Unlocking Revenue Growth with Modern-Day RGM Practices in CPG

Transforming revenue growth strategies

Over the last few years, Revenue Growth Management (RGM) has undergone significant shifts. As a result, the most successful businesses today leverage data-driven RGM practices instead of the sheer intuition of experienced professionals.

However, this transition is not coincidental. It was necessitated by a rapidly evolving business landscape marked by omnichannel selling, increasingly complex consumer behavior, supply chain reorganizations, availability of data, increase in capabilities, and digital advancement.

All of these factors rendered traditional RGM strategies ineffective. For example, during the pandemic, when consumer purchase behavior was geared towards stockpiling, traditional trade promotion optimization (TPO) that took place only at fixed intervals may not have yielded positive results. In addition, as consumer behavior changed and buying shifted towards residential convenience stores, companies using data-driven insights were able to respond much faster to these trends with corresponding pack format changes.

Business processes now happen at a high velocity, are much more dynamic and distributed, and generate exabytes of data containing crucial insights. Manual RGM practices of yesterday are not suited to drive CPGs to their optimum growth targets in today’s business landscape, and neither can they help them supersede the key challenges impeding their growth. What they need instead is a modern-day, data-driven RGM strategy.

Data-driven RGM: How it unlocks profitable revenue growth for CPGs

When considering all growth factors at an organization’s disposal, RGM emerges at the top. In fact, over the last five years, RGM drove 70% of organic growth for CPGs. Therefore, it is not surprising that 72% of these businesses are adding analytics capabilities to modernize their RGM strategies. ^[1]

In pursuing their vision for a data-driven RGM practice, CPGs aim to address the following key challenges in RGM, paving the way for unbounded growth by unlocking new ceilings of optimal performance.

1. Pricing optimization:

Traditionally, CPGs employed multiple resources to review pricing and promotion efficacy regularly for individual retail partners. This approach is failing them at a time when CPGs are selling through multiple channels simultaneously, each returning variable margins over time. Moreover, customer demand is now a function of more variables than ever, making such approaches ineffective.

Setting optimal price points now requires market-specific analyses that account for competitor pricing and brand price elasticities to achieve an alignment between list and target prices. Moreover, these analyses must inform pricing decisions in real-time (usually in an automated fashion), especially across D2C and e-commerce channels, to maximize potential profits on each sale.

2. Promotion effectiveness:

CPGs typically spend 11-27% of revenues on trade promotions,^[2] but 72% of these lose money.^[3] One of the key reasons behind this is that scenario-based Trade Promotion Optimization (TPO) solutions implemented by CPGs are suited for single-channel partner relationships. Whereas promotion decisions nowadays are spread out in multiple channels and geographies.

Therefore, these solutions are bound to fail without a holistic view of consumer impact across categories and channel-specific data. Data-driven RGM leverages historical promotion data to infer contextual promotion opportunities and identifies optimal promotion types, timings, and duration, along with forecasts of their expected impact. This helps optimize the output of each promotion dollar on the bottom line. In addition, companies using connected insights across promo planning and supply chain are able to adjust their promo calendars according to the data on stockouts and inventory in transit.

3. Assortment planning:

Legacy RGM practices relied on a wait-and-watch approach to product assortment decisions. However, this would result in cost-extensive investments in product development and launches, resulting in significant amounts of wastage. Today, keeping products that perform sub-optimally in the market stifles growth opportunities for CPGs and traps capital in non-performing assets.

The modern approach to assortment planning eliminates this wastage. It uses algorithms to simulate and forecast sales results and predict changes when new products are added or subtracted to the mix. Also, with these complex algorithms, integrated datasets are crunched to derive unique results for market clusters and customer segments. These practices enable CPGs to rationalize their portfolio, helping them eliminate merchandising resets for low-selling products.

4. Trade spend optimization:

Trade spend data is typically spread across various sources and siloes, and verifying forecasts against the actuals can be challenging. Moreover, reconciling prior-year events and deductions claimed months after actual trades further exacerbates the complexity. In the absence of a clear picture, optimization is close to impossible.

Data-driven RGM practices focus on gaining clear visibility into overall trade spend across multiple markets and channels. By leveraging data integration practices, CPGs can create high-fidelity views of trade spending and promotion investments. And with this, they can augment the data with more context to understand the ROI on each trade spend decision, thereby unlocking new avenues for optimization to fuel sustained growth.

5. Channel management:

Over the last decade, consumer preferences have undergone profound shifts. E-commerce and social media are now gaining traction in categories where traditional channels previously fared better. As a result, channel decisions have now become more complex. CPGs are currently struggling with channel cannibalization, and in the absence of reliable insights, they end up wasting investments in non-performing channels.

These challenges indicate the need for a data-driven approach to channel management decisions. The first step in this direction is to identify all data sources that can help them identify consumer behavior across all channels and assess its impact on the top line and bottom line. This requires integrating and harmonizing data from disparate sources and enriching it with 3rd party datasets. CPG companies can leverage the data to track real-time channel performance and eliminate or optimize underperforming channels. It can also help identify the most effective and efficient channels for reaching target segments and unlocking resource reallocation opportunities.

6. Customer segmentation and targeting:

Consumer behavior can vary significantly across markets, genders, and age groups. This behavior can drive differentiated responses to macroeconomic forces (as evidenced by this analysis),^[4] and is a key determinant of the success of marketing and channel strategies. Outcomes can vary by each consumer within a segment, which has elicited approaches like hyper-personalization and micro-segmentation.

While these practices can significantly impact profitability and revenue outcomes, legacy RGM practices cannot account for these granular variations in consumer behavior. Data-driven RGM enables CPGs to exploit granular insights on customer groups and their behavior to define micro-segments and tailor marketing efforts to maximize success with each segment (and focus on the most profitable ones).

7. Total revenue management:

In legacy RGM strategies, RGM levers were typically seen as disparate mechanisms to boost the margins and profitability of the organization. Moreover, RGM strategies were static and varied across markets. Even when companies started leveraging data-driven approaches, they utilized point solutions, which lacked applicability in other markets.

This gap characterizes a key opportunity in modern RGM. CPGs can significantly improve RGM effectiveness by treating it as a single function that drives growth optimization across markets and sectors. This calls for RGM solutions that are customizable to multiple markets and a data organization that encompasses all RGM decisions with respect to established quality and consistency thresholds. By taking a total revenue management approach and making use of connected insights across pricing, promotions, assortment, and demand forecasting, CPGs can achieve effective gains across all growth avenues.

Summary

Apart from the mentioned revenue growth challenges, CPGs today face challenges in integrating data, establishing agile process flows, developing user-centric designs, deriving actionable data, etc. Modern-day RGM approaches are well suited to solve these problems and can boost revenue growth uniformly across a business in a way that traditional approaches were previously incapable of.

The business landscape is in a state of constant evolution. And CPGs that leverage data to drive their RGM decisions will outperform their competition over the coming decade and emerge as leaders in the new digital economy.

Want to know how you can take your business to the next level with modern-day RGM practices? Reach out to us here and get started today!

Bibliography

1. POI. “Promotion Optimization Institute.” POI RGM Whitepaper, 2022. https://poinstitute.com/wp-content/uploads/2022/05/RGM-Whitepaper_Final.pdf

2. Najmi, Adeel, and Pam Brown. “New Strategies for CPG Revenue Growth Management.” POI and Symphony CPGAI – Revenue Growth Management Whitepaper, 2023. https://poinstitute.com/wp-content/uploads/2014/05/POIandSymphonyCPGAI-Revenue-Growth-ManagementWhitepaper.pdf

3. Dinardo, Dominic. “Council Post: Revenue Growth Management: Three Ways to Get Real Time with the Industry Cloud.” Forbes, August 16, 2021. https://www.forbes.com/sites/forbesbusinesscouncil/2021/08/16/revenue-growth-management-three-ways-to-get-real-time-with-the-industry-cloud/?sh=53d9ca8c4748.

4. Socha, Kassi. “How Consumer Shopping Habits Are Changing in Response to Inflation.” Gartner, September 9, 2022. https://www.gartner.com/en/articles/how-consumer-shopping-habits-are-changing-in-response-to-inflation