Payment data is extremely useful for verifying sales and understanding customer purchasing behaviours, payment issues, and signs of fraudulent activity.

According to the DX Trends 2025 report by Japan’s Innovation Platform Agency (IPA), the percentage of companies in Japan engaged in some form of digital transformation has reached 77.8%, up from 69.3% in 2022. This indicates that most companies recognise the importance of data-driven business decisions.

As digital transformation efforts progress, many companies are still trying to figure out exactly which data to examine. The focus needs to be on reliable purchase data generated through daily transactions. Analysing these records each day provides a reliable step towards improving revenue, simplifying operations, and driving business growth.

In this article, we will explain the basics of payment data, the findings it can reveal, and some real-world examples.

Key takeaways

• Payment data refers to information tied to a purchase, such as the timestamp, amount, payment method, refunds, and payment failures.
  
• Analysing payment data allows you to visualise sales trends, customer purchasing patterns, the payment methods being used, and signs that indicate possible fraud.
  
• These records are useful for sales promotions, new product development, demand forecasting, improving operational efficiency, and preventing fraudulent use.
  
• By combining payment data with purchase and customer data, rather than relying on it alone, it is possible to conduct more detailed analyses.
  
• Payment processing services and their specialised features enable centralised management of these records.
  

What is payment data?

Payment data refers to the details generated when purchasing goods or services.

These details typically contain the following items:

• Payment date and time
  
• Payment amount
  
• Currency code
  
• Payment method
  
• Purchase location
  
• Refunds and cancellations
  
• Failed payments
  
• Fraud
  

As cashless options become more widespread, payment data becomes increasingly valuable for showing purchase timing, location, and spending amounts. Combined with point of sale (POS), purchase, customer data, and other information, they support marketing initiatives, demand forecasting, and operational improvements.

What does payment data reveal?

Day-to-day transactions, including cashless payments, offer the following insights:

Information useful for marketing initiatives

By combining payment data with customer and purchase details, customer spending patterns and preferences, such as which segments are purchasing which products, when, and through which methods, can be analysed.

Information about failed payments

Payment data lets you view information about failed payments. If there are frequent failures, there could be issues involving complex input fields, identity verification procedures, error messages, or a lack of ways to pay.

Identifying recurring causes of failed transactions also provides a way to address cart abandonment. If a shopper continues to experience problems, the issue can be resolved by suggesting steps suited to the situation. These might involve updating the selected option or proposing an alternative.

Signs of fraud

Payment data can help identify activity patterns that require attention and remediation. Warning signs include repeated charges within a short period, high-value transactions, orders from unusual locations or devices, and an increase in refunds or chargebacks. This shows that payment data is a valuable tool for fraud prevention.

How to use payment data in business

By properly reviewing payment data, you can develop strategies that benefit your business, such as increasing sales and developing new products.

Use to increase sales

By conducting a detailed analysis of customer attributes, past purchases, and payment data, you can detect trends such as popular products, peak purchase periods, high-demand regions, and preferred ways to pay.

These findings can support personalised campaigns and ads, email marketing, and coupon promotions.

For instance, if a specific customer segment consistently purchases products from the same brand or flavour, the trend can guide personalised recommendations, including consistently displaying related products or sending emails that encourage repeat purchases.

Additionally, if matcha purchases by overseas customers are on the rise, improving the English content on product pages and labels, as well as reviewing shipping and payment methods for high-traffic regions, are measures that could increase sales from international customers.

Use to develop new products and forecast demand

By analysing this information, you can pinpoint developments such as which product categories are on the rise and when purchases are increasing. For example, if demand for protein supplements is growing, understanding the most popular flavours and which age groups are buying them can help develop new products and improve existing ones.

You can also consider developing private-brand products based on best-selling items and those with high repeat-purchase rates. By tracking changes in purchasing timing and frequency, you can forecast future demand and product trends, and apply these findings to review and enhance inventory management, procurement, and sales plans.

Use to increase work efficiency

By organising payment data such as sales, refunds, failed transactions, and chargebacks by payment option or time period, you can reduce the effort required for daily data aggregation and report generation. Immediate access to the necessary figures allows accounting and operations staff to devote less time to verification and more to improvement initiatives.

Key points when analysing purchase data

To effectively apply payment data for your business, it is important to keep the following points in mind during the review:

Clarify the purpose of the improvement

Payment data is used for a wide range of purposes, including sales studies, marketing campaigns, resolving checkout failures, and verifying refunds and chargebacks. The first step in the analysis process is to decide what needs improvement.

Combined use of purchase data and customer data

This information alone identifies trends in sales, ways to pay, refunds, and failed purchases. Still, it has its limitations when examining the preferences and purchasing behaviours of individual customers in detail. Combining purchase and customer records with payment data enables greater detailed analyses.

Use payment processing services and specialised tools

Checking multiple ways to pay, including credit card, convenience store, and QR code payments, one by one, is time-consuming. By using a payment agent or service, you can centrally manage data from multiple payment methods and view information on sales, refunds, and failed transactions all in one place.

In addition, many payment processing services offer specialised tools to analyse accumulated records. These features provide more efficient access to the necessary data.

Examples of the usage of payment data

The artificial intelligence (AI) marketing platform Blaze illustrates how payment data can be used.

As Blaze’s business grew, they needed to gain a deeper understanding of revenue, churn, retention, and other metrics. However, exporting Stripe payment and subscription data, integrating figures from multiple systems, and manually importing them into a modern data warehouse such as Snowflake were time-consuming tasks, resulting in a greater burden of preparation than actual analysis.

To address this, Blaze adopted Stripe Sigma and Stripe Data Pipeline to build a system that analyses Stripe’s payment and subscription records alongside customer personal data, product usage metrics, marketing figures, and other sources.

By recognising customer segments with high long-term value and applying those insights to refine its marketing, product, and service messaging, Blaze reduced customer acquisition costs by 25%.

How Stripe Sigma can help

Stripe Sigma provides a powerful SQL explorer that helps businesses build custom reports and analyse their Stripe data. Teams can gain faster access to deep financial insights directly within the Stripe Dashboard.

Stripe Sigma can help you:

• Build business intelligence dashboards with SQL: Query your Stripe data directly to generate more precise, custom reports on everything from revenue by product line to regional tax liability and customer lifetime value.

• Eliminate the need for complex data engineering: Gain immediate access to your Stripe data without building or maintaining costly ETL pipelines, saving your engineering team weeks of development and maintenance work.

• Unlock granular insights into revenue and retention: Analyse complex metrics like MRR, customer churn and cohort performance to identify growth opportunities and pinpoint where to address churn risks across your customer base.

• Streamline reporting and team-wide collaboration: Save and share your most important queries with your team, or schedule automated reports to ensure every stakeholder is aligned on the business's key performance indicators.

• Scale on a secure, enterprise-grade infrastructure: Rely on Stripe's highly available and PCI-compliant environment to query your most sensitive financial information without compromising performance or security.

Learn more about how Stripe Sigma can help you unlock your business data, or get started today.

FAQs about payment data in Japan