Retano CatMan

Q: What are the main benefits of category management systems for retailers?

Retano CatMan turns category management into a structured, data-driven process. It improves the consistency of decisions, reduces manual analysis, and helps ensure that assortment structures match category strategy, store formats, and real customer demand across the chain.

Q: How do category management systems use AI and machine learning?

Retano CatMan applies analytical and machine learning models to evaluate category structure, identify meaningful product attributes, and assess SKU contribution within clusters. AI-driven analysis reveals patterns, risks, and opportunities that are difficult to detect manually, supporting more objective and consistent assortment decisions.

Q: What role does demand forecasting play in Retano CatMan's assortment planning?

In Retano CatMan, demand forecasts are used as a reference input when evaluating assortment decisions at category and SKU level. Forecast data helps category managers consider expected demand trends alongside historical performance when reviewing assortment structure, cluster differences, and planned changes.

Q: What aspects of assortment optimization contribute to customer experience in retail stores?

Key aspects include a clear category hierarchy with defined roles, a balanced mix of price tiers and key attributes, differentiation of assortments by store cluster, and alignment between planned assortment and shelf execution. Together, these elements reduce shelf complexity and make product selection more intuitive and predictable for customers.

Q: How do retailers adapt assortments to different store types using data-driven category management?

Store clustering and item-level recommendations in Retano CatMan allow retailers to manage assortments at the level of comparable store groups rather than individual locations. This makes it possible to apply different assortment rules and SKU selections for each cluster, evaluate relevance at scale, and keep control without increasing operational complexity.

Assortment Category Management Powered by Big Data & AI

Modern retail is an art, and it hinges on accurate assortment category management.

Retano CatMan is a strategic category management solution that turns big data into concrete management decisions. Beyond mere analysis, it delivers precise actions designed to boost turnover, enhance profitability, and build stronger customer loyalty.

This solution is built for retailers aiming to outpace the market, empowering them with precise data, accurate predictions, and a well-crafted strategy.

Challenges Retano CatMan solves

Assortment Optimization

Data-driven category management enables the fine-tuning of assortments by leveraging sales and demand analysis. This ensures the avoidance of both overstocking and stockouts, maintaining optimal inventory levels on shelves.

Increased Profitability

Optimizing the assortment structure translates directly into higher margins and reduced costs. With Retano CatMan, decisions stem from real data, not mere assumptions—paving the way for more precise, effective, and profitable choices.

Enhanced Customer Experience

A custom-tailored assortment significantly boosts customer satisfaction and drives repeat purchases. Thanks to data analysis, retailers can genuinely grasp customers’ needs and offer products that perfectly align with their expectations.

Business Flexibility and Adaptability

Automated category management makes businesses far more agile and robust in the face of market shifts. Consistently tracking category performance allows for swift, decisive adjustments when needed.

Waste Reduction

Efficient inventory management significantly cuts down on excess stock and drastically minimizes losses from discarding expired or slow-moving products.

^{Data-driven category management: The key to stable profitability growth and increased customer loyalty.}

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Key Features

Clustering

Flexible store clustering by category, based on individual store properties.
Clustering driven by consumer demand patterns within specific categories and subcategories.
Seamless integration with Retano Shelf Space to enable store clustering based on category shelf space.

Assortment Rationalization and Planning

Analysis of adherence to category constraints during assortment planning.
ML model for item recommendation within the category for each store cluster.
Analysis of item effectiveness for category rationalization during assortment planning.
ML model for demand forecasting.
Construction of the target assortment based on sales forecasts.

Objectives, Strategies, and Effectiveness Analysis

Flexible system for configuring item, category, and subcategory properties.
Defining constraints within a cluster based on relevant category properties.
Defining the role and strategy of the category/subcategory within the business.

ML model for analyzing the actual role and strategy of a category.
Analytical reports on the effectiveness of categories and their associated items.

Ready to see how Retano CatMan can save you time, streamline processes, and boost your business’s efficiency? Request a demo!

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FAQ

What are the main benefits of category management systems for retailers?

Retano CatMan helps retailers transform category management into a structured, data-driven process. The system improves decision consistency, reduces manual analysis, and ensures that assortment structures align with category strategy, store formats, and real customer demand across the chain.

How can category management systems help increase profitability and reduce waste?

Retano CatMan supports more disciplined assortment decisions by showing how SKUs contribute to category structure, sales, and margin. By highlighting structural imbalances, low-rotation items, and deviations from category strategy, the system helps retailers make informed adjustments that reduce write-offs, slow movers, and inventory-related losses over time.

How do category management systems use AI and machine learning?

Retano CatMan applies analytical models to evaluate category structure, identify meaningful product attributes, and assess SKU contribution within clusters. AI-driven analysis supports objective assortment decisions by revealing patterns and risks that are difficult to detect manually.

What role does demand forecasting play in Retano CatMan's assortment planning?

Demand forecasting in Retano CatMan is used as a reference input when assessing assortment decisions at category and SKU level. Forecast data helps category managers consider expected demand trends alongside historical performance when reviewing assortment structure, cluster differences, and potential changes.

What aspects of assortment optimization contribute to customer experience in retail stores?

Key aspects include:
• a structured category hierarchy with defined roles and segments;
• balanced representation of price tiers and key product attributes within the category;
• differentiation of assortments by store clusters based on sales behavior;
• consistency between planned assortment structure and shelf execution.

Together, these aspects help reduce shelf complexity and make product selection more predictable for customers.

How do retailers adapt assortments to different store types using data-driven category management?

Store clustering and item-level recommendations allow retailers to manage assortment decisions at the level of comparable store groups rather than individual locations. This makes it possible to apply different assortment rules and SKU selections for each group, evaluate relevance at scale, and maintain control without increasing operational complexity.

How does Retano CatMan handle the scale and speed required by large retail chains?

Retano CatMan is designed to support assortment and category planning across hundreds and thousands of stores, wide assortments, and geographically distributed retail chains.

It uses a Big Data architecture as the foundation for both transaction-level processing and analytical calculations. Receipt-level data is processed within the Big Data environment, where aggregated and multidimensional analytical datasets are prepared for category analysis and planning. This approach supports fast calculations, stable performance, and consistent planning workflows even in large, complex retail environments.

Publications

Eurasian Retail Group selects Retano Digital Platform for launch of new retail chain in Kazakhstan

Posted: 31/03/2026

Retano and Eurasian Retail Group are launching a comprehensive implementation of the Retano Digital Platform to automate a new convenience store chain in Kazakhstan. The project deploys a unified ecosystem to manage 30 retail locations, ensuring operational transparency and efficiency from day one. Eurasian Retail Group represents a new strategic business direction for the Georgian… Read More »Eurasian Retail Group selects Retano Digital Platform for launch of new retail chain in Kazakhstan

Retano joins Retail Institute Italy

Posted: 19/03/2026

We are pleased to announce that Retano Solutions is officially a new member of Retail Institute Italy, the association that has represented the Italian retail ecosystem for over 30 years. With more than 200 members, including retailers, food & non-food companies, and service providers, Retail Institute Italy is a key reference point for fostering industry… Read More »Retano joins Retail Institute Italy

Grupo Upper – Spar Sureste (Spain) has selected Retano Shelfplan

Posted: 12/03/2026

We are very pleased to announce a new project with Grupo Upper – Spar Sureste. The group has selected Retano Shelfplan to drive in-store space management and the execution of planograms across its network of supermarkets and partner stores. This will make it possible to connect the head office and stores within the same operational… Read More »Grupo Upper – Spar Sureste (Spain) has selected Retano Shelfplan