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.
| Data Information | Value |
|---|---|
| Refresh Cadence | Weekly |
| Historical Coverage | Varies by Product |
| Geographic Coverage | United States |
Schema
| Name | Description |
|---|---|
| VERSION | Panel version; corresponds to most recent file date used in analysis |
| BRAND_NAME | Name of individual brand |
| BRAND_ID | Unique ID associated to brand |
| CROSS_BRAND_NAME | Name associated with cross-shopped brand |
| CROSS_BRAND_ID | Brand ID associated to cross-shopped brand |
| PERIOD_TYPE | Type of time period |
| PERIOD | Specific period name |
| PERIOD_START_DT | Date when period starts |
| PERIOD_END_DT | Date when period ends |
| SPEND_AMOUNT | Cumulative spend associated with period and brand; weighted as part of panel |
| TRANS_COUNT | Number of transactions associated with period and brand; weighted as part of panel |
| INDIVIDUAL_COUNT | Number of individuals associated with period and brand; weighted as part of panel |
Key Concepts
This is a multi-table dataset
Navigate to the drop down menu below the
Download Samplebutton to review the various tables in the dataset. Review our resources for support accessing multi-table datasets via API .
Data Filters
| Filter | Description |
|---|---|
| Date Range | No table reflects data associated with transactions received earlier than 1/1/2018. |
| Returns Excluded | Return transactions are not included; this includes all transactions with values < $0. |
| Non-U.S. ZIPS | Cards 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 >$10K | Transactions greater than $10K are not included |
| Cardholders spending > $50K/month | Cardholders who have spent more than $50K in any calendar month of our data history are excluded. |
| High-Volatility Financial Institutions | Any 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. |
Panel Information
Cohort USA1:
- Credit skewed
- 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:
- Debit skewed
- Consists of shoppers that have made at least one purchase every 35 days for the past 1960 days
- The panel is geobalanced
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.
Updated 9 days ago