Consider this planning scenario: six months after a ChatGPT launch, a client asks which days and products produced the strongest response. The team saved monthly totals but not the detailed rows. Even if the totals are correct, they cannot answer the new question. An archive plan should start with that future question, not with a folder named exports.
OpenAI documents different reporting-history availability for different report types. Check the current limits for the granularity you need before designing your schedule. Do not assume that a general annual view means every hourly or product-level detail remains retrievable for a year. This guide describes an internal archive process and does not promise automatic historical exports from AthillyAds.
Choose what you may need to explain later
List three or four plausible future questions. An agency might need to reconstruct a launch decision, compare campaign pacing between months or explain the response to a particular offer. A retailer may need product-level observations. Each question implies a different grain and different supporting metadata.
Write the required grain in ordinary language: one campaign per day, one product per day, or one campaign for the entire period. Saving a monthly total cannot later recreate daily variation. Saving only names cannot reliably reconnect renamed campaigns. The archive needs the identity fields and dimensions that make the intended comparison possible.
Do not collect every available field merely because it exists. Additional data increases storage, access and maintenance obligations. Preserve what supports a defined analysis or operational requirement, and record the reason for keeping it. The archive should have a purpose that survives staff turnover.
Build a retention matrix
For each dataset, record its source, reporting grain, documented retrieval horizon, planned extraction frequency and internal retention decision. Keep the source-check date beside the platform limit because product behavior can change. The internal retention period is your organization’s decision and should not be confused with the platform’s retrieval window.
A hypothetical matrix might distinguish daily campaign performance from product detail and decision snapshots. The schedule for one dataset need not match the others. What matters is that the chosen extraction cadence leaves room to detect and repair a failed run before the needed historical period becomes unavailable.
This buffer is an operational choice, not a guarantee of platform uptime. If a dataset is important enough to archive, define who notices a failed extraction and who can repair it. A scheduled job without an owner can fail quietly until the information can no longer be recovered.
Refresh recent history deliberately
An archive that only appends yesterday’s results may freeze provisional values permanently. Decide which recent dates will be re-fetched and how the updated observations will be stored. Keep a distinction between the latest known value for an analysis and the exact snapshot used in an earlier decision.
One possible internal design stores immutable extraction files plus a derived table containing the most recent accepted observation for each row identity. This supports both auditability and current analysis, but it needs clear rules for acceptance and replacement. It is a design option, not a required platform architecture.
Record whether a row changed because the source updated or because your transformation changed. Without that distinction, an analyst can mistake a pipeline repair for an improvement in the ChatGPT campaign. Data lineage provides the connection between the source observation and the published figure.
Prove that the archive can be read
Every successful extraction should pass basic checks for scope, date coverage, row identity and completeness. A file containing an error message is not a report archive. A file containing only the first page is not complete simply because it exists in storage.
Periodically restore a selected historical interval into a separate analysis workspace and reproduce a known total. Include a renamed campaign and a period with a corrected observation if those cases exist. This tests the full recovery path rather than merely checking that files have nonzero sizes.
Keep the restore procedure readable by someone other than its author. Document file format, encoding, time convention, currency handling and the meaning of missing values. These details determine whether the archive remains usable when a library, analyst or reporting tool changes.
Archive for a future question
- Choose detail
A product-and-day question requires that row grain.
- Plan recovery
The extraction schedule needs repair time before history expires.
- Test restoration
Reproduce a known total from the archive using documented rules.
Plan the ending as well as the collection
Assign an owner for access reviews and eventual disposal under your organization’s policy. A former client account’s reporting history should not remain broadly accessible just because nobody reviewed an old folder. Separate credentials from exported business data and avoid retaining unnecessary personal information.
When a platform report is no longer retrievable, state exactly what your archive covers. Missing historical granularity cannot be reconstructed by averaging a total or inventing a daily pattern. A transparent coverage statement is better than a fabricated trend.
Use snapshot preservation for decision evidence and pagination checks for complete retrievals. Verify the applicable history limits in OpenAI reporting. The archive is successful when it can answer the campaign questions you chose, with a known scope and a tested recovery path.
Sources and scope
Select historical reporting detail, extraction cadence and restore checks based on future campaign questions and documented availability.
Working methods and examples are editorial suggestions. Check current platform requirements and available features before implementation.