Conquering RGM Complexity With a Unified, AI-Driven Data Approach
Consumer goods companies are under increasing pressure to improve their RGM strategies, transitioning from siloed processes to a unified, integrated approach with improved business alignment, scalability support, and increased profitability.
Tim Schneider, head of sales engineering at Buynomics, shares his insights on how CPG companies can benefit from this shift, offering a deeper understanding of the challenges and opportunities that come with unifying pricing, promotions, trade spend, and assortment strategies. Through advanced analytics and a holistic view of RGM levers, companies can not only enhance decision-making but also maximize their revenue and profit potential.
Schneider also discusses how integrating AI-driven solutions into RGM processes is critical for transforming data into actionable insights and improving overall business performance.
CGT: How can transitioning from siloed RGM processes to a unified, integrated approach benefit CPG companies?
Tim Schneider: Many CPG companies still operate RGM in silos, with pricing, promotions, trade spend, and assortment managed independently across teams and regions. The fragmented approach often leads to inefficiencies, conflicting priorities, and suboptimal revenue decisions.
Moving to a unified RGM framework supported by advanced analytics can conquer the complexity of interdependent RGM levers and unlock significant benefits, including enhancing data consistency by breaking down silos for better decision-making; improving alignment of initiatives via a centralized RGM strategy with a holistic view of all levers (price, promotion, assortment strategies, trade spend); maximizing revenue and profit opportunities with a unified approach based on real-time insights; and improving scalability through modern AI-powered analytics.
By adopting an integrated approach, companies can move from siloed, local-optimum decision-making to integrated, data-based holistic-optimum decision-making.
CGT: What are some common challenges around RGM maturity that companies face when integrating AI into their processes, and how can they overcome these obstacles?
Schneider: While AI has the potential to transform RGM, it is common for many companies to face challenges along the way. Common ones that we observe regularly include a lack of clean, centralized data, a lack of business context of AI models, and a lack of trust in AI-generated recommendations.
To solve for these problems, CPGs can invest in data centralization and harmonization to ensure AI models can easily and automatically be fed with standardized, high-quality data across SKUs, regions, and retail partners.
Additionally, change management frameworks align people and processes to the new tools. Demonstrating AI’s quick wins for the wider team through pilot programs can also be a great way to showcase the value.
AI should be trained using CPG-specific data to ensure accuracy in particular market environments. Additionally, market expert input should be gathered prior to the AI model training to ensure market understanding. Afterward, AI insights need to be paired with human expertise to validate and refine recommendations.
Transparent testing and reporting help to significantly increase the trust in the AI solution’s recommendations. Transparency around accuracy metrics as well as joint tracking of actual results vs. forecasts once in market further increases trust.
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.
CGT: In terms of supply chain optimization, how can AI-powered forecasting and demand planning tools contribute to reducing costs and improving efficiency for CPG brands?
Schneider: For supply chain optimization AI-powered tools contribute to reducing costs and improving efficiency in a number of ways. For one, they can analyze data from multiple sources to predict demand fluctuations with greater precision to reduce overstocking and stockouts, optimize working capital, and improve service levels.
They can also align demand forecasts with inventory and production schedules to minimize excess inventory and reduce storage costs. Finally, they can identify potential disruptions, such as supplier delays, logistics bottlenecks, or raw material shortages, and suggest alternative solutions before issues escalate
By leveraging AI-powered forecasting, CPG companies can move beyond reactive supply chain management to a proactive, data-driven approach that enhances efficiency at every stage — from demand prediction to inventory optimization and disruption mitigation.
