Why Spend Classification is Hard – And How to Fix It

5 reasons why spend data classification is harder than you think.

Clean data is the foundation for smarter procurement decisions. But unless you’ve tried to normalize and organize vast amounts of data, it’s hard to understand the complexity required to achieve successful spend classification.

At first glance, spend classification sounds simple: pull your transactions, group them into categories, and voilà. In reality, when dealing with millions of lines of data, thousands of suppliers, and constantly shifting business rules, that simplicity disappears fast.

Spend classification isn’t just about tagging data correctly. It’s about making sense of the messy, evolving reality of how your organization spends money – and doing it in a way that stays accurate over time.

Here are five reasons why spend classification is harder than you think, and why the right combination of people, process, and technology makes all the difference. 

1. Data Quality is the Hard Part – and it Never Ends

Let’s be honest: spend data is messy. Supplier names are inconsistent (IBM, Intl Business Machines, IBM UK Ltd), descriptions are vague (consulting services, marketing conference, or even INV-123), and GL codes often mean different things.

CPOs sometimes assume this can be fixed at the source, but the truth is, the source is constantly changing. New suppliers are added every week, teams evolve, and acquisitions happen. Even if you clean your data today, tomorrow’s data will already start to drift.

And while supplier relationship management systems help with control and compliance, they won’t solve this problem. They’re built for onboarding and governance, not for continuously reconciling inconsistencies across hundreds of systems and regions.

You also can’t realistically ask business users to check if a supplier already exists before they raise a purchase request. They don’t know – and they don’t care. Their goal is to get work done, not to manage supplier data hygiene.

Manual fixes can’t keep up. You need a system that automatically cleans, normalizes, and enriches your spend data—because data quality isn’t a one-time project. It’s an ongoing discipline and the single biggest factor in whether your procurement analytics are trustworthy.

2. ERP Taxonomy is not a Strategic Taxonomy

Most organizations classify spend using ERP or accounting codes. That’s fine for closing books, but not for strategic procurement.

An ERP taxonomy tells you where the money went. A procurement taxonomy tells you why you spent it and how to manage it to align with strategic business priorities.

For example:

• “Software” in your ERP might lump together cloud hosting, cybersecurity, and CRM subscriptions – but each of these has completely different market dynamics.
• “Marketing services” could include digital agencies, print vendors, event sponsorships, and freelance creatives – all requiring different sourcing strategies and performance measures.
• “Facilities” could hide a mix of cleaning, maintenance, and security spend, each with separate owners, suppliers, and contract structures.

When these differences aren’t visible, you lose the ability to manage strategically. You can’t benchmark, you can’t consolidate suppliers effectively, and you can’t tell a complete story to your CFO.

Defining the right taxonomy means setting the right level of visibility for your business goals.  

A manufacturing firm, for example, needs to understand categories like logistics, maintenance, and raw materials. A bank will care more about technology, consulting, and professional services.

And most importantly, taxonomy isn’t a static thing you define once, it requires ongoing governance:

• Who owns each category?
• How often is it reviewed?
• How is user feedback incorporated?
• How does technology enforce consistency?

Without governance, strategic spend classification decays fast. 

3. AI Automates the Heavy Lifting – and Learns

Artificial intelligence has fundamentally changed what’s possible in spend classification.
What used to take weeks of manually tagging now happens in minutes, with consistency no human team can match.

AI detects patterns across supplier names, descriptions, and historical spend data. It can detect that “Acme Corp” is a marketing vendor, even if the description is vague. It can infer that “XYZ Freight” belongs under logistics, even if that supplier never appeared in your data before.

But the real advantage isn’t just automation, it’s AI’s ability to get smarter over time.

Each time you review, correct, or validate AI classifications, the system improves. It learns your business context, your taxonomy, and your specific definitions of what “accurate” means. Over time, accuracy compounds.

This combination of automation and expertise context is powerful. AI handles the repetitive, high-volume work; your team provides the nuance, judgment, and context.  

Together, they create a feedback loop that continuously refines your spend data. Fast enough to keep up with the business, and smart enough to adapt to it.

4. Accuracy Isn’t Absolute – It’s About Alignment

Everyone loves a clean number: 90%, 95%, 99%.

But accurate spend classification depends on your goals:

• Do you measure by spend value or transaction count?
• By high-level categories or detailed subcategories?
• By your taxonomy or a universal standard?

A company looking for savings opportunities might only need accuracy at a high category level, whereas a team focused on ESG reporting or supplier diversity might need perfect subcategory detail.

That’s why true accuracy isn’t about chasing a universal percentage. It’s about alignment. Alignment between your taxonomy, your business rules, and the outcomes you care about.

When governance and feedback loops are strong, accuracy naturally stabilizes above the 90% threshold and continues improving over time. Not because it’s a promise on paper, but because it’s a managed, measurable process.

5. Spend Classification is Never “Done”

Suppliers change. Business models evolve. Mergers happen.

Every new system or category you introduce adds complexity to your data. Spend classification isn’t a one-time clean-up exercise, it’s an ongoing process.

Without a structured governance process, regular validation, and ongoing enrichment, even the best spend classification project will start to decay tomorrow.

That’s why leading procurement organizations treat classification as an ongoing cycle:
automate what can be automated, validate continuously, and refine as the business evolves.

Modern spend analytics platforms like SpendHQ make this possible by combining AI, data governance, and human expertise into one continuous process – keeping your spend data accurate, usable, and strategically relevant over time.