Empowering category management with AI

Category management (CM) has long been a critical driver of retail success. In essence, CM is about managing a group of related goods (category) as a single business unit to maximize both profit margins and customer satisfaction.

However, traditional approaches that rely on historical data, expert judgment and relatively rigid rules are becoming increasingly outdated, failing to keep pace with exponential growth in data, volatile demand, hyper-personalization, and digital competition.

Conventional forecasting models used standard statistical techniques, such as regression and autoregression. They are easy to use and interpret, but this often comes at the cost of reduced accuracy compared to more advanced solutions.

Over the last two decades, AI and machine learning technologies have gained traction. Advanced models—such as neural networks and transformers—deliver greater accuracy and can handle vast amounts of diverse data, including time series with long-term dependencies. They uncover complex, non-linear correlations and seasonal patterns that human analysts might overlook.

By factoring in historical context, transformer models can identify the key factors influencing shifts in demand. Beyond that, next-generation neural networks no longer operate as black boxes—instead, they effectively explain demand behavior by exploring the reasons behind fluctuations, whether due to price movements, changes in inventory levels or other variables.

A transformer-based model equips category managers with a powerful tool for forecasting demand elasticity and optimizing pricing strategies—offering both high accuracy and the flexibility to support a wide range of retail scenarios.

Unlike traditional statistical techniques, AI-powered models are able to incorporate dozens of variables that influence demand. These factors generally fall into two categories: internal and external.

For instance, BaOne’s AI-based demand prediction model that relies on transformer neural networks is able to capture over 50 variables, delivering up to 95% accuracy for non-food consumer goods and up to 70% for clearance items.

The model produces highly accurate forecasts—even with noisy inputs. It accounts for long time spans and complex consumer behaviors, including diverse purchasing patterns and varied responses to marketing campaigns.

Key advantages of AI-based demand modeling:

AI plays a vital role in demand curve fitting—a technique used to measure the price elasticity of demand for individual goods or entire categories, depending on the research focus. The method evaluates how changes in prices or price ranges affect demand, sales volumes, profit margins, and other critical metrics.

By processing dozens of internal and external inputs, AI provides category managers with demand forecasts for weeks ahead, both with and without allowance for promotional pricing. Leveraging historical sales data, the AI model generates price ranges and demand curve scenarios, then recommends optimized discounts for promotions and clearance sales based on the expected sales velocity. This is how a forecasting model built on a neural network creates tailored sales and discount plans designed to maximize margins or revenue.

Below is an example of using BaOne’s AI-powered model in a baby juice promotion. Prediction accuracy naturally declines as the forecast horizon increases, but current promo terms and expected outcomes can still provide sufficient basis for advanced analytics, even over longer horizons.

Consider a 25% discount promotion running with a defined two-week promo strategy. The category manager can adjust discount terms flexibly to optimize either profit margins or sales volumes. The system supports shifting goals and scenario analysis for promotional campaigns, facilitating the creation of sophisticated promo mixes, including combined product bundles—a complex managerial challenge.

The impact of a promo offer can be assessed directly through the category manager’s interface. The system allows users to set a time frame (in weeks) within which goods should leave the warehouse and automatically assigns the discount rate needed to achieve the target sales velocity. The model provides price recommendations to achieve specific targets, such as selling out a product within five weeks.

With AI tools, businesses can determine, in real time, the optimal price for every product in every store—whether physical or online—by factoring in demand elasticity, competitor prices, inventory levels, sales margin or volume targets, and the overall strategy for the category.

Organizations can now automate their responses to competitors’ actions and optimize both the depth and frequency of discounts based on forecasts that accurately predict their real impact. Moreover, AI-driven solutions help prevent cannibalization of sales within categories or across brands.

Large retail chains offer an extensive range of products, often numbering in the tens or even hundreds of thousands. AI-powered product catalog analysis helps answer key questions such as:

This empowers category managers with a robust tool to build a focused, relevant product mix leading to improved inventory turnover and reduced logistics costs. With AI-driven predictive analytics, retailers can more accurately forecast future best and worst sellers—beyond what historical sales data alone can reveal. When applied to shopping cart analysis, AI can help uncover hidden cross-category relationships to inform smarter product placement decisions.

AI models analyze the effectiveness of current planograms and promptly generate optimized layout plans to maximize sales, return on shelf space, SKU visibility, and customer convenience. By considering inter-product dependencies and the retailer’s assortment strategy, these models help improve conversion rates, increase average check, and reduce time spent on manual planning—while tailoring product displays to local customer preferences and store formats.

Advanced AI models calculate reorder points (ROP) and quantities (ROQ) for thousands of SKUs by leveraging predictive demand analytics and factoring in considerations such as logistics, cost of storage, vendor terms and stockout risk. This helps businesses unlock significant cost savings by reducing excess stock and minimizing stockout losses. AI can also be of particular use when it comes to automating routine orders and optimizing warehouse operations.

Artificial Intelligence bridges the gap, transforming category management from a creative endeavor into an exact science. AI goes beyond simply automating the procurement routine—it opens new avenues for gaining consumer insights, forecasting market trends, and making strategic category-level decisions.