AI category management: Getting started

Unleash the power of AI in retail analytics to drive assortment planning, inventory optimization, and overall category growth.

For category managers tasked with managing brand growth across various customers, regions, and retail channels, AI category management offers a new frontier to unlock insights and drive data-fueled success. By leveraging autonomous AI in retail, teams can move beyond basic reporting to true strategic foresight. Here’s how a category’s front-line captains can master these tools to ensure growth in the year ahead.

Key takeaways:

• Standardize sources with a semantic layer.
• Use LLMs to instantly uncover complex, actionable business insights.
• Apply AI to optimize planograms using real-time and regional data.
• Use machine learning to forecast demand and detect inventory voids. 
• Strengthen retailer partnerships with transparent data.

1. Build the right foundation

Retailer data is as integral to the category manager role as it is for sales forecasting and supply chain teams. This data – made increasingly accessible by leading retailers – contains a wealth of product- and store-level insights to fuel supplier brand strategies and lead overall category success. As POS data becomes the new standard for daily decision-making, category managers need data that is clean, consistent, and standardized across retailers to keep a handle on their intelligence.

A semantic layer is essential to create a unified framework for disparate retailer and distributor data sources. By standardizing metrics, relationships, and business logic, a semantic layer transforms raw data into a consistent, business-friendly structure that is accessible to technical and non-technical teams alike. This scalable foundation not only supports clean, accurate data flows but also ensures readiness for AI use cases, enabling algorithms to deliver actionable insights that fragmented pipelines (costing up to $500K annually to maintain) cannot support.

2. Unlock revelatory insights with conversational analytics

AI has the potential to answer your most complex business questions in seconds. Imagine not just asking questions like, “What were the regional drivers of our spring assortment performance?” figuratively, in querying data, but literally, leveraging your large language model (LLM) of choice for instant, actionable insights. By referencing a well of structured real-time and historical data, LLMs deliver business-friendly responses that fuel curiosity and accelerate decision-making. These tools make it easier than ever for category managers to analyze seasonal performance, refine assortments, and prepare for resets or annual reviews.

This capability extends to the omnichannel landscape, where the integration of online performance data is increasingly part of the retailer package. With omnichannel analyses leveraging AI, category managers can develop a baseline understanding and build strategies tailored to today’s complex shopper journey. 
To implement these tools effectively, organizations can look to enterprise-grade solutions like Snowflake Cortex or Databricks Genie, designed for scale and equipped to process the vast breadth of data category managers manage.

3.  Optimizing planograms with AI and store clustering

For category managers, planogram (POG) mastery is the key to ensuring that product facings and placements are optimized for performance at every store. Historically, facings were determined by historic data and syndicated reports, but AI is now enabling category managers to rethink these calculations, particularly as online order fulfillment influences in-store inventory dynamics.

With real-time POS data and AI-driven insights, category managers can pinpoint opportunities to refine shelf strategies. By layering performance metrics with regional trends, they can ensure high-velocity SKUs have sufficient facings, while underperforming products are reallocated to maximize profitability.

Open-source data models like Crisp’s Jupyter notebooks bring additional sophistication to POG (or MOD) strategies. The store clustering model, based on localized sales data and shopper behavior, helps category managers identify patterns and tailor assortments regionally. For example, keto diet-focused products that outperform in specific metropolitan areas might warrant additional facings there, while lower-demand stores could use that shelf space for products with stronger local appeal.

4. Maximize inventory and demand forecasting

Managing inventory levels effectively is both a science and an art, and AI can help category managers pinpoint these quantities with precision. In addition to visibility fueled by real-time data intelligence, tools powered by machine learning (ML) like Crisp’s voids detection reports, identify gaps where products should be selling but aren’t, empowering teams to proactively address issues before they impact performance.

AI models can also integrate external factors, like weather data, to provide a more nuanced approach to demand forecasting. For example, weather-driven insights can predict spikes in demand for seasonal items, such as hot beverages during cold snaps or outdoor products during warm spells.

5. Stay one step ahead with AI-driven collaboration and sustainability

Strong retailer collaboration is the cornerstone of category success, and AI provides the data-backed edge needed to build lasting partnerships. With real-time analytics, category managers can create more compelling, data-informed cases during seasonal reviews and line meetings, showcasing the contributions of their brands to the category’s overall growth and unlocking further opportunities.

AI also plays a critical role in sustainability initiatives, a growing priority for retailers. Category managers can use AI-driven inventory optimization tools to reduce waste and align with greenhouse gas (GHG) reduction efforts, strengthening partnerships with environmentally-conscious retail partners (while recovering profits lost to waste).