Credit & Debit Card Transactions

Overview

Credit & debit card transaction data aggregated to the brand for fundamental analysis on demo, geo, ticket buckets, cross-shop, and loyalty & retention.

There are two products for Credit & Debit Card Transactions. They have the same attributes but use different panels for each dataset. See the Panel Information section below for more detail.

Schema

Create a Dewey Data login and navigate to the Table Structure view in the product for a description of each variable.
Credit & Debit Card Transactions

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

Panel Information

Cohort USA1:

• This panel consists of shoppers that have made at least one purchase every 70 days for the past 1120 days
• This panel is then balanced for age and income
• The panel is also geo-balanced
• There is also some temporal balancing to counteract card usage decay

Combined Cohort USA1-USA2:

• This panel consists of shoppers that have made at least one purchase every 35 days for the past 1960 days
• This panel is NOT balanced for age and income
• The panel IS geo-balanced
• There is NOT any temporal balancing to counteract card usage decay

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.

FAQs

If I’m looking at the Cross-Shop of merchant A and merchant B, how do I figure out how many total shoppers there were at merchant A to calculate the denominator?

You’re only showing the number of cross-shoppers. The file contains the cross-shop and A with A and B with B, which will give you the total number of shoppers at the respective merchants.

Why can’t I see all combinations of cross-shop in the data?

Because the size of the data for all cross-shop combinations would be limiting, we only provide cross-shop between merchants in the same subindustry. The exceptions are the key merchants Walmart, Target. Amazon, Costco, Shein, Temu, and Instacart.