Category Management in the New Reality: Is Retail Ready for Personalized Consumer Response (PCR)?
Category management has remained almost unchanged over the past few decades, relying on historical data and static analysis models. However, in the modern market, this approach no longer works. Consumers expect personalized experiences, retailers are trying to adapt to omnichannel strategies, and data is accumulating in massive volumes that cannot be processed without modern technologies.
This is why Dr. Brian Harris, Luc Demeulenaere, and Julie Beck in their paper “Revolutionizing Retail: The Next Wave of ECR & Category Management: Personalized Consumer Response (PCR)” proposed a new concept of Personalized Consumer Response (PCR). It is an evolution of Efficient Consumer Response (ECR) and traditional Category Management, combining AI analytics and automated assortment management for accurate forecasting and personalization.
The authors emphasize that traditional approaches are no longer capable of effectively responding to changes in consumer behavior, the growth of e-commerce, and increased competition. They propose a dynamic approach that enables retailers and manufacturers to make strategic decisions in real-time, improving category management.
Controversial Aspects of the PCR Concept
Our company, ABM Cloud, actively explores the potential of artificial intelligence in our solutions and deeply understands the complexities of this process. While Personalized Consumer Response (PCR) appears to be an ideal model for modern retail, we also acknowledge its potential challenges. Therefore, it is important to address some controversial aspects that may complicate its implementation.
Unrealistic Full Personalization
PCR envisions a shift from mass approaches to individual offers for each customer. However, do companies have enough technological and financial capacity to support such detailed personalization?
Additionally, the success of the PCR model relies on complete data sharing between retailers and manufacturers, which is not always feasible in practice. Many retail networks are reluctant to share their data, making the implementation of this concept more difficult.
Replacing Category Management with Artificial Intelligence
The authors of the concept hint that AI could fully replace category managers, making decisions autonomously. However, artificial intelligence is still unable to understand context the way a human can, especially in non-standard situations.
Category management is not just about data analysis but also involves strategic thinking, creativity, and negotiations with suppliers. Currently, AI can be a powerful tool that enhances the work of a category manager, but it does not fully replace them.
AI and Automation: What is Changing in Assortment Management?
Artificial intelligence is already being actively used in category management to solve the following tasks:
- Demand Forecasting – AI analyzes sales data, seasonality, customer behavior, and market trends, helping retailers develop effective category strategies.
- Dynamic Assortment Management – AI allows not just analyzing sales, but also responding to demand fluctuations by adapting the assortment.
- Personalized Solutions – AI helps create individual offers and optimize product displays based on customer behavior.
- Risk Reduction – AI predicts potential supply chain disruptions, helping to minimize risks of loss.
But can all retailers afford the implementation of AI? Here we face a key problem – the lack of digitalization.
Why Retail is Not Ready for AI: The Lack of Digitalization
Despite the active development of AI in retail, most companies are not yet ready for its implementation. The main issue is the absence of digitized assortment management and the general underdevelopment of processes.
While category managers still make decisions based on dozens of Excel files, and changes in the assortment are based on intuition rather than analytics, talking about AI is premature. The reasons for this include:
- Manual Management – Without automated systems, category management remains complex and labor-intensive.
- Fragmented Data – Sales, stock, and trend information is often scattered across different sources without a unified analytical system.
- Lack of Integration – Companies lack tools to integrate all sales channels into a single system.
Before moving on to AI solutions, it is essential to lay a solid foundation for digital automation.
ABM Assortment: The First Step in Digitalizing Category Management
This is precisely why ABM Assortment was created – a digital solution that automates assortment management, store clustering, and category analytics. It is the first step toward the implementation of AI.
What Tasks Does ABM Assortment Solve?
- Dashboard – Comprehensive sales analytics, product and category performance analysis, tracking seasonality and trends.
- Assortment Matrix – A tool for flexible management of product assortments and their distribution across stores.
- Warehouse Matrix – Automated management of product distribution between warehouses and stores.
- Analytical BI Module – In-depth data analysis for informed decision-making.
- Insights – Automated prompts that help: Control product statuses; Track test periods; Respond quickly to critical changes.
- The next step is the implementation of AI recommendations for adding new products and removing outdated ones, which will simplify decision-making and make assortment optimization even more efficient.
Conclusion: The Future of Category Management Starts with Automation
AI-powered assortment management is the future of retail. However, before transitioning to Personalized Consumer Response (PCR), a digital foundation for category management must first be established.
- Digitalization and Automation – The first step.
- AI Solutions for Analytics and Assortment Management – The next level.
- ABM Assortment – It is not just a system, but a stage in the evolution of category management.
