Procurement Case Studies » Turning Spend Data into Category Visibility
Turning Spend Data into Category Visibility
· 6 min read
Representative scenario — a finance-led mid-market company
In this representative scenario, a finance-led company used the spend data captured by buying on Lapasar Mall to see spend by category, supplier and cost centre for the first time. That visibility surfaced consolidation and savings opportunities that fragmented records had hidden — a published platform capability.
This is a representative scenario illustrating how spend analytics applies once buying is captured on one platform — not an audited account of a named customer. It follows a finance-led team that wanted to act on its spend, not just record it.
The challenge
A mid-market company's spend data was scattered across suppliers, spreadsheets and accounting codes. Finance could see totals but not the structure beneath them — which categories, which suppliers, which cost centres — so it was hard to know where to focus.
Without categorised, structured spend data, opportunities to consolidate suppliers or negotiate on volume stayed invisible.
The solution
By moving buying onto Lapasar Mall, every transaction is captured in a structured, categorised form. Finance can see spend broken down by category, supplier and cost centre without assembling it by hand.
That single, consistent view turns spend from something recorded after the fact into something the team can analyse and act on.
Implementation
Buying was consolidated onto the platform so transactions were captured consistently, and categories and cost centres were set up to match how the business thinks about its spend.
With a few cycles of clean data, finance reviewed the biggest categories, identified where spend was fragmented across too many suppliers, and prioritised consolidation and negotiation where the numbers were largest.
Targeted outcomes
Targeted outcomes, consistent with the platform's public ROI assumptions: complete spend-by-category visibility, identified consolidation and savings opportunities that were previously invisible, and a repeatable basis for reviewing spend each cycle.
Key takeaways
- Structured, categorised spend data is the prerequisite for every later analytics gain.
- Visibility surfaces the opportunities; acting on the largest categories is where the savings come from.
- Capturing all buying on one platform makes category visibility a by-product, not a project.
Results at a glance
- Spend data: Scattered → structured & categorised
- Category view: None → by category & supplier
- Savings opportunities: Hidden → identified
- Spend reviews: Ad hoc → repeatable
Targeted savings
Target: 3–6% on categories surfaced for action
Visibility itself does not save money — acting on it does. Consolidating and negotiating the categories it surfaces saves at the rate our public ROI calculator assumes. A target, not an audited result.
Illustrative timeline
- Weeks 1–2 — Consolidate buying: Move buying onto the platform so transactions are captured consistently.
- Weeks 3–4 — Structure categories: Set up categories and cost centres to match how the business sees spend.
- Weeks 5–8 — First spend review: Review the biggest categories once a few cycles of clean data exist.
- Ongoing — Act & repeat: Consolidate and negotiate the largest opportunities, then review again.
Key takeaways
- Structured, categorised spend data is the prerequisite for every later analytics gain.
- Visibility surfaces the opportunities; acting on the largest categories is where the savings come from.
- Capturing all buying on one platform makes category visibility a by-product, not a project.
Frequently Asked Questions
What is spend visibility?
Spend visibility is a clear, structured view of what an organisation buys — broken down by category, supplier and cost centre — rather than just totals. It is the foundation for finding savings, consolidating suppliers and controlling budgets.
Are the figures in this case study real?
No. This is a representative scenario. Outcome figures are targets consistent with our public ROI model, not audited results from a named client.
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