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.

<p><br/></p>

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.

<p><br/></p>

What is the panel size?

Approximately 200,000 users which is growing daily by anywhere from 500 - 1500 new active users.

<p><br/></p>

How do I receive the data?

Snowflake DataShare

<p><br/></p>

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.

<p><br/></p>

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.

<p><br/></p>

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.

<p><br/></p>

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.

<p><br/></p>

Banking Coverage

60+ financial institutions

<p><br/></p>

Data Metrics

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

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]

原始信息汇总

UK Row Level Transaction Data

数据集概述

• 提供商: Snoop
• 定价模式: 免费试用
• 试用期限: 7天试用期

数据内容

• 英国交易数据行级记录，跟踪信用卡和借记卡账户以及消费者人口统计详细信息
• 数据已移除所有个人身份信息(PII)
• 商户标记/清理覆盖4000多家商户
• 历史数据可追溯至2020年
• 可选择提供2年队列数据

面板规模

• 约200,000名用户
• 每日新增500-1500名活跃用户
• 总消费额超过200亿

数据代表性

• 数据与英国整体人口特征高度相似
• 在地区、富裕程度和年龄方面具有代表性
• 因银行应用典型用户群体，数据略微偏向年轻人群

数据质量

• 专注于数据彻底清理和商户匹配
• 通过客户严格的RFP流程审核
• 数据质量始终是首要任务

分类系统

• 创建自定义类别和子类别
• 可调整为匹配NAICS或SIC代码
• 支持客户定制分类需求

银行覆盖

• 覆盖60多家金融机构

数据指标

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
10. amount
11. category_name
12. account_type
13. provider_name
14. postcode_sector

交易类型

包含的交易类型:

• Apple Pay、Card Payment、Contactless Payment、Direct Debit、Google Pay
• International Payment、Paypal、Refund、Samsung Pay

排除的交易类型:

• 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

数据交付

• 交付方式: Snowflake DataShare
• 数据结构: 原始交易数据
• 可选访问: SpendMapper平台用于高级分析

时间覆盖范围

• 2024年1月1日 - 2024年3月22日

地理覆盖范围

• 国家: 英国
• 粒度: 按城市

数据更新

• 静态数据

业务需求应用

市场规模分析

了解消费者支出行为在市场或品牌层面的增长趋势和随时间变化

竞争跟踪

了解品牌客户同时使用主要竞争对手的情况及频率

购物篮规模和频率

分析购物篮规模和使用频率随时间的变化

每周支出趋势

使用每周支出数据实时跟踪关键零售期的表现

市场份额趋势

快速了解个别品牌/行业市场份额趋势和钱包份额趋势

客户人口统计和行为

分析品牌与竞争对手的客户人口统计和消费行为

使用示例

Gen-Z购物最受欢迎的10大商户

基于总支出计算前10大商户，筛选18-24岁年龄段且交易类别为购物的交易

杂货店平均支出

计算指定杂货店商户的平均支出金额

各银行提供商总支出

按银行提供商汇总所有交易产生总支出

宠物主人识别

通过商户消费模式识别养宠物的客户

联系方式

• 销售: [e-mail]
• 支持: [e-mail]

法律条款

• 标准条款

分类

• 金融