Expose Archived Program Flag & Improve Program Membership API for Large-Scale Instances
Hello Marketo Community,
We are currently operating a large Marketo instance and are facing scaling challenges related to program membership data extraction via the REST/Bulk APIs.
Current Limitation
At present:
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There is no API-exposed field that identifies whether a Program is archived.
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Archiving a program in the UI (e.g., moving it to an archived folder) does not translate into a machine-readable “archived” flag via the REST API.
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The Program Membership Bulk API requires filtering by Program ID.
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There is no supported endpoint to retrieve program membership changes between two timestamps without iterating through individual Program IDs.
Because of this, integrations must iterate across all program IDs to ensure no membership changes are missed — even for programs that are long archived and no longer operational.
Business Impact
For instances with thousands of programs:
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API calls increase significantly as program volume grows.
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Sync runtimes increase over time, even if most programs are inactive.
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There is no supported way to exclude archived/inactive programs from recurring membership checks.
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This creates scaling and performance constraints for downstream data integrations and analytics platforms.
Requested Enhancements
We would like to request consideration for one (or more) of the following improvements:
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Expose an “archived” (or equivalent) boolean flag via the REST API
This would allow integrations to safely exclude archived programs from ongoing membership syncs (assuming archived programs cannot have membership updates).
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Allow bulk program membership export without requiring Program ID filters
For example, enabling exports based purely on time windows (start/end timestamps) rather than per-program iteration.
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Introduce an endpoint to retrieve program membership changes between two timestamps
This would dramatically reduce the need to scan all program IDs during each integration cycle.
Why This Matters
As program volume grows over years of usage, performance and API efficiency become critical. These enhancements would:
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Improve scalability for enterprise customers
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Reduce unnecessary API consumption
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Improve sync performance for data warehouse integrations
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Enable better architectural patterns for reporting and analytics