UK Row Level Transaction Data

Row level UK transaction data tracking both credit and debit accounts along with demographic details for a growing panel of approximately 200,000 consumers. Our data has all PII removed and our merchant tagging/cleansing is the best available for over 4,000 merchants. Why Snoop? We have a large active panel of approximately 200,000 users contributing to over 20 billion in spend. Our historic data goes back to 2020 for a pre covid view or we can share data as a 2 year cohort with a consistent batch of consumers. We have over 4000 merchants cleansed with millions more being tracked and available for custom projects. What is the panel size? Approximately 200,000 users which is growing daily by anywhere from 500 - 1500 new active users. How do I receive the data? Snowflake DataShare What’s the structure of the data? Raw transactions with the option of access to our SpendMapper platform for high level analysis or bespoke analysis How representative is your data? Our data looks almost the exact same as the whole of the UK. When we do make the data nationally representative, the trends barely change, so we are very confident that our data represents what the UK looks like and what spending looks like by region, affluence and age. We do skew slightly younger given typical users of banking apps. Cleansing and tagging Since the Snoop App was created, we have been hugely focused on cleaning the data thoroughly and matching merchants to make the data as easy to use and accurate as possible. We have been asked to help other firms improve their process since data quality has always been a top priority. We also went through a very intense RFP process with a client to review data quality and how representative the data is which we passed successfully and went on to win that RFP. Categorisation We have created our own custom categories and subcategories with support of our clients. We can adjust these to match NAICS or SIC codes or any custom categorisation necessary for the client. Banking Coverage 60+ financial institutions 1 customer_id 2 customer_location 3 gross_annual_salary 4 account_id 5 transaction_id 6 transaction_date 7 created_date 8 merchant_name 9 transaction_type The type of transaction included: Apple Pay Card Payment Contactless Payment Direct Debit Google Pay International Payment Paypal Refund Samsung Pay Transaction Types excluded (to eradicate PII leakage risk): Account Fees ATM Withdrawal Balance Adjustment Bank Giro Credit Bank Transfer Cashback Cash Deposit Cash Withdrawal CHAPS Transfer Cheque Interest Monzo Pot Mortgage Payment Non-Sterling Transaction Fee Overdraft Fees Returned Transaction 10 amount 11 category_name The transaction category: Charity Eating Out Entertainment Finances General Groceries Health & Beauty Home & Family Income Shopping Transport Travel 12 account_type States whether the originating account is a Current Account, Credit Card or Savings 13 provider_name 14 postcode_sector For information regarding the complete dataset please contact [e-mail]

搜集汇总

背景与挑战

背景概述

该数据集包含约20万英国消费者的匿名化交易记录，涵盖信用卡和借记卡交易，涉及4000余家商户的清洗标签数据。数据通过Snowflake平台提供，包含详细的交易类型、金额分类和人口统计信息，能较准确反映英国全国消费趋势。