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Retano CatMan

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 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 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 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 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 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

+28% visit frequency and +18% loyal customer revenue following the launch of MEGO’s loyalty system

Posted: 09/06/2026

In February 2026, MEGO, one of Latvia’s major local supermarket chains, launched an upgraded loyalty program for regular customers powered by the Retano CRM & Loyalty platform. The new solution integrates flexible loyalty program management, advanced data analytics, and real-time omnichannel communication into a single digital ecosystem. What drove MEGO to modernize its loyalty program?… Read More »+28% visit frequency and +18% loyal customer revenue following the launch of MEGO’s loyalty system

Retail Demand Forecasting Software for Automated Replenishment

Posted: 03/06/2026

In the retail industry, demand forecasting remains one of the most critical components of effective supply chain management. Accurate forecasting is essential for maintaining optimal inventory levels, minimizing lost sales, and significantly reducing waste. Conversely, inaccurate predictions often result in empty shelves or excess stock, unnecessarily tying up working capital and eroding profitability. Many retail… Read More »Retail Demand Forecasting Software for Automated Replenishment

Next-Gen Supply Chain: Automated Replenishment & Inventory Optimization

Posted: 28/05/2026

As retail and supermarket chains continue to expand, inventory management becomes significantly more difficult to control manually. Modern retailers must coordinate replenishment across physical stores, e-commerce channels, dark stores, regional warehouses, and distribution centers — often within a single unified supply chain. In a highly dynamic demand environment, traditional static planning leads to chronic imbalances… Read More »Next-Gen Supply Chain: Automated Replenishment & Inventory Optimization

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