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Brand Tracker

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Overview

Credit & debit card transaction data aggregated to 12K brands with state & channel breakouts for corporate and PE/VC customers, with the most cards and most accuracy. The tables in this dataset are Aggregated by state, industry, channel (online, offline, unknown), and brand.

Data InformationValue
Refresh CadenceDaily
Historical Coverage2018-Present
Geographic CoverageUnited States

Schema

Create a Dewey Data login and navigate to the Table Structure view in the product for a description of each variable.
Brand Tracker

Key Concepts

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This is a multi-table dataset

Navigate to the drop down menu below the Download Sample button to review the various tables in the dataset. Review our resources for support accessing multi-table datasets via API

Data Filters

FiterDescription
Date RangeNo table reflects data associated with transactions received earlier than 1/1/2018.
Returns ExcludedReturn transactions are not included; this includes all transactions with values < $0.
Non-U.S. ZIPSCards which do not have a valid U.S. ZIP code and associated transactions are not included. Non-U.S. ZIP codes include US territories and military ZIPs.
Transactions >$10KTransactions greater than $10K are not included
Cardholders spending > $50K/monthCardholders who have spent more than $50K in any calendar month of our data history are excluded.
High-Volatility Financial InstitutionsAny financial institution (including within a processor) that has seen a change in cards or transactions per card that exceed volatility filters; any institution without transactions going back to the beginning of the dataset history.

Matching Merchant Descriptions to Brands:

  • Merchant names in the raw data are matched to brands.
  • Additional data like MCC codes (industry codes), transaction sizes, and proprietary fields are used to refine this matching.
  • Exceptions are tracked to ensure that subsidiary brands are included while non-revenue transactions are excluded.

Handling Complex Scenarios:

Special cases like "store-within-a-store" setups are carefully managed to credit revenue accurately to the appropriate brand. This ensures proper revenue attribution to the brand benefiting from the transaction.

Channel Identification:

  • Transactions are categorized as either ONLINE or OFFLINE/UNKNOWN based on tagging processes.
  • Catalogue and call center purchases are included in the ONLINE category.
  • If not all online transactions for a brand can be confidently identified, they are flagged as OFFLINE/UNKNOWN.
    If there’s insufficient confidence in the data, the channel is marked as NA.

Attributing Multi-Brand Transactions:

  • A single transaction can relate to multiple brands. For example: "PayPal DoorDash Wendy’s" might tag Wendy’s as the Main brand, DoorDash as the Delivery brand, and PayPal as the Payment brand.
  • Delivery brands’ revenue (e.g., fees and tips) is excluded from Main brand sales to avoid discrepancies caused by differing revenue capture rates.

Revenue Reporting for Delivery Brands:

Separate files (e.g., day_delivery and period_delivery) break out revenue related to Delivery brand transactions when a Main brand can be identified.

Updated about 8 hours ago