AI and Machine Learning (ML) seem to be on the minds of just about everyone who is seriously thinking about the future of Revenue Growth Management (RGM) in the CPG industry. While AI and ML may seem poised to revolutionize the way the industry handles complex business challenges, many firms in the industry lack the foundation to fully exploit the capabilities of AI and other forms of advanced modeling. In this piece, we will examine some of the potential use cases of AI and ML for RGM and how firms can establish the prerequisites needed to take advantage of advancements in technological decision-making.  

First off, what is AI and Machine Learning? 

Simply put, AI is the “science of engineering of making intelligent machines.” Using techniques like Machine Learning (which is a type of AI), computing power combined with advanced algorithms can be used to make predictions, identify hidden trends, and make associations that humans may struggle to identify given traditional resources. These techniques are distinct from Generative AI, such as ChatGPT, which specializes in generating content based on large corpuses of existing knowledge.  

What is required to leverage AI? 

AI and other forms of advanced modeling require a large amount of accurate and reliable data. While that may not seem like much of a revelation, many firms lack the adequate data foundation to take advantage of advances in data modelling. Any firm looking to exploit AI-enabled tools for RGM should consider what is required for AI enablement to ensure that your organization can best leverage its data.  

Strong data governance is required to ensure data accuracy and data consistency across your organization. You must also ensure that your organization has the technological infrastructure in place to store and manage the large amounts of data needed to capitalize on machine learning.  

What are the specific ways that RGM can benefit from Machine Learning?  

Once a strong data foundation has been established, your organization can begin leveraging ML to solve specific RGM-related business challenges. While there are numerous tools and applications on the market offering a wide range of capabilities, here is a short list of some of the specific RGM use cases that have shown the greatest promise.   

Trade Performance Optimization (TPO): The capabilities of the RGM-specific analytic tools on the market vary significantly, but nearly every tool available promises that they can deliver improved TPO. Where tools differ is in their ability to deliver predictive analysis vs. prescriptive post-event evaluations. Many of the more advanced tools on the market claim that they can optimize promotional scenarios to provide forward-looking guidance on how best to engage customers and consumers with promotional actions. Predictive forward-looking scenario planning provides the opportunity to tweak or alter promotional plans to maximize return. Ideally, small improvements to promotional efficiency should translate into significant improvements in the profitability of trade investments.  

Pricing Optimization: Next to TPO, Pricing Optimization is the most proven use case for advanced modelling and AI-enabled tools. Advanced modelling tools can be leveraged to perform multi-factor pricing analyses and consider a wide range of market data including retailer-level consumption data. While pricing curve analysis is nothing new, many RGM tools on the market can perform analysis much faster than many of these existing analytical services on the market and may some give organizations the ability to bring pricing analysis in-house without the need for external support.  

Advanced Category & Shopper Analysis: Two of the more intriguing emerging applications of AI and advanced modelling are category and shopper analysis. Immense cloud computing power combined with AI algorithms can analyze vast amounts of data in real-time, helping identify patterns, trends, and anomalies that humans might miss. AI tools can be leveraged to evaluate segmentation assumptions, determine optimal product assortment with a greater degree of certainty, better understand shoppers’ behavior to provide greater insight into buying patterns, and amplify your innovation insights. By ensuring the right products are on the right selves in the right stores, manufacturers can further enhance the effectiveness of promotional events to maximize the overall return on trade investments. 

Demand Planning: Demand Planning was another early application of AI and most advanced Demand Planning tools on the market today leverage AI or advanced modelling in some way. While the process of developing the demand plan is typically considered outside of RGM, effective demand forecasting is an essential for accurately forecasting spend and managing costs. Many of the more advanced RGM tools available on the market today can ingest inputs from demand planning systems to help ensure the consistency of assumptions made between functions. Sharing consistent baseline assumptions between systems helps ensure a common starting point for forecasting and analysis. In addition to the forward-looking application for demand planning, AI tools also offer the potential to disaggregate sales actuals to determine how well predictions matched reality.  

Looking Ahead 

To ensure that your organization is ready to leverage ML and AI for RGM purposes, it’s important to honestly assess your current data foundation. Prior to implementing ML capabilities, your organization must ensure that your sales, TPM, PoS, and consumption data are both accurate and reliable. Additionally, it’s important that your data is properly governed and stored to ensure the ongoing accuracy and reliability of any insights generated from your data.  

A solid data foundation will allow your organization to unlock the advanced decision-making capabilities that ML and AI enable and provide meaningful insights to your business.