Conquering RGM Complexity With a Unified, AI-Driven Data Approach

CGT: What role does AI play in consolidating and analyzing data from different sources to optimize decision-making?

Schneider: AI can act as a centralized intelligence engine, aggregating and analyzing data from multiple sources (e.g., sell-out data, promotional information, product data, survey results), to enable informed and data-driven decision-making. 

One of the biggest challenges is data fragmentation. Companies rely on a mix of internal sales data, retailer POS data, syndicated market data (e.g., Nielsen, Circana), trade spend records, and macroeconomic indicators. By consolidating and connecting all that information via AI, RGM teams (and neighboring teams like finance or marketing) can access a unified source of truth, improving alignment and efficiency in decision-making.

AI can also help CPGs understand the complex interplay between pricing, promotions, and consumer demand. Traditional analytics often fail to uncover deeper correlations, such as how price changes impact cross-brand cannibalization or how promotional effectiveness varies across channels and shopper segments. 

AI-powered analytics address this by: detecting hidden demand patterns that influence price elasticity and promotional ROI. They can also uncover cross-product and cross-market interactions that might be missed with traditional methods, as we ll as identify optimal pricing and promotional strategies by analyzing vast amounts of data.