Retano CatMan

 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.

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.

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 Shelfspace 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.

• 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.