What is Revenue Growth Management?
Revenue Growth Management (RGM) adopts an integrated approach brought about by different functions of an organization working in unison. It involves understanding shopper behavior, developing strategies for specific markets and channels, executing growth plans, and minimizing losses, especially in a volatile economy.
Data and analytics play an essential role in revenue growth management in the CPG industry. Data-driven algorithms form the basis of the decisions made, and offer an understanding of how to execute price pack architecture, how to arrive at the right pricing, what channels to sell the product in, and what trade promotions to adopt, all of which when performed efficiently can contribute to growing revenues in a profitable manner.
What are the pillars of revenue growth management?
The three pillars include pricing, portfolio and mix, and trade investments/promotions.
- Pricing is one of the main pillars of RGM contributing to revenue growth. The key is for companies to achieve an equilibrium in pricing, where the brand equity is aligned with pricing in order to meet margin goals while maintaining or improving a competitive position (market share).
- Portfolio and mix refer to the assortment of products that the company sells in specific markets and channels. Companies arrive at this through a careful study of shopper preferences and what products they are most likely to buy. This not only helps retain existing shoppers but also attract new ones. Portfolio and mix optimization is an important lever to drive the expansion of the brand portfolio.
- Trade investments refer to promotional spend targeted at retail stores and online marketplaces, which can be in the form of discounts or complementary merchandise to drive a tactical short-term uplift in sales. Companies measure the success of trade promotion campaigns based on returns on trade funds invested.
What are the principles of revenue growth management?
The revenue growth management discipline is evolving in two dimensions:
- Strategic RGM: This involves understanding the product portfolio, consumer behavior, competitor behavior, and promotion channel management, to develop a long-term, end-to-end revenue growth strategy. It requires connecting different teams and the pieces they hold within an organization, and aligning them to the RGM strategy and the shared goal.
- Precision RGM: This involves using digital tools to produce a precise and detailed analysis, and create data-and-analytics-driven solutions tailor-made to cater to customer trends and needs. Specific digital tools are available to help decide the pricing, assortment of products, demand forecasting, and trade promotion effectiveness.
What are the benefits of revenue growth management?
A solid revenue growth management strategy helps companies:
- Harness data and analytics to gain detailed insight into shoppers’ needs, increasing revenue and profitability.
- Understand which promotion channels can lead to favorable return on investment (ROI), so companies can continue to leverage them or adjust use accordingly.
- Achieve sustainable and profitable revenue growth that results from making well-informed decisions, as opposed to trial and error.
- Get critical insights on untapped markets.
What is the role of a revenue growth manager?
A revenue growth manager drives strategy using data and technology tools including AI to identify successful approaches to maximizing revenues. This includes evaluating the most appropriate and effective sales channels, devising promotions, and executing campaigns.
This involves working across the business with product teams, sales and marketing to ensure a holistic RGM strategy, and managing senior stakeholders so that the strategy is championed from the top.
What are the RGM strategies being used for pricing?
The standard elasticity model that is commonly used to create strategies for pricing has become the bare minimum for most companies to set tactical pricing and related promotion decisions.
However, brands need to do more than the basics, and are increasingly turning to AI-driven RGM tools that help them understand consumer behavior. These tools can also provide insights into the ideal display and mix of products on the shelves, and the most effective sales channels.
Brand equity trackers or surveys help marketers understand brand equity and make related pricing decisions. Econometric models can deliver information on competitors, which helps marketers make brand-building and promotion decisions as part of the pricing strategy, to ensure that the brand value is not diluted. Brands are using these models to arrive at strategic pricing instead of tactical pricing, and to establish guidelines they need to adhere to for the next couple of years, in pricing.
What is the role of AI in revenue growth management?
Using a data-driven model powered by AI can help companies arrive at well-considered pricing and price pack architecture decisions. It helps by summarizing insights from data and, combined with a statistical model used for predictive analysis, helps create hypothetical scenarios of pricing, price pack architecture, and trade promotion.
An AI tool can also forecast consequent revenue and profit changes more accurately than traditional RGM models, and curbs brands making random price changes. Based on what trade promotion measures were successful in the past, it can make recommendations for the present, including recommendations for the likely most effective channels for promotions. It can also reduce the probability of human error.
Why should brands look beyond simple price elasticity models to deliver sustainable and profitable revenue growth?
Traditional price elasticity models deliver short-term growth and are not ideal for navigating the nuances of a constantly shifting economy, unlike advanced RGM models and solutions that rely on data and analytics to help companies make long-term and sustainable decisions.
These advanced tools can reduce the guesswork in pricing decisions for CPG revenue managers while also showing the impact those decisions will have on the brand equity and market share.
They draw data from a range of sources including margin information, master data and external sources such as syndicated data, brand equity survey data and demographics into an econometric and AI model to arrive at the optimal price for profitable revenue growth. They analyze the effect on market share across parameters such as short-term and long-term, pack or brand level, volume growth and growth potential, and finally recommend an optimal price based on a price elasticity analysis that considers the brand's power in a competitive setting and helps CPG businesses make accurate pricing change decisions.