Daily Card Payments by Irish Households. A subset of the monthly Card Payment Statistics. The onset of the Covid-19 pandemic created the need for timely, high-frequency data, such as the Daily Credit and Debit Card Statistics, to better understand the impact of the pandemic on personal expenditure and economic activity. his high-frequency daily Credit and Debit Data captures expenditure of euro-denominated credit and debit cards issued to Irish residents. The dataset consists of total daily debit and credit card spending and ATM withdrawals, while from 1 October 2020, expenditure in a number of key sectors of the economy, and a split of online and in-store spending is also available.

Daily, weekly and monthly data showing seasonally adjusted and non-seasonally adjusted UK spending using debit and credit cards. These are official statistics in development. Source: CHAPS, Bank of England.

The credit card penetration in Thailand was forecast to continuously increase between 2024 and 2029 by in total 36.8 percentage points. After the fifteenth consecutive increasing year, the credit card penetration is estimated to reach 67.53 percent and therefore a new peak in 2029. Notably, the credit card penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population who use credit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the credit card penetration in countries like Malaysia and Philippines.

The credit card penetration in Canada was forecast to continuously increase between 2024 and 2029 by in total 1.4 percentage points. After the seventh consecutive increasing year, the credit card penetration is estimated to reach 84.55 percent and therefore a new peak in 2029. The penetration rate refers to the share of the total population who use credit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the credit card penetration in countries like United States and Mexico.

ExactOne delivers unparalleled consumer transaction insights to help investors and corporate clients uncover market opportunities, analyze trends, and drive better decisions.

Dataset Highlights - Source: Debit and credit card transactions from 600K+ active users and 2M accounts connected via Open Banking. Scale: Covers 250M+ annual transactions, mapped to 1,800+ merchants and 330+ tickers. Historical Depth: Over 6 years of transaction data. Flexibility: Analyse transactions by merchant/ticker, category/industry, or timeframe (daily, weekly, monthly, or quarterly).

ExactOne data offers visibility into key consumer industries, including: Airlines - Regional / Budget Airlines - Cargo Airlines - Full Service Autos - OEMs Communication Services - Cable & Satellite Communication Services - Integrated Telecommunications Communication Services - Wireless Telecom Consumer - Services Consumer - Health & Fitness Consumer Staples - Household Supplies Energy - Utilities Energy - Integrated Oil & Gas Financial Services - Insurance Grocers - Traditional Hotels - C-corp Industrial - Tools And Hardware Internet - E-commerce Internet - B2B Services Internet - Ride Hailing & Delivery Leisure - Online Gambling Media - Digital Subscription Real Estate - Brokerage Restaurants - Quick Service Restaurants - Fast Casual Restaurants - Pubs Restaurants - Specialty Retail - Softlines Retail - Mass Merchants Retail - European Luxury Retail - Specialty Retail - Sports & Athletics Retail - Footwear Retail - Dept Stores Retail - Luxury Retail - Convenience Stores Retail - Hardlines Technology - Enterprise Software Technology - Electronics & Appliances Technology - Computer Hardware Utilities - Water Utilities

Use Cases

For Private Equity & Venture Capital Firms: - Deal Sourcing: Identify high-growth opportunities. - Due Diligence: Leverage transaction data to evaluate investment potential. - Portfolio Monitoring: Track performance post-investment with real-time data.

For Consumer Insights & Strategy Teams: - Market Dynamics: Compare sales trends, average transaction size, and customer loyalty. - Competitive Analysis: Benchmark market share and identify emerging competitors. - E-commerce vs. Brick & Mortar Trends: Assess channel performance and strategic opportunities. - Demographic & Geographic Insights: Uncover growth drivers by demo and geo segments.

For Investor Relations Teams: - Shareholder Insights: Monitor brand performance relative to competitors. - Real-Time Intelligence: Analyse sales and market dynamics for public and private companies. - M&A Opportunities: Evaluate market share and growth potential for strategic investments.

Key Benefits of ExactOne - Understand Market Share: Benchmark against competitors and uncover emerging players. - Analyse Customer Loyalty: Evaluate repeat purchase behavior and retention rates. - Track Growth Trends: Identify key drivers of sales by geography, demographic, and channel. - Granular Insights: Drill into transaction-level data or aggregated summaries for in-depth analysis.

With ExactOne, investors and corporate leaders gain actionable, real-time insights into consumer behaviour and market dynamics, enabling smarter decisions and sustained growth.

The credit card penetration in Brazil was forecast to continuously increase between 2024 and 2029 by in total 16.6 percentage points. After the twelfth consecutive increasing year, the credit card penetration is estimated to reach 62.27 percent and therefore a new peak in 2029. The penetration rate refers to the share of the total population who use credit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Mexico Card Transactions: Credit Cards: Day Care Centres data was reported at 0.250 MXN mn in 15 Apr 2025. This records an increase from the previous number of 0.213 MXN mn for 14 Apr 2025. Mexico Card Transactions: Credit Cards: Day Care Centres data is updated daily, averaging 0.239 MXN mn from Jan 2009 (Median) to 15 Apr 2025, with 5949 observations. The data reached an all-time high of 2.354 MXN mn in 05 Feb 2025 and a record low of 0.000 MXN mn in 12 Jul 2020. Mexico Card Transactions: Credit Cards: Day Care Centres data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.KA008: Card Transactions. Starting December 01, 2022, the form through which the card clearing houses (EGlobal and Prosa) report the operations that were cleared was modified to improve the quality of information, as well as add relevant information considering the new technologies. Additional institutions that were not previously reported due to lack of updated catalogues are now included.

Mexico Card Transactions: Credit Cards: Fast Food data was reported at 108.715 MXN mn in 15 Apr 2025. This records a decrease from the previous number of 181.738 MXN mn for 14 Apr 2025. Mexico Card Transactions: Credit Cards: Fast Food data is updated daily, averaging 20.069 MXN mn from Jan 2009 (Median) to 15 Apr 2025, with 5949 observations. The data reached an all-time high of 181.738 MXN mn in 14 Apr 2025 and a record low of 5.678 MXN mn in 19 May 2015. Mexico Card Transactions: Credit Cards: Fast Food data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.KA008: Card Transactions. Starting December 01, 2022, the form through which the card clearing houses (EGlobal and Prosa) report the operations that were cleared was modified to improve the quality of information, as well as add relevant information considering the new technologies. Additional institutions that were not previously reported due to lack of updated catalogues are now included.

Mexico Card Transactions: Credit Cards: Restaurants data was reported at 216.089 MXN mn in 15 Apr 2025. This records a decrease from the previous number of 411.497 MXN mn for 14 Apr 2025. Mexico Card Transactions: Credit Cards: Restaurants data is updated daily, averaging 132.635 MXN mn from Jan 2009 (Median) to 15 Apr 2025, with 5949 observations. The data reached an all-time high of 596.558 MXN mn in 11 May 2024 and a record low of 8.556 MXN mn in 21 Apr 2020. Mexico Card Transactions: Credit Cards: Restaurants data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.KA008: Card Transactions. Starting December 01, 2022, the form through which the card clearing houses (EGlobal and Prosa) report the operations that were cleared was modified to improve the quality of information, as well as add relevant information considering the new technologies. Additional institutions that were not previously reported due to lack of updated catalogues are now included.

This dataset is fictional and is trying to simulate real life details. Any similarity to real life cases is purely coincidental. It has the following columns.

trans_date_trans_time: The date and time of the transaction.

cc_num: credit card number.

merchant: Merchant who was getting paid.

category: In what area does that merchant deal.

amt: Amount of money in American Dollars.

first: first name of the card holder.

last: last name of the card holder.

gender: Gender of the cardholder.Just male and female!

street:Street of card holder residence

city:city of card holder residence

state:state of card holder residence

zip:ZIP code of card holder residence

lat:latitude of card holder

long:longitude of card holder

city_pop:Population of the city

job:trade of the card holder

dob:Date of birth of the card holder

trans_num: Transaction ID

unix_time: Unix time which is the time calculated since 1970 to today.

merch_lat: latitude of the merchant

merch_long:longitude of the merchant

is_fraud: Whether the transaction is fraud(1) or not(0)

Mexico Card Transactions: Credit Cards: Colleges & Universities data was reported at 80.674 MXN mn in 15 Apr 2025. This records an increase from the previous number of 34.886 MXN mn for 14 Apr 2025. Mexico Card Transactions: Credit Cards: Colleges & Universities data is updated daily, averaging 50.074 MXN mn from Jan 2009 (Median) to 15 Apr 2025, with 5949 observations. The data reached an all-time high of 393.375 MXN mn in 11 Feb 2025 and a record low of 2.255 MXN mn in 27 Mar 2016. Mexico Card Transactions: Credit Cards: Colleges & Universities data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.KA008: Card Transactions. Starting December 01, 2022, the form through which the card clearing houses (EGlobal and Prosa) report the operations that were cleared was modified to improve the quality of information, as well as add relevant information considering the new technologies. Additional institutions that were not previously reported due to lack of updated catalogues are now included.

Aggregated and anonymized purchase data from consumer credit and debit card spending. Spending is reported based on the ZIP code where the cardholder lives, not the ZIP code where transactions occurred. Data from Affinity Solutions, compiled by Opportunity Insights.

Update Frequency: Weekly Date Range: January 13th until the most recent date available.

Data Frequency: Data is daily until the final two weeks of the series, and the daily data is presented as a 7 day lookback moving average. For the final two weeks of the series, the data is weekly and presented as weekly data points.

Index Period: January 4th - January 31st

Indexing Type: Seasonally adjusted change since January 2020. Data is indexed in 2019 and 2020 as the change relative to the January index period. We then seasonally adjust by dividing year-over-year, which represents the difference between the change since January observed in 2020 compared to the change since January observed since 2019. We account for differences in the dates of federal holidays between 2019 and 2020 by shifting the 2019 reference data to align the holidays before performing the year-over-year division.

Mexico Card Transactions: Credit Cards: Supermarkets data was reported at 0.615 MXN mn in 15 Apr 2025. This records an increase from the previous number of 0.592 MXN mn for 14 Apr 2025. Mexico Card Transactions: Credit Cards: Supermarkets data is updated daily, averaging 0.366 MXN mn from Jan 2009 (Median) to 15 Apr 2025, with 5949 observations. The data reached an all-time high of 1.201 MXN mn in 20 Oct 2019 and a record low of 0.019 MXN mn in 02 Jan 2017. Mexico Card Transactions: Credit Cards: Supermarkets data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.KA008: Card Transactions. Starting December 01, 2022, the form through which the card clearing houses (EGlobal and Prosa) report the operations that were cleared was modified to improve the quality of information, as well as add relevant information considering the new technologies. Additional institutions that were not previously reported due to lack of updated catalogues are now included.

In the end, you should only measure and look at the numbers that drive action, meaning that the data tells you what you should do next.🥰

Please do upvote if you love the work.♥️🥰 For more related datasets: https://www.kaggle.com/datasets/rajatsurana979/fifafcmobile24 https://www.kaggle.com/datasets/rajatsurana979/most-streamed-spotify-songs-2023 https://www.kaggle.com/datasets/rajatsurana979/comprehensive-credit-card-transactions-dataset https://www.kaggle.com/datasets/rajatsurana979/hotel-reservation-data-repository https://www.kaggle.com/datasets/rajatsurana979/percent-change-in-consumer-spending https://www.kaggle.com/datasets/rajatsurana979/fast-food-sales-report

Description: This dataset captures sales transactions from a local restaurant near my home. It includes details such as the order ID, date of the transaction, item names (representing various food and beverage items), item types (categorized as Fast-food or Beverages), item prices, quantities ordered, transaction amounts, transaction types (cash, online, or others), the gender of the staff member who received the order, and the time of the sale (Morning, Evening, Afternoon, Night, Midnight). The dataset offers a valuable snapshot of the restaurant's daily operations and customer behavior.

Columns: 1. order_id: a unique identifier for each order. 2. date: date of the transaction. 3. item_name: name of the food. 4. item_type: category of item (Fastfood or Beverages). 5. item_price: price of the item for 1 quantity. 6. Quantity: how much quantity the customer orders. 7. transaction_amount: the total amount paid by customers. 8. transaction_type: payment method (cash, online, others). 9. received_by: gender of the person handling the transaction. 10. time_of_sale: different times of the day (Morning, Evening, Afternoon, Night, Midnight).

Potential Uses: - Analyzing sales trends over time. - Understanding customer preferences for different items. - Evaluating the impact of payment methods on revenue. - Investigating the performance of staff members based on gender. - Exploring the popularity of items at different times of the day.

Makeyourhandsdirtyonit

Belgium - Number of Cashless payments, Card and e-money payments, by card with a credit function

Graph and download economic data for Delinquency Rate on Credit Card Loans, All Commercial Banks (DRCCLACBS) from Q1 1991 to Q2 2025 about credit cards, delinquencies, commercial, loans, banks, depository institutions, rate, and USA.

From the selected regions, the ranking by number of credit cards in use is led by the United States with 1.1 billion cards and is followed by Japan (295.11 million cards). In contrast, the ranking is trailed by Saudi Arabia with 2.73 million cards, recording a difference of 1.1 billion cards to the United States. Shown is the estimated number of credit cards currently in use.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Mexico Card Transactions: Credit Cards data was reported at 8,019.887 MXN mn in 15 Apr 2025. This records an increase from the previous number of 6,700.107 MXN mn for 14 Apr 2025. Mexico Card Transactions: Credit Cards data is updated daily, averaging 2,892.865 MXN mn from Jan 2009 (Median) to 15 Apr 2025, with 5949 observations. The data reached an all-time high of 13,571.354 MXN mn in 17 Nov 2024 and a record low of 838.864 MXN mn in 31 Oct 2015. Mexico Card Transactions: Credit Cards data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.KA008: Card Transactions. Starting December 01, 2022, the form through which the card clearing houses (EGlobal and Prosa) report the operations that were cleared was modified to improve the quality of information, as well as add relevant information considering the new technologies. Additional institutions that were not previously reported due to lack of updated catalogues are now included.

Credit card payments in Singapore grew by nearly 70 million transactions in 2022, as the per capita use also grew. The payment method plays a significant role and is part of a much wider movement of payment digitalization. Credit cards ranked as Singapore's most used in-store payment method, for example, but they were slowly losing terrain to mobile wallets. In 2020 , Singapore had the highest penetration rate for cashless payments in Southeast Asia.

The Alternative Data Market size was valued at USD 7.20 billion in 2023 and is projected to reach USD 126.50 billion by 2032, exhibiting a CAGR of 50.6 % during the forecasts period. The use and processing of information that is not in financial databases is known as the alternative data market. Such data involves posts in social networks, satellite images, credit card transactions, web traffic and many others. It is mostly used in financial field to make the investment decisions, managing risks and analyzing competitors, giving a more general view on market trends as well as consumers’ attitude. It has been found that there is increasing requirement for the obtaining of data from unconventional sources as firms strive to nose ahead in highly competitive markets. Some current trend are the finding of AI and machine learning to drive large sets of data and the broadening utilization of the so called “Alternative Data” across industries that are not only the finance industry. Recent developments include: In April 2023, Thinknum Alternative Data launched new data fields to its employee sentiment datasets for people analytics teams and investors to use this as an 'employee NPS' proxy, and support highly-rated employers set up interviews through employee referrals. , In September 2022, Thinknum Alternative Data announced its plan to combine data Similarweb, SensorTower, Thinknum, Caplight, and Pathmatics with Lagoon, a sophisticated infrastructure platform to deliver an alternative data source for investment research, due diligence, deal sourcing and origination, and post-acquisition strategies in private markets. , In May 2022, M Science LLC launched a consumer spending trends platform, providing daily, weekly, monthly, and semi-annual visibility into consumer behaviors and competitive benchmarking. The consumer spending platform provided real-time insights into consumer spending patterns for Australian brands and an unparalleled business performance analysis. .