Procurement Glossary
Classification rate: key figure for data quality in Procurement
November 19, 2025
The classification rate is a key performance indicator for evaluating data quality in procurement. It measures the proportion of correctly classified materials, suppliers or expenses in relation to the total quantity and forms the basis for well-founded purchasing decisions. A high classification rate enables precise analyses and strategic optimization. Find out below what the classification rate is, what methods exist for improving it and how you can use this key figure successfully in your company.
Key Facts
- Measures the percentage of correctly classified data in purchasing systems
- Typical target values are between 85-95% depending on industry and complexity
- Direct impact on spend transparency and purchasing strategies
- Automated classification can increase rates by 20-40
- Regular monitoring prevents data degradation
Contents
Definition: Classification rate
The classification rate is defined as the mathematical ratio between correctly assigned data records and the total number of all elements to be classified, multiplied by 100 for the percentage representation.
Basic components
The calculation is based on the formula: (number of correctly classified data records / total number of data records) × 100, taking various dimensions into account:
- Material classification according to UNSPSC or eCl@ss
- Supplier categorization by business segment
- Expenditure allocation to cost centers or projects
Classification rate vs. data quality score
While the classification rate specifically measures assignment accuracy, the data quality score comprehensively evaluates all aspects of data quality, including completeness and consistency.
Importance of the classification rate in Procurement
A precise classification forms the basis for spend analytics and strategic procurement decisions. It enables meaningful benchmarks, supplier consolidation and risk assessments.
Methods and procedures
Various approaches enable the systematic improvement of the classification rate through structured processes and technological support.
Manual classification procedures
Traditional methods are based on human expertise and defined sets of rules. Data stewards take over the assignment according to predefined criteria:
- Rule-based classification based on material designations
- Expert evaluation for complex or new product categories
- Validation by department heads
Automated classification approaches
Modern automatic spend classification uses machine learning and natural language processing. These systems learn from historical classifications and continuously improve their accuracy.
Hybrid processes and quality control
The combination of automated pre-classification with manual post-processing optimizes efficiency and precision. Data quality KPIs continuously monitor the classification performance.
Tacto Intelligence
Combines deep procurement knowledge with the most powerful AI agents for strong Procurement.
Key figures for controlling the classification rates
Systematic measurement and monitoring of classification performance requires defined key figures and regular evaluation cycles.
Primary performance indicators
The classification rate itself is the main key figure, supplemented by specific detailed metrics:
- Overall classification rate (target value: >90%)
- Category-specific quotas by material group
- Time-based development of classification accuracy
- Degree of automation in classification
Quality and efficiency metrics
Supplementary data quality KPIs evaluate the sustainability and efficiency of the classification processes. The degree of standardization measures the uniformity of the classification rules applied.
Operational performance indicators
Process-oriented metrics support continuous improvement: throughput times for reclassifications, correction effort for misclassifications and resource requirements for data cleansing are important control parameters for operational management.
Risk factors and controls for classification quotas
Inadequate classification quality can have far-reaching negative effects on purchasing decisions and corporate management.
Data inconsistency and wrong decisions
Low classification rates lead to distorted analyses and suboptimal procurement strategies. Incorrect categorization can conceal potential cost savings:
- Overlooked consolidation opportunities with suppliers
- Incorrect spend reporting to management
- Inefficient negotiation strategies due to incomplete market overview
Compliance and governance risks
Poor classification makes it difficult to comply with regulatory requirements. Master data governance is undermined by inconsistent categorization, which can cause audit problems.
Systemic deterioration in quality
Without continuous monitoring through data quality reports, the classification rate gradually deteriorates. New materials or suppliers are often insufficiently categorized, which has cumulative effects on the overall data quality.
.avif)
Practical example
A mechanical engineering company with 50,000 material master records achieved an increase from 72% to 94% within 18 months by systematically improving the classification rate. First, the company implemented an automatic spend classification for standard materials, which resulted in 60% of data records being correctly classified. A data steward then trained the purchasing teams in standardized classification rules.
- Reduction of manual effort by 40%
- 25% improvement in spend transparency
- Identification of potential savings of 2.3 million euros
Current developments and effects
Technological advances and changing market requirements are shaping the evolution of classification procedures in modern Procurement.
AI-supported classification systems
Artificial intelligence is revolutionizing data classification through self-learning algorithms. Deep learning models achieve accuracy rates of over 95% and significantly reduce manual effort:
- Automatic recognition of product categories from description texts
- Continuous improvement through feedback loops
- Integration into existing ETL processes
Standardization and harmonization
Industry-wide initiatives promote uniform classification standards. The convergence of different spend taxonomies simplifies cross-company comparisons and benchmarking activities.
Real-time classification
Modern systems classify data as soon as it is collected, minimizing downstream cleansing processes. This real-time processing significantly improves data up-to-dateness for supply chain analytics.
Conclusion
The classification rate is a fundamental building block for data-driven purchasing decisions and strategic procurement optimization. High classification quality enables precise spend analyses, effective supplier consolidation and well-founded risk assessments. Modern AI-supported processes significantly increase both efficiency and accuracy. Companies should see continuous monitoring and systematic improvement of the classification rate as a strategic investment in their data quality.
FAQ
What is a good classification rate?
A classification rate of 85-95% is considered standard for the industry, with critical categories requiring higher values. Complex industrial companies often achieve 90%, while trading companies with standardized products should aim for 95%.
How is the classification rate calculated?
The calculation is made by dividing the correctly classified data records by the total number, multiplied by 100. Example: 8,500 correct classifications out of 10,000 result in a rate of 85%. Sampling methods can be used for large amounts of data.
What factors influence the classification quality?
Data quality of the source data, clarity of the classification rules, level of employee training and technical system support are crucial. Incomplete material designations or missing manufacturer part numbers make correct classification considerably more difficult.
How often should the classification rate be checked?
Monthly monitoring of the overall quota and weekly checks for critical categories are recommended. New materials or suppliers require immediate classification to avoid quality deterioration. Quarterly in-depth analyses uncover systematic problems.
.png)
.png)
.png)
