In Japan, the importance of purchase data is growing amid the expanding ecommerce market and the widespread adoption of cashless payments. Reviewing purchase records makes it possible to identify best-selling items and customer-specific purchasing trends. This information can then be used to improve marketing strategies, inventory management, and product planning.

Because there are several kinds of purchase data, including point-of-sale (POS), ID-POS, online retail orders, and payment records, determining what to examine and how to apply the findings often proves challenging.

In this article, we will explain the basic meaning of purchase data, its main types, common analysis methods, and how it can be used for both marketing and business improvement.

Key takeaways

• Purchase data is the information recorded when a customer buys a good or service, including the date and time of the order, product name, purchase price, quantity, sales channel, customer ID, and payment method.
  
• In Japan, the expansion of the ecommerce market and the widespread adoption of cashless payments have made it easier to collect purchase data across multiple touchpoints, including physical stores, ecommerce sites, apps, and payment platforms.
  
• Analyzing purchase data allows businesses to gain insights into customer behavior, best-selling products, shifts in demand, the effectiveness of marketing initiatives, and any issues with the shopping experience.
  
• Purchase data can be analyzed using approaches such as recency, frequency, and monetary (RFM) analysis, decile analysis, segmentation analysis, basket analysis, ABC analysis, and trend analysis. The resulting findings support product planning, assortment enhancement, recommendations, campaign refinement, demand forecasting, and inventory management.
  
• When using purchase data, it is important to clearly define your objectives and conduct ongoing analysis by combining it with payment and customer records.
  

What is purchase data?

Purchase data refers to the records generated when a customer buys a good or service. Specifically, this includes the order date and time, product name, purchase price, quantity, sales channel, customer ID, and payment method.

As reported by the Ministry of Economy, Trade and Industry’s 2024 eCommerce Market Survey, Japan’s business-to-consumer (B2C) ecommerce market reached ¥26.1 trillion in 2024, up 5.1% from ¥24.8 trillion the previous year. In addition, the B2C ecommerce penetration rate reached 9.8%, indicating that the shift toward online retail is gaining momentum. The 2025 utilization rate of cashless payments also reached 58%, with transactions totaling ¥162.7 trillion.

As Japan’s ecommerce expands and cashless payments become widespread, purchase behavior is increasingly straightforward to record as data. For this reason, purchase data can be considered a valuable resource for understanding shoppers, improving marketing strategies, forecasting demand, and managing stock.

Key information in purchase data

Purchase data is collected from multiple touchpoints, including in-store POS systems, ID-POS, ecommerce sites, member apps, and payment platforms. The information captured and the insights that can be derived from it will vary depending on where the data was collected, so it is necessary to understand the characteristics of each.

POS data

POS data refers to the sales records captured through a store’s cash registers or POS systems. It contains details including product name, date and time of sale, price, quantity sold, and store name.

Analyzing POS data shows which items are selling and when, at which stores, and in what quantities. The findings help pinpoint best-sellers, track sales patterns by time of day and day of the week, manage inventory, and refine procurement plans.

ID-POS data

ID-POS data refers to records that link customer or membership IDs to POS data. Standard POS data allows a business to track what was sold, when, and at what price. In contrast, ID-POS data enables analysis of who made the purchase.

ID-POS data is often collected via loyalty cards, membership cards, and apps, and is useful for reviewing individual shopping activity at specific locations. This data can be analyzed alongside customer information, such as membership tier and point usage.

Ecommerce site order data

Ecommerce site order data refers to records of online transactions. It contains details including the order date and time, product name, purchase amount, quantity, delivery region, coupons applied, purchase channel, and device used.

In other words, analyzing order data from an ecommerce site can reveal which items sell well online, which ads or campaigns are driving sales, and whether customers are more likely to buy via their smartphones or computers.

Additionally, examining items added to the cart but not bought, as well as the point where users abandoned the site, helps pinpoint the causes of cart abandonment.

Furthermore, by combining in-store POS data with ecommerce order records, it becomes easier to understand customers’ purchasing behavior across online and offline channels, such as webrooming and showrooming.

Payment data

Payment data refers to details about the checkout options selected when buying goods or services, as well as the status of the transaction. For example, this includes the payment method (credit cards, debit cards, e-money, code-based payments, buy now, pay later [BNPL], and bank transfers), the payment date and time, the amount paid, and whether the payment was successful or not.

Analyzing this data shows the options shoppers select and those most likely to result in a completed order. Because Japanese ecommerce sites offer payment options such as convenience store payments, bank transfers, and BNPL, companies need to assess which choices suit their intended audience and sales channels.

Companies also need to avoid storing or examining sensitive payment information—such as credit card numbers—in-house and instead retain the records required for analysis, including payment methods and transaction results. When handling credit card details, companies must follow Credit Card Security Guidelines, such as Payment Card Industry Data Security Standard (PCI DSS) compliance and nonretention of data.

Advantages of using purchase data

Customer data provides a clearer view of customer behavior and supports stronger marketing strategies. Reviewing purchasing trends makes developing effective approaches more straightforward.

Understanding customer insights

Analyzing purchase data allows you to identify which customer segments are buying which products, and when.

For instance, combining demographic details including age group and region with purchase frequency, average transaction value, and purchase channels simplifies the process of recognizing the characteristics of high-spending shoppers, repeat buyers, and inactive accounts.

The findings also help tailor item recommendations and improve the content of promotional campaigns.

Keep track of best-sellers and changes in demand

Reviewing purchase data reveals patterns in sales volume and revenue by product, as well as the times, days of the week, regions, and channels where orders are most likely to occur. The findings help identify best-sellers and track seasonal shifts in demand.

Recognizing changes in demand early supports adjustments to product planning, assortment, inventory management, and sales promotion plans.

Improve the accuracy of marketing initiatives

By analyzing customer purchase data, you can more effectively design initiatives such as promotional campaigns, coupons, email newsletters, advertising, and item recommendations.

In addition, comparing the conversion rate, average order value, and repeat purchase rate for each initiative shows which efforts produce results.

Identification of issues and improvements

Purchase data can reveal which products are struggling to generate revenue and channels with weak conversion rates.

For instance, on an ecommerce site, if the rate of order completion after products are added to the customer’s cart is low, there could be issues with shipping costs, payment methods, the checkout form, or the amount of information on the product pages. By analyzing purchase data, it becomes easier to identify areas for improvement in items, pricing, sales channels, promotions, and the overall shopping experience.

Major analysis methods for purchase data

Common methods for analyzing purchase data include RFM analysis, decile analysis, segmentation analysis, basket analysis, ABC analysis, and trend analysis. Choosing the technique aligned with a specific purpose makes it more straightforward to uncover shopper buying patterns, product sales performance, and shifts in demand.

RFM analysis

RFM analysis is a method of segmenting customers derived from their last order date, purchase frequency, and average purchase amount. By categorizing shoppers into recent purchasers, regular purchasers, and high spenders provides a clearer picture of each group’s buying trends.

It is also useful for understanding and creating customer status classes, such as inactive shoppers who bought previously but have not done so recently.

Decile analysis

Decile analysis ranks customers by total spend and divides them into 10 groups, revealing the share of total sales generated by the highest-spending segments.

While RFM analysis classifies customers according to factors such as last order date, purchase frequency, and purchase amount, decile analysis focuses primarily on total spend.

Segmentation analysis

Segmentation analysis is a method of categorizing customers based on their attributes and purchasing behavior. For example, you can categorize customers into numerous segments in line with factors such as age group, region, membership tier, order frequency, purchase channel, and purchase category.

Grouping shoppers into distinct cohorts provides a clearer picture of their buying patterns and needs. It is also an effective analytical approach for organizing and interpreting customer insights.

Basket analysis

Basket analysis is a method for identifying products that are frequently bought together in the same transaction.

Recognizing items commonly bought together helps uncover product combinations and broader shopping trends.

ABC analysis

ABC analysis is a method of classifying products and customers by importance, drawing on metrics such as sales revenue, unit sales, and profit. To illustrate, A items contribute significantly to revenue, B items make a moderate contribution, and C items contribute minimally.

The classification helps assess each product’s value, simplifying the process of spotting best-sellers and items that require closer attention.

Trend analysis

Trend analysis is a method for examining sales, sales volume, and purchase frequency over a defined period. Reviewing daily, weekly, monthly, and seasonal figures reveals periods of peak demand and times when revenue will likely decline.

How to use purchase data

Purchase data can be used to guide marketing initiatives spanning product planning, sales promotions, inventory management, and customer relationship management (CRM). Several applications appear below:

Product planning and assortment review

Reviewing revenue figures reveals which products sell well and which underperform. Applying ABC analysis and trend analysis helps strengthen your core offerings, review your product lineup, and improve your item selection to meet seasonal demand.

Recommendations and personalization

You can recommend products tailored to each customer based on their order history, demographics, and purchase categories. For example, by analyzing basket data to identify which items are frequently bought together, you can recommend related products or offer bundle deals.

Improving promotional campaigns and sales initiatives

Purchase records show whether coupons, advertising, and promotional campaigns are leading to actual orders. Reviewing purchase rates, average order value, and the impact on repeat purchases helps refine messaging, target audiences, and email and coupon timing.

RFM analysis also supports tailoring the content of automated email campaigns and coupons according to the customer’s most recent transaction and buying frequency.

Demand forecasting and inventory management

By analyzing past sales and seasonal trends, you can identify periods of high demand and which products tend to run out of stock. Adjusting procurement and stock allocation helps prevent shortages and excess inventory.

Key points for using purchase data

Purchase data is a core element for any marketing initiative. Align the evaluation with the intended objectives and incorporate the findings into your strategies.

Clarify objectives

Before analyzing your purchase data, clearly define what you want to improve. Concrete goals include “increasing sales,” “boosting repeat purchases,” and “improving inventory management.”

If the objective is too vague, it becomes difficult to translate the analysis results into concrete actions. Define the objectives and key performance indicators (KPIs) and select the necessary data.

Centralize data management for analysis

Purchase data is often managed across multiple sources, including in-store POS systems, ecommerce sites, member apps, payment systems, and CRM systems. When data is fragmented, it becomes impossible to accurately track which channels customers are engaging with and how they’re making purchases.

When working with purchase data, it is valuable to analyze order history alongside payment and customer data. Linking shopper attributes, checkout options, membership tiers, and purchase channels provides a more nuanced view of the groups completing purchases, their touchpoints, and their preferred methods.

If you can centrally manage multiple datasets, it becomes easier to analyze the purchasing behavior of customers who shop both online and offline. When implementing omnichannel or OMO (online-merge-offline) strategies, establishing an environment that brings the datasets into a unified view is advisable.

Continuous analysis and improvement

Analyzing purchase data isn’t a one-time task. Because customer needs, best-selling products, and purchasing channels all change over time, it is important to continuously review the records and adjust your strategies.

By regularly monitoring metrics such as purchase rates, repeat purchase rates, and average order value after a promotional campaign, you can more easily evaluate the effectiveness of your initiatives. The use of dashboards and AI-powered analytics tools has increased, making it easier for people without specialized knowledge of structured query language (SQL) or similar technologies to monitor changes in sales and buying trends.

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