About the client
A Fortune 100 global leader had three years of point-of-sale spend data across 5+ markets, covering 200,000+ procurement records and over €200M in spend.
The data represented a significant volume of Point of Sale Materials (POSM) procurement activity, but information was spread across 4+ source systems, multiple languages, formats, currencies, and vendor records. Without a consistent data foundation, procurement teams had to spend significant time cleaning and reconciling information before they could analyze their POSM spend.
The challenge
Three years of messy, multilingual POSM records, but no reliable view of the spend
The company already had the point-of-sale spend data. The constraint was the quality and consistency of the information. Point-of-sale records contained different languages, corrupted characters, free-text descriptions, fragmented vendor codes, duplicate records, and missing dates. Without a consistent category structure, procurement could not reliably classify and analyze spend.
What was holding procurement back:
- POSM data came from 4+ source systems in different formats.
- Records contained 7+ languages and corrupted encoding.
- Transaction descriptions were largely unstructured free text.
- 1,441 vendor codes included fragmented and duplicate supplier records.
- 33,140 records had blank PO dates.
- No consistent category structure for analyzing point-of-sale spend.
- Manual classification and reconciliation created significant analyst effort.
The issue was not simply the amount of POSM spend. The company needed its existing procurement data to become consistent, classified, and usable for spend analysis.
The solution
Standardize the data. Classify the spend. Consolidate the suppliers.
ewiz procure applied a three-stage AI-powered data cleansing pipeline to transform fragmented point-of-sale procurement data into a consistent spend foundation.
The implementation brought together 100K+ line items from 4+ source systems and produced 90,909 clean, classified, and deduplicated transactions across 7 markets.
1. Standardized POSM procurement data
The first priority was creating consistent procurement data across source systems, markets, languages, currencies, and transaction formats.
Capabilities included:
- Data ingestion across 4+ source systems
- Encoding correction
- Normalization across 7+ languages
- Currency reconciliation to EUR
- Date repair
- Standardized transaction structure
This created a consistent data foundation for analyzing point-of-sale spend instead of requiring analysts to work across disconnected data formats.
2. Classified point-of-sale spend
The standardized transactions were then mapped into a consistent procurement taxonomy.
4 categories and 144 subcategories were created to classify the transactions, replacing manual VLOOKUP-based classification.
Capabilities included:
- Automated transaction classification
- 4 standardized categories
- 144 subcategories
- Free-text classification
- Consistent spend categorization
This gave procurement a common structure for analyzing POSM spend across markets.
3. Enriched and deduplicated supplier data
The next priority was making supplier and transaction information easier to analyze.
The solution extracted 9 structured attributes from free-text records and consolidated fragmented vendor codes into master entities.
Capabilities included:
- Attribute extraction from free-text data
- Vendor-code consolidation
- Supplier deduplication
- Master-entity creation
- Structured procurement records
The result was 90,909 clean, fully classified and deduplicated transactions across 7 markets, creating a single source of truth.
The impact
Fragmented POSM data became actionable procurement intelligence
Standardized and classified POSM data gave procurement a clearer view of supplier, campaign, and market-level spend. Supplier consolidation opportunities became visible. One-off campaign spend could be separated from underlying spend. Market-level trends could be identified and used to inform procurement decisions
- 99% classification accuracy
- 90% reduction in manual analyst effort
- 100% of spend classified and deduplicated
- 5 days to insight vs. 6+ weeks previously
- €40M vendor-consolidation opportunity across 4+ codes and 3 countries
- €30M+ one-off campaign spend identified
- 10X growth in permanent in-store signage surfaced
- 2.4X growth in the best-performing market identified
Why it worked
It solved the underlying POSM data problem
The company already had years of point-of-sale procurement data. The challenge was turning that data into something procurement could reliably analyze.
ewiz procure standardized, classified, enriched, and deduplicated the underlying records before applying spend analysis.
It created a consistent spend structure
Transactions from different markets and source systems were mapped into 4 categories and 144 subcategories, giving procurement a common way to analyze point-of-sale spend.
It consolidated fragmented supplier records
Fragmented vendor codes were consolidated into master entities, making supplier spend easier to analyze. This helped uncover the €40M vendor-consolidation opportunity that was hidden across 4+ vendor codes and 3 countries.
It turned procurement data into actionable insight
The cleansed data did more than improve data quality. It helped procurement identify one-off campaign spend, changes in permanent in-store signage, and market-level growth, giving teams a clearer basis for spend analysis, forecasting, and supplier decisions.
