Daily Consumer Spend by Company
Overview
Company Data for Academic Finance Research Covering Company-Level Aggregates of daily spend and transactions as well as important company metadata. Tables are aggregated by transaction info and period info.
| Data Information | Value |
|---|---|
| Refresh Cadence | Weekly |
| Historical Coverage | 2018-Present |
| Geographic Coverage | United States |
Schema
| Name | Description |
|---|---|
| VERSION | Panel version; corresponds to most recent file date used in analysis |
| SYMBOL | Contains the ticker symbol of public companies or the parent company name for private companies |
| SYMBOL_ID | Unique ID associated to symbol |
| COMP_BASE_FLAG | Flag of 1 when these sales are included in the comparable/same stores sales calculation |
| PARTIAL_PERIOD_FLAG | Flag of 1 when this period is not complete |
| PERIOD_TYPE | Type of period: COMP_QTR is each individual company's comparable period |
| PERIOD | Specific calendar week, calendar month, or comparable period that the data is calculated for |
| PERIOD_START_DT | Date when period starts |
| PTD_END_DT | Date when period ends if complete or the most recent data date if in-process |
| SPEND_AMOUNT_USD | 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 |
| AVG_TKT_USD | Average spend per transaction |
| YA_PERIOD | Specific year-ago calendar week, month, or comparable period |
| YA_PERIOD_START_DT | Date when year-ago period starts |
| YA_PTD_END_DT | Date when year-ago period ends or partial if the current period is partial |
| YA_SPEND_AMOUNT_USD | Cumulative spend in year-ago period; weighted as part of panel |
| YA_TRANS_COUNT | Number of transactions in year-ago period; weighted as part of panel |
| YA_AVG_TKT_USD | Average spend per transaction in year-ago period |
| PTD_SPEND_USD_YOY | Year-over-year percentage growth in spend amount |
| PTD_TRANS_YOY | Year-over-year percentage growth in transaction counts |
| PTD_AVG_TKT_USD_YOY | Year-over-year percentage growth in average ticket |
Key Concepts
This is a multi-table dataset
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🔑 These tables can be joined using SYMBOL_ID
Data Collection
The data contained in these tables reflects aggregations of individual transactions made with credit, debit, and prepaid cards carried over payment networks. All of the data comes directly from either card issuers or processors based on the transactions going over their networks. This data is sourced by our partners, who provide a variety of services to card issuers and processors that require it. Some things to note about this data that may affect the interpretation and use of it:
- The data used to create the reports is aggregated from multiple providers on the issuing side of the payment networks. Note that Issuer side data is comprised of ALL merchant activity for a sample of cards going over those networks, as opposed to "acquiring side" data, which would be all cardholder activity from a sampling of
merchants. - Data is provided at the brand level only, with no parent company association.
- Data is provided at the individual day level only.
- All data is reported in USD. For companies that do not consistently provide any reporting or due diligence in USD or on a constant-currency basis, correlations may need to be adjusted. It is recommended that you adjust from USD to the reported currency with exchange rates calculated on a daily basis.
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. |
NAICS Codes
NAICS Industries are delineated by US Government-defined 3-digit NAICS codes. NAICS codes are manually mapped to individual brands for transactions tagged to a main brand. For other transactions, MCC codes have been mapped to NAICS codes and then the transactions are mapped to NAICS codes via their MCC code where available.
Panel Information
The number of cards in the database is growing over time, both due to macro trends in credit card usage and due to bank-specific customer acquisition trends. In order to normalize for this card growth.
This panel goes back to 1/1/2015 for USA1 data; it goes back to 1/1/2018 for USA2 and Combined USA1-USA2 data.
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 9 days ago