Dataset

Purchase-based brand analytics

US retail behaviour inferred from itemized receipts captured through the Fetch rewards app: share of wallet, brand switching, basket composition and price trends.

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Dataset card

Fetch's Purchase-based brand analytics is a commercial dataset that translates consumer receipt data into brand-level purchase metrics for CPG market intelligence. It is offered under a paid license and is positioned for share-of-wallet and consumer-purchase analytics.

What this dataset contains

The product aggregates email-receipt data into brand purchase metrics for consumer packaged goods categories. Tags describe the coverage as receipts, consumer purchases, CPG, and share-of-wallet, indicating an emphasis on household-level buying behavior rather than point-of-sale aggregates. The publisher, Fetch, is known for its receipt-scanning rewards app, which supplies the underlying transaction stream. No row count, time range, or retailer coverage is disclosed in the provided facts.

Structure and fields

The provided schema information is '(unknown)', so individual column names such as basket identifiers, item UPCs, brand names, or spend totals cannot be quoted from the facts. The dataset is delivered under a commercial license and no sample rows, masked or otherwise, are available for inspection. Analysts evaluating the feed should request a data dictionary directly from the publisher before purchase.

How analysts use it

Given the share-of-wallet and CPG tags, plausible long-only and hedge fund applications include tracking brand-level repeat purchase rates, monitoring private-label substitution versus national brands, and generating alt-data overlays for staples, beverages, and household products earnings. Because fields and historical depth are not specified, any deeper use case such as cohort-level lifetime value modeling would require additional confirmation from Fetch. Treat the feed as a supplementary consumer-demand signal rather than a primary forecast input.

Documentation & references 2