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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 NameData TypeDescriptionExample
end_datestringThe 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_datestringThe date that the 1-month period begins, inclusive.2020-08-01
card_transactions*stability**1m*_days_presentintThe number of days with card transactions in the month leading up to and including end_date.25
card_transactions*stability**1m*_weeks_presentintThe number of weeks with card transactions in the month leading up to and including end_date.4
card_transactions*stability**1m*_months_presentint1 if the month had any transactions, 0 otherwise.1
card_transactions*stability**1m*_daily_coverage_ratiofloatThe 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_ratiofloatThe 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_ratiofloat1 if the month had any transactions, 0 otherwise.1
card_transactions*stability**3m*_start_datestringThe date that the 3-month period begins, inclusive.2020-06-01
card_transactions*stability**3m*_days_presentintThe number of days with card transactions in the 3 months leading up to and including end_date.61
card_transactions*stability**3m*_weeks_presentintThe number of weeks with card transactions in the 3 months leading up to and including end_date.10
card_transactions*stability**3m*_months_presentintThe number of months with card transactions in the 3 months leading up to and including end_date.3
card_transactions*stability**3m*_daily_coverage_ratiofloatThe 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_ratiofloatThe 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_ratiofloatThe 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_datestringThe date that the 12-month period begins, inclusive.2019-08-31
card_transactions*stability**12m*_days_presentintThe number of days with card transactions in the 12 months leading up to and including end_date.110
card_transactions*stability**12m*_weeks_presentintThe number of weeks with card transactions in the 12 months leading up to and including end_date.27
card_transactions*stability**12m*_months_presentintThe number of months with card transactions in the 12 months leading up to and including end_date.11
card_transactions*stability**12m*_daily_coverage_ratiofloatThe 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_ratiofloatThe 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_ratiofloatThe 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

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