Transaction Data: Best Transaction Datasets & Databases

What is Transaction Data?

Transaction data is information about all recorded transactions carried out between a customer and company. The customer could be a business or individual consumer. Key transaction information includes total spend, product information including SKU, number of orders, purchase history, invoices and receipts, payment method, and customer data. It’s used for marketing, financial compliance, spend analysis, pricing strategies, and many more applications.

What are the sources of Transaction Data?

There are several different sources of transaction data. Here are the top 10:

1. Point of Sale (POS) Systems: POS systems are used in retail and other businesses to process transactions. They capture transaction data such as the item purchased, the price, and the payment method. This data can be used for inventory management, sales tracking, and financial analysis.
2. Online Transactions: E-commerce transactions generate data about the customer, the product purchased, and the payment method used. This data can be used for marketing, customer profiling, and fraud detection.
3. Credit Card Payments: Credit card companies generate transaction data when customers use their credit cards. This data includes the date and time of the transaction, the merchant’s name, the amount of the purchase, and the payment method used.
4. Bank Transactions: Banks generate transaction data when customers use their debit or credit cards, make wire transfers, or withdraw money from ATMs. This data can be used for fraud detection, risk management, and compliance.
5. Bank Statements: Banks generate transaction data when customers use their debit cards, make wire transfers, or withdraw money from ATMs. This data includes details such as the date and time of the transaction, the amount of money involved, and the parties involved in the transaction.
6. Mobile Payments: Mobile payments are becoming increasingly popular, and they generate transaction data that includes details such as the user’s location, the type of transaction, and the amount of money involved. This data can be used for marketing, fraud detection, and risk management.
7. Loyalty Programs: Loyalty programs generate transaction data when customers make purchases using their loyalty cards. This data can be used for customer profiling, marketing, and sales tracking.
8. Third-Party Providers: There are many third-party providers that offer payment services, such as PayPal and Stripe. These providers generate transaction data that includes details such as the payment method, the amount of money involved, and the parties involved in the transaction. This data can be used for financial analysis, risk management, and compliance.
9. Online Surveys: Companies generate transaction data when they conduct online surveys about recent purchases. This data includes the respondent’s name and email address, the survey questions, and the responses.
10. Insurance Claims: Insurance companies generate transaction data when they process claims. This data includes the policyholder’s name and address, the type of claim, the amount of the claim, and the payment method used.

So as we’ve seen, transaction data can be generated from a variety of sources, including POS systems, online transactions, bank transactions, mobile payments, loyalty programs, and third-party providers.

What are the typical attributes of a Transaction Dataset?

It’s important that whichever transaction dataset you buy has the necessary information fields or data attributes for your project. Here are some of the core attributes of a transaction dataset:

1. Panel size: This refers to the number of customers or households included in the dataset. A larger panel size can provide more robust insights and a better representation of the population.
2. Time and date: This attribute includes the date and time of each transaction. It allows businesses to analyze sales trends and seasonality, and to identify patterns in consumer behavior over time.
3. Total spend: This attribute refers to the total amount spent by customers on each transaction. It can provide insights into customer behavior and purchasing patterns, such as the average transaction value or the most popular items purchased.
4. Product information including SKU: This attribute provides information about the products or services purchased, including product name, category, and SKU (stock keeping unit) number. This information allows businesses to understand which products are selling well and which are not, and to identify opportunities for product development and marketing.
5. Number of orders: This attribute refers to the number of orders placed by each customer. It can provide insights into customer loyalty and repeat purchase behavior.
6. Purchase history: This attribute includes information about a customer’s previous purchases, including the date, time, and value of each transaction. It allows businesses to identify patterns in customer behavior and tailor their marketing and sales strategies accordingly.
7. Invoices and receipts: This attribute includes information about the invoice or receipt for each transaction. It can provide insights into payment methods, taxes, and other transaction details.
8. Payment method: This attribute refers to the payment method used for each transaction, such as credit card, cash, or mobile payment. It allows businesses to identify trends in payment behavior and tailor their payment options accordingly.
9. Customer identity data: This attribute includes information about each customer, such as their name, age, gender, and location. It allows businesses to identify patterns in customer behavior based on demographic information and to tailor their marketing and sales strategies accordingly.

A transaction dataset including these attributes will be able to give you a comprehensive insight into exactly who is buying what, how they’re paying, and how regularly they’re purchasing.

What is Transaction Data used for?

Transaction data is a valuable source of information that can be used by different stakeholders for various purposes. Here are some of the main use cases for transaction data across a range of industries:

1. Marketing: Marketing teams use transaction data to understand customer behavior, preferences, and buying patterns. This helps them create targeted marketing campaigns and promotions that resonate with customers and increase sales.
2. Targeted advertising: Advertisers use transaction data to target specific customer segments with relevant ads. For example, if a customer has recently purchased a phone, advertisers can target them with ads for phone cases or accessories.
3. Purchase intent estimation: Companies can use transaction data to estimate customers’ purchase intent by analyzing their buying patterns. This information can help companies plan their inventory and marketing strategies accordingly.
4. Pricing strategy: Business owners can use transaction data to determine their pricing strategy by analyzing the prices of similar products or services offered by competitors.
5. Audience segmentation: Companies can use transaction data to segment their customers based on their buying patterns, preferences, and behavior. This helps them tailor their marketing and sales strategies to each customer segment.
6. Supply chain management: Inventory and logistics managers use transaction data to manage their supply chains effectively. This includes tracking inventory levels, identifying bottlenecks, and optimizing distribution channels.
7. Consumer spend trend mapping: Market analysts can use transaction data to map consumer spend trends and identify opportunities for growth or expansion. This information can also be used to forecast future sales and revenue.
8. Fraud detection: Loan companies and financial institutions use transaction data to detect and prevent fraudulent activities. By analyzing patterns and anomalies in transaction data, they can identify suspicious transactions and take appropriate action.
9. Risk management: Banks and lending institutions use transaction data to assess and manage risks related to financial transactions. This includes identifying potential losses, managing credit risk, and monitoring compliance with regulations.
10. Compliance: All companies can use transaction data to comply with regulatory requirements related to financial transactions. This includes tracking and reporting suspicious activities, complying with anti-money laundering laws, and adhering to data protection regulations.
11. Financial analysis: Economic researchers can understand consumer finance trends by analyzing transaction data. On a micro-level, companies can also use transaction data to analyze their financial performance, including revenue, profit margins, and cash flow. This information helps them make informed decisions about their business strategy and operations.

Stakeholders from various industries and vertical use transactions datasets to gain valuable insights into customer behavior, improve their operations, and manage risk and compliance.

How can a user assess the quality of Transaction Data?

Assessing the quality of transaction data is important to ensure accurate and reliable insights. Here’s Datarade Marketplace’s three-step guide on how to assess the quality of transaction data:

Step 1: Check the completeness of the data

The first step in assessing the quality of transaction data is to check its completeness. This involves ensuring that all necessary data attributes (as we listed above!) are present. Always run an initial check that there are no missing values or incomplete records. Additionally, check that the data includes all relevant variables such as transaction date, product information, and customer identity data.

Step 2: Evaluate a sample

The second step is to evaluate a sample of the transaction data. This can help identify any errors or inconsistencies in the data that may affect its quality. It is important to select a representative sample that includes a range of different transaction types, dates, and customers.

Step 3: Verify data accuracy

The final step is to verify the accuracy of the data. This involves checking that the data matches external sources or benchmarks. For example, compare transaction data against bank statements or credit card statements to ensure that the data is accurate. Additionally, verify the data against industry standards or benchmarks to ensure that it is reliable and consistent. Lastly, read data provider reviews on Datarade Marketplace.

By following these three steps, users can assess the quality of transaction data and ensure that it is reliable and accurate.

Where can I buy Transaction Data?

Data providers and vendors listed on Datarade sell Transaction Data products and samples. Popular Transaction Data products and datasets available on our platform are Credit card/ Debit card spend data, number of transactions, transaction by shop type by TagX, Transaction Data at Individual POI | Spend Patterns | US Credit Card/Debit Card Transaction Data by SafeGraph, and QueXopa Debit & Credit Card Transaction Data (Mexico) - Uniquely Refined Transactions Datasets by QueXopa.

How can I get Transaction Data?

You can get Transaction Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Transaction Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Transaction Data APIs, feeds and streams to download the most up-to-date intelligence.

What are similar data types to Transaction Data?

Transaction Data is similar to Consumer Review Data, Product Data, Ecommerce Data, Shopper Data, and Brand Data. These data categories are commonly used for Purchase Intelligence and spending analytics.

What are the most common use cases for Transaction Data?

The top use cases for Transaction Data are Purchase Intelligence, spending analytics, and Consumer Trend Analysis.