegerad_account_idstringad_group_idstringcampaign_idstringad_account_budget_dailynumberad_account_budget_lifetimenumber
ad_groups
campaign_idstringidstringbidding_config.billing_event_typestringbidding_config.max_bid_microsintegercontext_hintsarraycreated_atintegerdescriptionstringnamestring
ad_insights
end_timeintegeridstringstart_timeintegerad_account_idstringad_group_idstringad_idstringcampaign_idstringad_account_budget_dailynumber
ads
ad_group_idstringidstringcreated_atintegercreative.bodystringcreative.file_idstringcreative.image_urlstringcreative.target_urlstringcreative.titlestring
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with ChatGPT Ads in Extract workflows.
Why Extract
Why Extract for ChatGPT Ads
- Rust-native performanceUse Rust-native data movement to refresh ChatGPT Ads reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect ChatGPT Ads API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine ChatGPT Ads data with customer and finance models, then send approved outputs through a separately configured Reverse ETL destination.
FAQ
Frequently asked questions
How does Extract handle incremental and full ChatGPT Ads syncs?
An incremental sync processes only new or changed data since a stored checkpoint or new date partition, while a full sync processes the complete selected dataset or date range again. By avoiding repeated processing and writes for the full dataset, incremental syncs can reduce destination database updates and warehouse compute costs. Stream schemas define full refresh, partition-based extraction. Behavior varies by stream and configuration. Extract records run details so teams can verify each sync.
Can I backfill historical ChatGPT Ads data with Extract?
Historical coverage for ChatGPT Ads: Backfill (Days) (backfill): The backfill setting controls the rolling lookback window for the daily Ads insights streams. Use the setup guide and run details to confirm the available source records and selected settings.
How can teams monitor ChatGPT Ads data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect ChatGPT Ads API activity, execution steps, and timing. This helps teams distinguish a source-data delay from a failed or incomplete run.
How often can Extract sync data from ChatGPT Ads?
Extract supports schedules every 15 minutes, hourly, daily, or on a custom cadence. Teams can choose a frequency that matches routine reporting, time-sensitive monitoring, or operational activation needs.
What does the ChatGPT Ads data model look like in the destination?
The Available streams table describes the datasets Extract can read from ChatGPT Ads. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.