Transaction categorization, also known as transaction taxonomy, is the process of classifying financial transactions into predefined categories so businesses and individuals can better understand where funds are coming from and how they’re spent. This is a common process in personal finance management and business accounting.

With nearly 70% of businesses experiencing an increase in fraud losses in recent years, transaction categorization can play an important role in identifying and preventing fraudulent activities. This guide will explain how customers and businesses can use transaction categorization to improve financial organization and overall financial health.

What’s in this article?

• How transaction categorization works
  
• What types of businesses can benefit from transaction categorization?
  
• Examples of transaction categories
  
• How to implement transaction categorization with Stripe
  
• Benefits of accurate transaction categorization for businesses
  
• How to create a better user experience with detailed transaction insights
  

How transaction categorization works

Transaction categorization, or transaction taxonomy, is the process of categorizing financial transactions by nature, purpose, or type. This categorization helps individuals, businesses, and financial institutions manage their finances.

The first step of this process is to identify individual financial transactions within the records of an individual or organization, either manually or through automated systems that read transaction descriptions, amounts, and other relevant data. The organization, individual, or automated system then classifies and labels the transactions as specific categories or groups, using one of the following systems.

• Rule-based systems: These systems use predefined rules or keywords to assign categories based on transaction descriptions. For example, a transaction with the word “Starbucks” might be categorized under “Coffee Shops.”

• Machine learning models: These models use algorithms to learn from large datasets of labeled transactions and apply that knowledge to categorize new transactions. Machine learning models can be more accurate and adaptable than rule-based systems.

It’s important to maintain consistency in the categorization process by using the same categories and subcategories over time and across different transactions. After the categorization process, organizations use various analytical tools to gain insights into spending patterns, income sources, and other financial trends in order to make decisions about budgeting, investing, and overall financial management.

What types of businesses can benefit from transaction categorization?

Almost any business that handles financial transactions can benefit from using transaction categorization. Some types of businesses are particularly well-suited to this practice. These include:

• Small and medium-sized businesses (SMBs): Often, SMBs have limited resources to put toward financial management. Transaction categorization can help them track expenses, identify areas for cost savings, and make informed financial decisions.

• Ecommerce businesses: These businesses deal with a large volume of online transactions. Categorization can help ecommerce businesses analyze sales data, track customer behavior, and identify popular products or services.

• Subscription-based businesses: These businesses rely on recurring revenue. Categorization can help them track subscription payments, identify churn, and forecast future revenue.

• Businesses with multiple revenue streams: Companies with diverse income sources can use categorization to track each revenue stream’s performance and allocate resources accordingly.

• Businesses with complex expense structures: Companies with many expense categories can use categorization to track spending, identify cost centers, and update their budgets.

• Nonprofit organizations: Nonprofits must track donations and expenses carefully to ensure transparency and accountability. Categorization can help them manage their finances effectively and demonstrate their impact to donors.

• Freelancers and solopreneurs: Independent contractors can use categorization to track their income and expenses, prepare for tax season, and make informed financial decisions.

• Restaurants: Restaurants can use categorization to track food costs, labor expenses, and revenue from different menu items.

• Retail stores: Retail stores can use categorization to track sales by product category, identify top-selling items, and analyze customer spending patterns.

• Service-based businesses: Service-based businesses can use categorization to track billable hours, project expenses, and client payments.

• Manufacturing companies: Manufacturing companies can use categorization to track raw material costs, production expenses, and sales of finished goods.

Examples of transaction categories

Transaction categories break down income and expenditures into manageable, logical groups. For businesses, detailed categorization improves financial analysis and reporting and strategic decision-making. For individuals, it improves budgeting, savings, and overall financial management.

Here are some common transaction categories. Depending on the user’s specific needs, these categories can be customized further.

Personal finance categories

Housing

• Rent
  
• Mortgage payments
  
• Home insurance
  
• Property taxes
  
• Maintenance and repairs
  

Utilities

• Electricity
  
• Water
  
• Gas
  
• Internet
  
• Cable
  

Transportation

• Fuel
  
• Public transit costs
  
• Vehicle maintenance
  
• Parking fees
  

Car payments

• Food
  
• Groceries
  
• Dining out
  
• Fast food
  

Healthcare

• Health insurance premiums
  
• Doctor visits
  
• Prescriptions
  
• Dental care
  

Entertainment

• Movies
  
• Concerts
  
• Sporting events
  
• Books
  
• Hobbies
  

Savings and investments

• Savings account deposits
  
• Retirement contributions
  
• Investment purchases
  

Education

• Tuition
  
• School supplies
  
• Student loans
  
• Online courses
  

Business finance categories

Revenue

• Product sales
  
• Service fees
  
• Royalties
  
• Investment income
  

Cost of goods sold (COGS)

• Raw materials
  
• Direct labor
  
• Manufacturing supplies
  
• Shipping costs
  

Operating expenses

• Salaries and wages
  
• Rent or lease payments
  
• Utility expenses
  
• Marketing and advertising
  
• Professional services (e.g., legal, consulting)
  

Capital expenses

• Equipment purchases
  
• Building improvements
  
• Technology upgrades
  

Taxes

• Income tax
  
• Sales tax
  
• Payroll tax
  
• Property tax
  

Debt service

• Interest payments
  
• Principal repayments
  

Miscellaneous expenses

• Travel and entertainment
  
• Insurance premiums
  
• Office supplies
  

How to implement transaction categorization with Stripe

Stripe provides balance transaction objects that represent every transaction that affects your Stripe account balance, including payments, refunds, disputes, and fees. You can also add metadata to your Stripe charges, customers, and other objects, which you can use to store additional information about the transaction, such as category, product type, or customer segment. Businesses that use Stripe can categorize transactions with the following steps to gain financial insights, simplify bookkeeping, and make data-driven decisions.

Define your categories

Create a clear and consistent set of categories that align with your business needs.

• Income: This might include sales, subscriptions, and donations.

• Expenses: This might include refunds, fees, chargebacks.

• Product or service categories: These categories could be clothing, software, or consulting.

• Customer segments: These segments could be retail, wholesale, or enterprise.

Implement categorization

Manual categorization involves categorizing each transaction within Stripe’s Dashboard or API by assigning it a category label or tag. This method can be time-consuming for businesses with a high volume of transactions. For automated categorization, you can use Stripe’s reporting categories for basic categorization, but you’ll need a third-party tool or integration for more granular control. Typically, you’ll need to combine manual and automated categorization for optimal results, using automated categorization for most transactions and manually reviewing and adjusting as needed.

Here are some popular options for third-party integrations that automatically categorize transactions.

• Accounting software integrations: Many accounting platforms, such as Xero, have integrations with Stripe that can automate categorization based on rules or matching.

• Financial analytics tools: Tools such as Baremetrics or ChartMogul can analyze your Stripe data and provide automated categorization, reporting, and insights.

• Custom solutions: If you have specific requirements, you can build your own custom solution using Stripe’s API or webhooks.

Analyze categorized data

Once you have categorized your transactions, you can analyze the data for the following purposes.

• Track revenue and expenses: Analyze your financial performance by category.

• Identify trends and patterns: Spot opportunities for growth or cost savings.

• Make informed business decisions: Base your strategies on data-driven insights.

• Simplify accounting and reporting: Improve your bookkeeping and tax preparation process.

Benefits of accurate transaction categorization for businesses

Accurate transaction categorization empowers businesses to make informed decisions, improving operations and driving growth. Here are some key advantages.

Financial visibility

• Cash flow: By categorizing transactions, businesses gain a comprehensive understanding of their income and expenses, which improves their ability to manage cash flow and forecast their finances.

• Profitable and unprofitable areas: Categorized data reveals which products, services, or customer segments generate the most revenue and which incur the highest costs.

• Expense tracking: Accurate categorization allows businesses to track expenses in detail and identify areas where they can reduce costs.

Accounting and reporting

• Bookkeeping: Categorized transactions make it easier for businesses to reconcile accounts, prepare financial statements, and comply with tax regulations.

• Audits: Accurate and organized financial records make audits easier, reducing the time and resources required for compliance.

Strategic decisions

• Trends and patterns: Categorized data can reveal spending patterns, seasonal fluctuations, and emerging trends, enabling businesses to proactively adjust their strategies.

• Marketing campaigns: By tracking marketing expenses and correlating them with revenue generated, businesses can assess the effectiveness of their marketing efforts.

• Pricing and inventory: Transaction data can inform pricing decisions and inventory management, ensuring optimal stock levels and maximizing profitability.

Customer insight

• Personalization: By analyzing transaction data, businesses can gain insights into customer preferences, enabling personalized marketing and tailored products and services.

• Customer segmentation: Categorized transactions allow for effective customer segmentation, allowing businesses to target specific groups with relevant promotions and loyalty programs.

Fraud detection and prevention

• Identifying unusual patterns: Accurate categorization can help identify unusual transaction patterns that might indicate fraudulent activity, allowing businesses to intervene early.
  

How to create a better user experience with detailed transaction insights

Detailed transaction insights can create a better user experience by allowing businesses to personalize their offerings, predict customer preferences and needs, and improve their outreach and support.

• Personalization: By analyzing detailed insights from each transaction, businesses can tailor their offerings to match the needs and preferences of their customers. For example, if transaction data shows that a customer frequently purchases organic products, a retailer might recommend to them other organic items or offer special deals on organic goods. This makes the customer’s shopping experience more relevant and engaging.

• Predictive analytics: By understanding patterns and trends, businesses can promote products or services before the customer explicitly requests them. For example, if transaction insights reveal that a customer orders printer ink every three months, a business could set up reminders or allow the customer to opt into automatic reordering around this schedule, simplifying the customer’s experience and ensuring they don’t run out of essential supplies.

• Optimized customer support: Detailed data can help support teams understand the context of a customer query or issue faster. If a customer contacts support regarding a purchase, having immediate access to categorized transaction details allows the support team to provide faster, more accurate, and more helpful responses.

• Product and service development: By using transaction data to inform product development, businesses can align new products and services with real customer needs and market demand. For example, if transaction insights show an increasing purchase trend in eco-friendly products, a company might decide to expand its range of sustainable offerings.

• User interfaces: Understanding how customers interact with your website or app— such as which features they use most, at what point they abandon transactions, and where pain points crop up—can guide improvements in the user interface. Improvements might include simplifying the checkout process, making navigation more intuitive, or providing more relevant information where users need it most.

• Marketing strategies: Transaction data can help businesses create more effective marketing campaigns that speak directly to the customer’s interests and needs, based on past purchasing behaviors. Personalized email campaigns, targeted advertisements, and customized promotions are more effective when they come from detailed insights into customer transactions.

• Loyalty programs and rewards: By analyzing transaction data, businesses can create personalized rewards programs with discounts, perks, or benefits that are most appealing to a specific customer, thereby increasing engagement and loyalty.