Amazon Replenishment Data

Technical reference for DataHawk's Subscribe & Save performance and forecast datasets, including table names, grain, fields, and deprecations.

This page is the technical reference for DataHawk's Amazon Subscribe & Save performance and forecast datasets. Use it when you need table names, grain, common fields, deprecation notes, or the source-of-truth column reference.

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For business usage, metric interpretation, and inventory-planning examples, see Subscribe & Save Forecasting.

Available datasets

DatasetWhat it contains
FBA_RESPLENISHMENT_SNS_PERFORMANCEHistorical Subscribe & Save performance data
FBA_RESPLENISHMENT_SNS_FORECASTAmazon's forward-looking Subscribe & Save projections
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Deprecated and empty: the legacy SELLING_PARTNER.FBA_SNS_PERFORMANCE_SRC and SELLING_PARTNER.FBA_SNS_FORECAST_SRC tables are no longer populated and are marked obsolete. Migrate any queries to the FBA_RESPLENISHMENT_SNS_* tables above. See the changelog entry.

Grain

Both datasets follow the same grain: 1 row = 1 ASIN x 1 marketplace x 1 time interval

Common fields

ColumnDescription
ASINAmazon Standard Identification Number
MARKETPLACE_KEYMarketplace identifier (e.g. Amazon-US)
PRODUCT_KEYDataHawk internal product identifier; use it to join with other DataHawk tables
TIME_INTERVAL_START_DATEStart of the period covered by the metrics
TIME_INTERVAL_END_DATEEnd of the period covered by the metrics
CURRENCY_CODEISO currency code (USD, EUR, GBP, etc.)

Field families

Use the Exhaustive Column Referential for the complete, current field list. At a high level:

DatasetField families
FBA_RESPLENISHMENT_SNS_PERFORMANCEShipped subscription units, subscription revenue, active subscriptions, missed subscription deliveries, out-of-stock impact, revenue penetration
FBA_RESPLENISHMENT_SNS_FORECASTForecasted subscription revenue and forecasted subscription units for upcoming periods

Join guidance

  • Use PRODUCT_KEY when joining to DataHawk product reference tables.
  • Use ASIN and MARKETPLACE_KEY when joining to Amazon-facing product or marketplace analysis.
  • Keep the time interval in the join or filter condition. These datasets are period-based, not single-snapshot tables.

Where to go next

On this page