SKU Level Email Receipt Data from 2M Users

YipitData/SKU Level Email Receipt Data from 2M Users is a Email Receipts data product published on databricks-marketplace and indexed by The Alternative Data Encyclopedia.

YipitData publishes SKU Level Email Receipt Data from 2M Users as an offering in the Email Receipts signal family.

The publisher is covered in its own article: YipitData.

The data

YipitData maintains it as part of its Email Receipts portfolio and lists it through databricks-marketplace.

Structure, access and licensing

License: other. Pricing: Commercial terms (Databricks Marketplace).

Access is through a dataset that is obtained from the databricks-marketplace listing. The listing does not state an explicit refresh schedule, which is worth confirming before backtesting.

The signal

Digitized purchase receipts collected from consenting consumers through rewards apps and connected inboxes, normalized into line-item purchase records. The signal provides basket-level truth — exact items, quantities and prices actually paid — that card panels see only as merchant totals.

Item-level visibility reveals trade-down within categories, promotional dependence and basket-share shifts weeks before company disclosures quantify them. Brand analysts track new-buyer acquisition and repeat rates as leading indicators of volume growth, while price-paid data exposes real inflation at shelf rather than sticker. Because receipts capture offline and online purchases uniformly, they reconcile channels that web-scraping alone cannot. Receipt-level records carry SKU or UPC identifiers, quantities, list and paid prices, discount amounts, retailer, payment method and timestamp. Aggregation yields brand- and category-level sales series, basket composition matrices, cross-retailer price comparisons and repeat-purchase cohorts. Derived products benchmark promotional lift and buyer acquisition across competing brands.

Consumers submit paper receipts by photographing them inside reward apps in exchange for points, while email-connected flows authorize parsing of e-receipts through OAuth with explicit consent. Parsers extract merchant identity and line items using retailer-specific templates and machine-learning fallbacks, then normalize product text to UPC taxonomies. Panels are weighted toward demographic benchmarks and merged with transaction-panel style scaling before delivery as aggregates.

Caveats and compliance

Participants self-select into reward programs, skewing panels toward deal-responsive households. Long-tail retailers and cash-only venues remain underrepresented, and unusual receipt formats raise parsing error rates. Promo-chasing users overweight discounted purchases, biasing measured promotion shares upward.

Email ingestion is privacy-sensitive: buyers should require explicit opt-in, data-minimization and retention limits, with GDPR-style impact assessments standard in Europe. Emerging state laws, including health-data statutes covering pharmacy purchases, increasingly constrain how line items may be used or sold.

Who uses this signal

CPG brand and retail strategy teams use baskets and price-paid truth for assortment and promotion decisions; consumer equity analysts model brand-level volume and mix ahead of earnings. Advertising platforms buy the same records for closed-loop campaign measurement.

Complementary signals

This kind of signal pairs naturally with adjacent categories of the encyclopedia:

Further reading

About the provider

Analyst-ready research products built from web, transaction, and app data across consumer and internet sectors.