Transaction categorisation, 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 categorisation can play an important role in identifying and preventing fraudulent activities. This guide will explain how customers and businesses can use transaction categorisation to improve financial organisation and overall financial health.

What’s in this article?

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

How transaction categorisation works

Transaction categorisation, or transaction taxonomy, is the process of categorising financial transactions by nature, purpose, or type. This categorisation 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 organisation, either manually or through automated systems that read transaction descriptions, amounts, and other relevant data. The organisation, 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 pre-defined rules or keywords to assign categories based on transaction descriptions. For example, a transaction with the word “Starbucks” might be categorised under “Coffee Shops.”

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

It’s important to maintain consistency in the categorisation process by using the same categories and sub-categories over time and across different transactions. After the categorisation process, organisations 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 categorisation. 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 categorisation can help them track expenses, identify areas for cost savings, and make informed financial decisions.

• E-commerce businesses: These businesses deal with a large volume of online transactions. Categorisation can help e-commerce businesses analyse sales data, track customer behaviour, and identify popular products or services.

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

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

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

• Non-profit organisations: Non-profits must track donations and expenses carefully to ensure transparency and accountability. Categorisation can help them manage their finances effectively and demonstrate their impact to donors.

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

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

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

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

• Manufacturing companies: Manufacturing companies can use categorisation 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 categorisation 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 customised 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

• Cinema
  
• 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 labour
  
• 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 categorisation 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 categorise 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 categorisation

Manual categorisation involves categorising 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 categorisation, you can use Stripe’s reporting categories for basic categorisation, but you’ll need a third-party tool or integration for more granular control. Typically, you’ll need to combine manual and automated categorisation for optimal results, using automated categorisation for most transactions and manually reviewing and adjusting as needed.

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

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

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

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

Analyse categorised data

Once you have categorised your transactions, you can analyse the data for the following purposes.

• Track revenue and expenses: Analyse 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 categorisation for businesses

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

Financial visibility

• Cash flow: By categorising 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: Categorised data reveals which products, services, or customer segments generate the most revenue and which incur the highest costs.

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

Accounting and reporting

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

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

Strategic decisions

• Trends and patterns: Categorised 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 maximising profitability.

Customer insight

• Personalisation: By analysing transaction data, businesses can gain insights into customer preferences, enabling personalised marketing and tailored products and services.

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

Fraud detection and prevention

• Identifying unusual patterns: Accurate categorisation 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 personalise their offerings, predict customer preferences and needs, and improve their outreach and support.

• Personalisation: By analysing 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 re-ordering around this schedule, simplifying the customer’s experience and ensuring they don’t run out of essential supplies.

• Optimised 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 categorised 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 behaviours. Personalised email campaigns, targeted advertisements, and customised promotions are more effective when they come from detailed insights into customer transactions.

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