Category Management: Key Insights for 2026 From Sievo

Rounding out our series of interviews with category management (CatMan)-related solution providers about how their solution supports the end-to-end CatMan lifecycle, today we hear from Natasha Toshevska, Head of Product Marketing at procurement analytics provider Sievo.

Read our definition of CatMan as a central procurement discipline, which includes a list of the potential benefits its deployment can afford.

How does your platform specifically enable or support category managers?

“Sievo supports category managers throughout the entire CatMan lifecycle by providing comprehensive solutions for data integration, analytics, strategic planning and performance management, strengthened by recommendations and peer benchmarking.

“Data integration, categorization and cleansing: Automates the data extraction from any ERP, data source or procurement system, handling the data cleansing and categorization with AI-powered classification accuracy guarantee of 94% and 98% coverage at the most granular level. It also enriches customer data with public data and relevant third-party data, as well as aggregated cross-customer community data, to provide a reliable, unified view of spend. This enables category managers to analyze historical spend, identify key drivers of spend growth or reduction and segment their categories effectively.

“Peer benchmarking and recommendations: Provides category managers with actionable recommendations by contextualizing their performance against peer benchmarks:

“Community Data Benchmarks: Category managers can compare their category performance against aggregated, anonymized data from Sievo’s entire customer base. This includes benchmarks for payment terms, pricing and other metrics specific to over 130 Level 4 categories across different industries and regions. For example, a category manager can compare their payment terms for a specific supplier category with those of their peers.

“AI-Powered recommendations: The system uses AI to analyze internal performance data alongside external market intelligence and community benchmarks to provide concrete recommendations. Category managers can set their user preferences to a particular category, so that the system delivers relevant insights.

“Category 360 view: In 2026 we will deliver a new ‘Category 360’ which aims to create a single source of truth for category data, combining internal data with external and proprietary community benchmarks to provide a holistic view for decision-making. Leveraging Sievo AI, category managers will be able to generate natural language overviews and qualitative strategy recommendations, such as consolidating suppliers or finding alternatives for single-sourced materials.

“Execution management: Once strategies are formulated with Sievo Initiative Management category managers can plan, track and manage the entire savings initiative process, from idea generation to execution and reporting. The solution’s seamless integration with the spend analytics and collaboration features ensures that strategic plans are translated into concrete actions with clear accountability. Initiative Management supports various initiative types, including savings and those with no direct financial impact like CO2 reduction.

“Performance tracking: We also provide robust tools for tracking performance against targets. Realized Savings Measurement offers transparency into the financial impact of the savings projects, aligning procurement and finance. Additionally, category managers can monitor supplier performance, track delivery performance development and ensure contract compliance in their categories.”

How does your solution manage data ingestion, normalization and enrichment across multiple systems and maintain accuracy at scale?

“Data ingestion and integration: The system is designed to integrate with any data source, including various ERPs, procurement systems and enterprise data lakes. It can extract data from both on-premises and cloud sources through tools like the Sievo Data Extractor, APIs and Azure Data Factory. This ensures all relevant procurement data, such as invoices, purchase orders and master data, is consolidated into a single platform.

“Data cleansing, normalization and enrichment: A core capability is addressing common data quality issues. It uses a combination of AI, machine learning and human expertise to perform data cleansing, normalization and enrichment:

“Spend classification: Spend is classified to the most granular level of a customer’s taxonomy with a guaranteed accuracy of 94% and 98% coverage.

“Supplier normalization: It uses a cross-customer shared supplier repository with over 100 million mapped suppliers, enriched with D&B data, to consolidate and normalize supplier information.

“Data harmonization: It standardizes data across different systems and regions, including currency and UOMs normalization, translations and material master data harmonization using AI-driven features.

“Accuracy at scale: AI and machine learning algorithms automate data cleansing and classification, improving accuracy rates and scalability while reducing manual effort and time to value. It guarantees data quality with a Service Level Agreement (SLA) of over 94% accuracy and is designed to handle large and complex datasets for global organizations. Cleansing improves over time utilizing categorization and reparenting feedback from the entire customer base to keep the database up-to-date.”

How do you help organizations translate analytical findings into actionable insights for category strategies or sourcing plans?

“Analytics and best-practice dashboards enable category managers to understand the root causes, correlations and reasons of their spend and its outcomes. Predictive analytics capabilities like Sievo Insights, What-if Scenario and Payment Term Simulation enable category managers to identify savings opportunities in their categories and make proactive data-driven decisions:

“Intuitive prescriptive analytics, such as Insights Hub and Automated Actions, recommends guided actions to optimize outcomes. This not only improves decision-making but also enhances process efficiency and reduces costs.

“AI-Powered Insights: The system automatically identifies opportunities, like cost savings, working capital improvements and risk mitigation from transaction-level data. It surfaces over a variety of insight types, such as price arbitrage opportunities, payment term consolidation, tail spend reduction and decarbonization opportunities.

“Guided actions: Each insight is presented on a card with a clear summary, opportunity value, detailed analytics and AI-powered recommendations for next steps. Users can act on insights directly from the interface. A key feature is the ability to convert an insight into a savings initiative with a single click, which automates the data flow and streamlines the process from opportunity identification to execution. This seamless integration helps category managers quickly build sourcing plans and category strategies based on reliable data.

“Strategic recommendations: Sievo is developing features to provide more qualitative strategic recommendations. By leveraging Sievo IQ (AI assistant), category managers will be able to generate natural language summaries and strategy suggestions based on a holistic view of internal, external and community data. This will help users who are ‘drowning in information’ to draw the right conclusions and decide on the best course of action.”

How is AI or machine learning used to anticipate category risks, market changes or supplier performance shifts?

“Sievo uses AI to provide predictive analytics, automate data processes and enhance risk assessment, while aiming for transparency.”

AI in risk prediction

“Predictive analytics: AI-driven predictive analytics help forecast future spending patterns, enabling organizations to anticipate budget needs and identify potential cost-saving opportunities.

“Risk assessment: Combining the customer spend data with external third-party risk data, Sievo can enhance risk assessment and mitigation by enabling proactive management of supplier risks and ensuring regulatory compliance.

“Market changes: Sievo Market Benchmarking and Community Data benchmarks use market or proprietary indexes to analyze the impact of market price fluctuations on purchasing categories and materials, helping to anticipate market shifts. A future improvement of the capability is to leverage AI to provide recommendations based on public data, which could include market changes.

“AI in data management: AI is central to data processing. It is used to automate data cleansing, normalization, classification and enrichment, which significantly improves data accuracy and reliability.

“AI-classification: The categorization engine is trained on 2% of global GDP data and used to increase the speed and accuracy of spend classification.

“AI-powered supplier normalization: The system maps all customers’ ERP suppliers to its highest global parent company relevant for procurement (i.e., supplier parenting). This is how they determine the number of contracts they have and with whom. Parenting improves over time by leveraging reparenting feedback from the entire customer base.

“AI-powered material harmonization enables category managers to find the best price with harmonized material visibility deduplicating up to 23% of the materials. This provides visibility to single-, dual- and multi-sourced materials within each category.

“AI for curating public data: Sievo AI processes and organizes public data, integrating all types into comprehensive actionable insights, like for example suppliers SBTi performance mapped to customers spend.”

Explainability and transparency

“Data transparency: Sievo provides full data transparency and an audit trail down to the transactional level, allowing users to understand the data behind the analytics.

“Insight explanation: For each AI-generated insight, the system provides simplified analytics and data visualizations to give users meaningful context and help them understand the development trends behind the opportunity.

“User feedback loop: The Insights are designed to learn from user’s feedback. When users discard an irrelevant insight, the system learns to show only relevant insights in the future, improving personalization.”

How does your analytics platform support CatMan to include sustainability, risk and innovation?

“Sievo has evolved beyond traditional spend analysis to support these expanded dimensions of value, offering dedicated modules and features for sustainability, risk and innovation.”

Sustainability (ESG and CO2)

“CO2 Analytics: The system provides a comprehensive CO2 Analytics solution to measure and reduce supply chain emissions. It calculates Scope 3 emissions in compliance with the GHG Protocol by combining spend data with emission factors from databases like Ecoinvent and Exiobase or supplier-specific data. The solution helps users identify categories with high emissions from the supply chain and track reduction targets.

“Supplier Sustainability: Integrated with data providers like EcoVadis to match spend with supplier sustainability scores across themes like environment, labor and ethics, the sustainability solution allows for internal benchmarking and risk identification.

“Initiatives: Both CO2 reduction and other sustainability goals can be managed as non-financial initiatives within Sievo Initiative Management. Sievo Insights can even surface decarbonization opportunities that can be turned into reduction initiatives.”

Risk management

“Supplier Risk Analytics: The system integrates with risk data providers to monitor financial, reputational, geopolitical, cyber and other risks. It matches spend data with supplier risk scores, allowing category managers to understand their risk exposure and find alternative, lower-risk suppliers from their existing supplier base.

“Proactive risk mitigation: Through the insights, the system proactively identifies opportunities for risk mitigation and can identify risks like single-sourcing or non-compliant purchasing, alarming users to act.”

Innovation

“Supplier Diversity: The Diversity Analytics solution provides visibility into diverse suppliers, supporting inclusive sourcing strategies that are known to promote innovation and creativity.

“Managing innovation initiatives: While not a primary focus, Sievo Initiative Management module is flexible enough to track value beyond cost savings, which can include innovation projects with strategic suppliers.”

Many thanks to Sievo for being part of this series.

You may find this handy, free-to-download Guide to Spend Analytics useful.