Card Transactions Stability

Description:
How many days, weeks, or months a business had any credit card transactions in a particular period.

See the Merchant Transaction Signals overview page for details about data sources, high-level methodology, and timeliness of this attribute.

Child attributes (and data file structure):

Column Name	Data Type	Description	Example
`end_date`	string	The time index of the features. All features in the row assume this is the final date, inclusive, of the calculation.	2020-08-31
`card_transactions_stability__1m__start_date`	string	The date that the 1-month period begins, inclusive.	2020-08-01
`card_transactions_stability__1m__days_present`	int	The number of days with card transactions in the month leading up to and including `end_date`.	25
`card_transactions_stability__1m__weeks_present`	int	The number of weeks with card transactions in the month leading up to and including `end_date`.	4
`card_transactions_stability__1m__months_present`	int	1 if the month had any transactions, 0 otherwise.	1
`card_transactions_stability__1m__daily_coverage_ratio`	float	The ratio of days with card transactions to the total number of days in the month leading up to and including `end_date`.	0.8064
`card_transactions_stability__1m__weekly_coverage_ratio`	float	The ratio of weeks with card transactions to the total number of weeks in the month leading up to and including `end_date`.	1
`card_transactions_stability__1m__monthly_coverage_ratio`	float	1 if the month had any transactions, 0 otherwise.	1
`card_transactions_stability__3m__start_date`	string	The date that the 3-month period begins, inclusive.	2020-06-01
`card_transactions_stability__3m__days_present`	int	The number of days with card transactions in the 3 months leading up to and including `end_date`.	61
`card_transactions_stability__3m__weeks_present`	int	The number of weeks with card transactions in the 3 months leading up to and including `end_date`.	10
`card_transactions_stability__3m__months_present`	int	The number of months with card transactions in the 3 months leading up to and including `end_date`.	3
`card_transactions_stability__3m__daily_coverage_ratio`	float	The ratio of days with card transactions to the total number of days in the 3 months leading up to and including `end_date`.	0.6777
`card_transactions_stability__3m__weekly_coverage_ratio`	float	The ratio of weeks with card transactions to the total number of weeks in the 3 months leading up to and including `end_date`.	0.7692
`card_transactions_stability__3m__monthly_coverage_ratio`	float	The ratio of months with card transactions to the total number of months in the 3 months leading up to and including `end_date`.	1
`card_transactions_stability__12m__start_date`	string	The date that the 12-month period begins, inclusive.	2019-08-31
`card_transactions_stability__12m__days_present`	int	The number of days with card transactions in the 12 months leading up to and including `end_date`.	110
`card_transactions_stability__12m__weeks_present`	int	The number of weeks with card transactions in the 12 months leading up to and including `end_date`.	27
`card_transactions_stability__12m__months_present`	int	The number of months with card transactions in the 12 months leading up to and including `end_date`.	11
`card_transactions_stability__12m__daily_coverage_ratio`	float	The ratio of days with card transactions to the total number of days in the 12 months leading up to and including `end_date`.	0.3013
`card_transactions_stability__12m__weekly_coverage_ratio`	float	The ratio of weeks with card transactions to the total number of weeks in the 12 months leading up to and including `end_date`.	0.5192
`card_transactions_stability__12m__monthly_coverage_ratio`	float	The ratio of months with card transactions to the total number of months in the 12 months leading up to and including `end_date`.	0.9167

JSON Sample:

{
        "card_transactions_stability": [
    {
        "end_date": "2020-08-31",
        "date_accessible": "2020-11-15",
        "1m": {
                "start_date": "2020-08-01",
            "days_present": 25,
            "weeks_present": 4,
            "months_present": 1,
            "daily_coverage_ratio": 0.8064,
            "weekly_coverage_ratio": 0.8,
            "monthly_coverage_ratio": 1
        },
        "3m": {
                "start_date": "2020-06-01",
            "days_present": 61,
            "weeks_present": 10,
            "months_present": 3,
            "daily_coverage_ratio": 0.6667,
            "weekly_coverage_ratio": 0.7692,
            "monthly_coverage_ratio": 1
        },
        "12m": {
                "start_date": "2019-09-01",
            "days_present": 110,
            "weeks_present": 27,
            "months_present": 11,
            "daily_coverage_ratio": 0.3013,
            "weekly_coverage_ratio": 0.5192,
            "monthly_coverage_ratio": 0.9167
        }
    }
  ]
}

Other notes and tips:
Enigma uses unique card counts per day, i.e., “daily unique cards,” to estimate customer visits.

There are a few limitations with this approach: If a customer splits a purchase across two different cards, this would show up as two distinct customers. This is because Enigma is estimating customers based on unique cards not unique cardholders.

The count may not be an integer because Enigma applies a projection factor to the aggregated panel counts to estimate the total counts of each merchant

Multiplying the average daily customer by 30 can give you a proxy for the number of customers a business has. Note: this method will overestimate the number of customers because it will not take into account repeat customers in that month