Spend Classification Series: Building a Procurement Taxonomy People Actually Use (Part 3)

Spend Classification Series – 5-part guide

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Many procurement teams struggle with how to categorise procurement spend in a way that’s both accurate and easy to use. In this post, we explore how to build a spend taxonomy that improves spend visibility, reduces the “Other” category, and supports automated spend classification.

Does this Sound Familiar?

Your spend feeds are finally funnelled into one clean “crystal-lake” dataset, yet the CFO’s dashboard still shows 23 percent in “Other.” Data integration delivers visibility, but only a smart spend taxonomy delivers understandability. Without the right category tree, even perfectly cleansed records scatter across cryptic labels and inflate the “Miscellaneous” bucket.

Today’s post solves that.

We’ll weigh standard codes (UNSPSC, NAICS) against purpose-built taxonomies, explain why the MECE rule keeps categories sharp, and show how a three-level hierarchy slashes confusion while boosting insight. By the end, you’ll know exactly how to move every transaction out of “Other” and into a bucket decision-makers recognise.

But before diving into classification accuracy and AI models, let’s establish the foundation: what exactly constitutes a spend taxonomy and why does its structure matter so much?

What Is a Procurement Spend Taxonomy and Why It Matters

A spend taxonomy is a deliberately designed, hierarchical list of categories, Facilities ► Utilities ► Electricity, for example, that tells your classification engine where every transaction belongs.

Unlike GL account codes, which exist for financial reporting, a procurement-focused taxonomy groups items the way sourcing teams negotiate and suppliers compete.

Why it matters:

Automated spend-classification tools can hit 95 percent accuracy only when they map to a clear, mutually exclusive, collectively exhaustive (MECE) category tree.

If the taxonomy is fuzzy or overlapping, AI models mis-tag invoices, dashboards bloat with “Miscellaneous,” and savings opportunities stay hidden.

In short, the taxonomy is the logic layer that turns clean data into actionable procurement insight, making or breaking the ROI of your entire spend-analysis programme.

Now that we understand what a spend taxonomy is and why it’s critical, the next question becomes: should you build from scratch or start with an established framework?

UNSPSC vs Custom Procurement Spend Taxonomy: Which Should You Use? 

Most procurement teams face the same fork in the road: grab a ready-made taxonomy like UNSPSC and adapt it, or invest the time to build something purpose-built for their business.

Each path has clear trade-offs that affect everything from implementation speed to long-term maintenance. Here’s how they stack up:

Bottom line: most organisations settle on a hybrid model, import a standard code set as the skeleton, then prune, merge, and relabel lower levels so the hierarchy speaks the language of your business while still satisfying external reporting requirements.

Regardless of which approach you choose, the underlying data needs to be clean and structured before any taxonomy can work effectively.

Here’s how the technical transformation happens behind the scenes.

How PI Handles Spend Data Transformation & Standardisation (In a Nutshell)

Purchasing Index runs an automated, two-phase pipeline that quietly handles all the heavy SQL lifting behind the scenes, so you see a clean, analysis-ready dataset without exposing any proprietary scripts.

The first phase focuses on schema standardisation and data structuring. During a brief onboarding workshop, every field from each source system gets mapped into a single, PI-approved reporting schema.

This canonical format means faster joins, easier downstream modelling, and zero rework when new feeds arrive, while keeping key client attributes intact and ensuring cross-source compatibility.

The second phase handles advanced transformation and enrichment. Combined codes, like account plus cost-centre strings, are decoded into separate, usable columns. Vendor, GL, and cost-centre IDs automatically map to plain-language descriptions for intuitive reporting.

Address strings get parsed for city, state, and postcode to enable geospatial spend views, while multi-currency amounts convert at transaction-date FX rates and month-year fields become proper dates for time-series analysis.

Everything runs through fully automated, SQL-driven processing. Each ingest follows a repeatable, audited pipeline with no spreadsheets or point-and-click macros. Real-time monitoring validates file integrity and flags anomalies instantly, while the same pipeline scales effortlessly as volumes grow.

This zero-manual-intervention approach means fewer errors, bulletproof traceability, and a process that keeps pace with business growth.

Net result: your team skips straight to insight, analysing spend trends, negotiating volume discounts, and challenging “Miscellaneous” spend, while PI guarantees data integrity, scalability, and audit-ready lineage.

With clean, structured data in place, the next step is designing a taxonomy that actually works, one that follows proven principles to avoid the common pitfalls that plague most classification systems.

Spend Taxonomy Best Practices: MECE Rule + 3-Level Hierarchy

A solid taxonomy must be MECE, Mutually Exclusive, Collectively Exhaustive. Picture a pizza cut into perfect slices: no slice overlaps another, and no cheese falls through a gap. If two slices overlap, you double-count spend; if there’s a gap, transactions default to Miscellaneous.

Goldilocks depth, three levels, not too many, not too few

Designing a use-able spend taxonomy is like city planning. You need major roads for high-level traffic, side streets for neighbourhood detail, and clear signs so no one gets lost. The MECE principle makes sure every “street” is unique while collectively covering the whole map.

The three-level “Goldilocks” model gives just enough depth for insight without burying analysts in micro-codes. Here is how the levels work in practice:

Level 1 – Executive lens (8–12 buckets, each ≤ 20 percent of total spend)

Think of these as city districts. They should be instantly recognisable to the C-suite and close to functional ownership. Typical examples: IT & Telecom, Facilities, Marketing, Professional Services, MRO, Direct Materials, Travel. If one bucket swells beyond 20 percent, split it. If another sits below 1 percent, consider folding it into a neighbour.

Level 2 – Category manager view

Each district divides into neighbourhoods that describe natural sourcing groups. Marketing breaks into Advertising, Creative Services, Events, Sponsorships. IT & Telecom becomes Hardware, Software, Network Services, Cloud Hosting. At this level, spend owners quickly spot where budgets drift.

Level 3 – RFP ready

Here you list the shops on the street. Advertising → Digital Ads, Social Media, Outdoor or Facilities → Maintenance → HVAC, Electrical, Cleaning. The test: could one supplier market or contract cover everything inside this leaf? If yes, it is granular enough. If no, split again but stop before the detail adds more admin than value.

Leaf-level sanity check

Ask: “Could a single RFP cover this leaf?” If Digital Ads shares suppliers, pricing logic, and performance metrics, keep it. If search and display behave differently, create two leaves yet avoid analysis paralysis.

The right spend categorization improves RFP readiness and spend reporting accuracy.

MECE keeps slices clean:

No overlap equals zero double counting; no gaps equals no “Miscellaneous”. Combine MECE with the three-level rule and you have a taxonomy that serves dashboards and sourcing strategies alike.

Of course, a perfect structure means nothing without people who believe in it. The next step is winning stakeholder buy-in through a collaborative design workshop.

How to Build a Procurement Spend Taxonomy with Stakeholder Buy-In 

Even the smartest spend-taxonomy development fails without buy-in. Block two to three hours for a workshop:

1. Invite the right mix: category managers, finance controllers, IT data stewards, and anyone who owns supplier relationships.
2. Sticky-note affinity clustering: ask participants to jot typical purchases on notes, then group similar items on a wall. Natural hierarchies surface fast when people see shared suppliers and workflows.
3. Debate & vote: for contentious splits (“Does SaaS live under IT or Professional Services?”), let the room vote. Majority wins, but document minority concerns, useful for revision later.
4. Document on the spot: assign a scribe to capture final Level 1–3 names and definitions in a shared doc. Clarity today prevents Miscellaneous creep tomorrow.

The outcome? Stakeholders see their language reflected in the taxonomy, feel ownership, and police data quality for you, shrinking the “Other” bucket before it grows.

A perfectly structured taxonomy is worthless if your team doesn’t buy into it. The key to adoption lies in collaborative design that gives stakeholders ownership from day one.

Even with stakeholder buy-in and a solid design process, certain predictable mistakes can still derail your taxonomy. Spotting these early warning signs, and knowing the quick fixes, prevents major headaches down the road.

Common Spend Taxonomy Mistakes and How to Fix Them Fast

Before you lock the taxonomy, run a quick quality-check for the three pitfalls that derail most first drafts. Catching them early prevents endless rework later and stops the “Miscellaneous” bucket from ballooning a month after go-live.

Here are the usual suspects, and the fast fixes that get you back on track:

1. Slip-up: Too broad – Symptom: 40 % of total spend sits in one catch-all bucket called “Services”

→ Fast repair: slice that mega-category by functional owner, IT Services, Legal Services, Facilities Services, so each new Level-1 bucket stays below 20 %. This redistributes spend into areas a single manager can control and immediately surfaces sourcing opportunities.

2. Slip-up: Too granular – Symptom: dashboards grind because separate codes track blue pens, black pens, and every ink shade in between

→ Fast repair: collapse micro-codes into a sensible leaf such as Writing Instruments and, if detail is ever required, store colour or SKU as an attribute, not a category. Analysts regain clarity without losing drill-down capability.

3. Slip-up: Duplicate buckets – Symptom: transactions bounce between “IT Consulting” and “Consulting – IT,” confusing reports and double-counting spend

→ Fast repair: consolidate under one agreed label, document the scope in a single-line definition, and update mapping rules so every future line item flows to the chosen bucket.

Consistency restores confidence and stops “Other” from creeping back.

A quick monthly “taxonomy health check” keeps these errors from re-inflating the Miscellaneous bucket.

These principles and practices form the foundation of any successful spend taxonomy, but implementing them from scratch can be a significant undertaking.

Avoid bloated dashboards by following spend taxonomy best practices and keeping your hierarchy MECE.

Key Take-aways: Spend Taxonomy Design

• Use the MECE rule: no overlaps, no gaps.
• Aim for an 8–12-bucket Level 1 and a 3-tier hierarchy overall.
• Let “leaf” categories mirror how you’d structure an RFP.
• Co-design the tree with procurement, finance, and IT to lock in ownership.
• Review regularly, too broad, too granular, and duplicates are easy to fix early.

Ready to transform your spend visibility without the months of taxonomy development?

Need a taxonomy that makes sense from day one?

Purchasing Index delivers ready-to-tailor category trees mapped to UNSPSC for easy reporting, plus governance tools to keep them fresh. Skip the blank-sheet pain and drop straight into high-accuracy, automated spend classification.

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