Today's grocers are juggling a multitude of priorities, from implementing successful omnichannel strategies and investing further in private label differentiation, to hedging the potential impacts of tariffs and inflation. While it is yet to be seen how economic and inflation-based factors will impact grocers, many are using category management strategies to keep a handle on product pricing. These methods help grocers determine if and how any cost increases can be absorbed into their product portfolio, which costs may need to be passed on to consumers to maintain profitability, and how pricing and promotional strategies can be deployed to drive volume.
Category management is traditionally defined as a procurement process that focuses on the price, promotion and assortment of goods both from a top-down investment, and a bottom-up execution standpoint. While many grocers have invested in a customer-facing digitization, many of the back-office processes have yet to be included in overall digital transformation initiatives, which is reflected in the fact that 90% of grocers still manage category management in Excel spreadsheets.
Many category managers are essentially "the CEOs" of their commodity group, handling both purchasing and sales responsibilities and working cross-functionally with demand planning, supply chain, marketing, and finance to manage initiatives. Category managers have to strike a balance between their suppliers' needs and their customers' needs, which can be difficult to manage through manual processes. As such, many are ready to apply data-centric planning platforms that can help connect cross-functional data, gain greater visibility across various levers, and produce accurate insights that enhance their decision-making.
However, before transitioning to a digitized category management process that is powered by an AI-enabled planning platform, a few things need to be in place to facilitate an effective implementation. The first step is to develop a data strategy and management process so that data is collected, cleansed and organized. This ensures that the relationship between different types of data is understood. An example of this is matching up internal with external SKU numbers so that any data insights are correlated to the correct products. The next step is to align the processes and workflows of category managers so that they are all using an agreed-upon set of best practices and working cohesively, which is a precursor to using a planning platform that provides a digitized, unified category management process.
Transitioning away from reliance on spreadsheets and implementing a digitized category management platform—especially one with agentic AI capabilities—can be beneficial in the following ways:
Scenario planning
Among category managers who are already utilizing category management platforms, a common application is scenario planning. A platform allows category managers and planners to test ideas and see multiple scenarios and potential outcomes before implementing a plan. Additionally, they are able to understand the correlation between certain factors and possible outcomes to ensure their decisions are directionally sound.
Machine learning models
Another layer of digitization that is benefiting category managers is the ability to access machine learning models that can ingest large volumes of data and provide proactive suggestions for how pricing, promotion, and assortment strategies can be adjusted. This allows planners to best meet the goals and initiatives for their specific category or commodity group.
Post-game analysis
Another function of category management where agentic AI's capabilities come to the forefront is in analyzing and automating the sales performance reporting process. Traditionally, this was a report that category managers had to prepare first thing on Monday morning to evaluate product sales throughout the week and parse out the underlying context of the results. When using a platform to assess this data and provide specific outcomes and actionable insights in a post-game analysis report, category managers can review the results and rely on the insights provided to build a plan of action for the week ahead.
By transitioning to a digital platform with agentic AI capabilities, category managers can focus less on gathering data and building reports, and instead emphasize the art of category management. This encompasses everything from finding ways to further diversify their product portfolios, strengthening supply relationships, negotiating better rates with vendors, to working with marketing teams to better understand consumer patterns to get ahead of trends.
