OperatingLocationCardTransaction

Description

Contains quantitative information about the card transactions processed by the operating location.

Data Sources

The card transaction data is derived from a panel of around a third of all U.S. credit card transactions.

type OperatingLocationCardTransaction implements MathFunctions {
  quantityType: String
  period: String
  id: UUID!
  firstObservedDate: String
  lastObservedDate: String
  rawQuantity: Float
  projectedQuantity: Float
  periodStartDate: Date
  periodEndDate: Date
  internalId: String
  internalOperatingLocationId: String
  count(
    field: String!
    conditions: Conditions
  ): Int
  sum(
    field: String!
    conditions: Conditions
  ): Int
  min(
    field: String!
    conditions: Conditions
  ): Int
  max(
    field: String!
    conditions: Conditions
  ): Int
  avg(
    field: String!
    conditions: Conditions
  ): Float
  collect(
    field: String!
    separator: String
    conditions: Conditions
  ): String
  minDateTime(
    field: String!
    conditions: Conditions
  ): DateTime
  maxDateTime(
    field: String!
    conditions: Conditions
  ): DateTime
}

Fields

`OperatingLocationCardTransaction.quantityType` ● `String` scalar

This field indicates the type of quantity represented in the record.

It may contain the following values:

**avg_transaction_size**: The record is the average transaction size in dollars for the location for that time period.
**has_transactions**: The record is a 1 if the location had any transactions in the time period, and 0 otherwise.
**refunds_amount**: The record is the total amount of refunds in dollars for the location for that time period.
**card_transactions_count**: The record is the total number of card transactions for the location for that time period.
**card_revenue_amount**: The record is the total sales amount in dollars for the location for that time period.
**card_customers_average_daily_count**: The record is the average number of unique daily customers for the location for that time period.
**card_revenue_yoy_growth**: The record is the ratio of the location's current period's revenue to the period one year prior's revenue.
**card_revenue_prior_period_growth**: The record is the ratio of the location's current period's revenue to the previous period's revenue.

`OperatingLocationCardTransaction.period` ● `String` scalar

This field indicates the length of the time period represented in the record.

It may contain the following values:

**1m**: One month.
**3m**: Three months.
**12m**: Twelve months.

`OperatingLocationCardTransaction.id` ● `UUID!` non-null scalar

`OperatingLocationCardTransaction.firstObservedDate` ● `String` scalar

`OperatingLocationCardTransaction.lastObservedDate` ● `String` scalar

`OperatingLocationCardTransaction.rawQuantity` ● `Float` scalar

This field contains the raw value of the quantity specified in quantity_type. In cases where the underlying raw data is based on fewer than a threshold number of transactions required by the data provider for compliance reasons, this number may be null for all quantity_types except for "has_transactions".

`OperatingLocationCardTransaction.projectedQuantity` ● `Float` scalar

This field contains the projected value for the quantity specified in quantity type. In cases where the underlying raw data is based on fewer than a threshold number of transactions required by the data provider for compliance reasons, this number may be null for all quantity_types except for "has_transactions".

For quantity_types of ["avg_transaction_size", "card_revenue_prior_period_growth", "has_transactions", "card_revenue_yoy_growth"], this is usually the same as raw_quantity. In cases where the underlying raw data is based on fewer than a threshold number of transactions required by the merchant for compliance reasons, these numbers may not align.

For quantity_types of ["refunds_amount", "card_transactions_count", "card_revenue_amount", "card_customers_average_daily_count"], projected_quantity is raw_quantity scaled up by a multiple that approximates the proportion of total card transactions Enigma's panel captures for the given location and time period. That multiple is based on features of the location, including geography, industry, and size, that are predictive of the proportion of transactions the panel includes.