More than 80% of CPG CEOs aren’t satisfied with their Revenue Growth Management (RGM) results. But research shows that companies can see 80% SKUs gained, 50% Lower BAU cost with RGM initiatives. So, what’s the catch?
The reality is that a lot of CPG companies are not capitalizing on larger revenue growth opportunities because of being too fixated on traditional revenue growth management. It worked; however, it left a lot of potential on the table for traditional tools for managing the RGM levers better. Because of this, the RGM teams started exploring modern solutions in their revenue growth management strategies leading them to discover a holistic approach to optimize all RGM levers like: Pricing, Promotion, Product assortment and Distribution management.
Now what that approach is? Let’s find out!
How can you optimize RGM levers?
Before discussing the approach, let’s start with a fact. RGM which keeps pace with industry needs can generate significant benefits – studies show a potential 3% to 5% increase in gross profit. So, now the question in place is how do you rise to the occasion?
The key is to integrate the main elements of RGM to create a robust framework that enhances overall effectiveness.
1. Portfolio Price Pack Architecture
One crucial component of this integrated approach is Portfolio Price Pack Architecture (PPA). Rethinking your PPA involves prioritizing, planning, and taking action to enhance focus and effectiveness. By asking critical questions, you can clarify your priorities and align your actions accordingly.
Consider these essential aspects:
Time– At what time does the consumer decide which product to buy?
Brand umbrella – What is the brand category based on the product portfolio?
SKU size -What size is suitable for which event and which brand?
Price – what price should be set after considering all the above?
Channels – where can the products be sold based on the above?
Now with better understanding, you can leverage the AI and ML models used in RGM to predict sales impact of price & pack changes, map competitors, optimize pricing by channel all at once.
2. Trade promotion Optimization
Trade promotion is a substantial expense for CPG companies, typically accounting for 11% to over 27% of revenues, making it the second-largest item on the P&L statement. To optimize this expenditure, companies should incorporate robust data validation, integration, and modeling techniques.
For example – Using statistical methods such as regression analysis and time series forecasting, can accurately predict the sales impact of various discount strategies. Techniques like Monte Carlo simulations can help assess the risk and variability of different promotional scenarios, allowing for more informed decision-making.
Having ML algorithms employed will help you toanalyze historical promotion data, identifying patterns that inform optimal markdown strategies and ideal promotion plans. And this comprehensive approach enables companies in ensuring that promotional resources are allocated efficiently for maximum return on investment.
3. Product Assortment Management
Though most organizations have some sort of assortment management tools. But what these legacy planning platforms haven’t provided is a holistic enterprise view due to high reliance on historical sales data and guesswork to predict future demand. This has obvious limitations like limited future visibility, static assortment planning, and reactive approaches that results in delayed actions, ultimately risking missed sales opportunities and diminishing customer trust.
To move beyond these blind spots, we embrace proactive planning for assortment optimization. Here using scenario planning and demand forecasting helps companies simulate different market scenarios based on potential events or competitor strategies.
Incorporating Market Mix Modeling (MMM) further enriches this strategy by analyzing the impact of marketing channels and promotions on sales performance.
This allows for a comprehensive understanding of how factors influence demand, enabling data-driven decisions. By leveraging these analytical techniques, companies can have bettering product clustering, maximizing sales opportunities and customer satisfaction allowing for pre-emptive adjustments for proactive assortment optimization.
4. Distribution Optimization
Now it is equally important to have the right products reach the right consumers. Hence, once there is a clear understanding of product, price and promotions the next step is to align inventory, assortment, and planograms to improve product category performance. By identifying and working on underperforming portfolios, there is an opportunity to reduce capital and costs.
To do so, leverage demand forecast with hierarchical aggregation plans after experimenting at least with Price and Portfolio optimization to achieve better forecast accuracy.
Now being integrated with sales data, seasonality, market trends, etc. use techniques like Anomaly detection for outlier identification to multi-armed bandit algorithms for resource allocation to optimize channel strategies and preventing cannibalization.
Future of growth X Revenue growth management in CPG
Now with better understanding of what and how to optimize for better results. It must be taken into consideration that implementing these measures requires solid CPG analytics. Therefore, Companies looking to strengthen their revenue growth management will have some homework to do.
As an expert in revenue growth management, we understand that while there are numerous AI solutions availableyielding results but not all are suitable for every organization.
Hence, you may need to partner with specialized providers like Polestar solutions who expertise in understand the nuances of the industry, have sound analytical domain expertise, and tools to provide quick and accurate results to gain the upper hand to enhance their position in any market – USA or abroad, ultimately leading to stronger revenue growth and business performance.
