Forty Years of Revenue Growth Management — and Why the Last 10 Matter

Some consider Revenue Growth Management as a matter-of-fact tool for today’s modern Consumer Products enterprises. It has become such an essential tool, that it’s become integrated into every touch point CPG’s have with price optimization, trade promotion and spend effectiveness to portfolio and product mix optimization strategies.

Today, it’s difficult to find an enterprise that isn’t achieving measurable gains from RGM. Many report a 3-7% uplift in revenue through channel optimization, 10-20% reduction in inventory costs or 2-4% increase in top line growth with product assortment and mix optimization.

Yet there are many businesses that are lagging.

To understand how we got here, and why the last decade has been pivotal, we need to rewind the clock to the mid-1980’s – forty years ago.

Not many workers today were born into an analog world, but RGM was born at this time. Before digital tools were commonplace, CPGs relied on manual reports, spreadsheets on paper and mainframe computers for basic analytics. At best, the work was manual, siloed, reactive and driven more by experience than data.

In short, RGM started as a function of necessity, not strategy.

Jump forward to the 2010’s and we enter a transitory period where the decade marked a shift from reactive decision-making to diagnostic and predictive approaches, thanks to improved data availability, stronger statistical tools, and scalable ERP/CRM systems.

Leading large CPG’s like Coca-Cola, PepsiCo, P&G, Nestlé began building formal internal RGM teams with defined roles, structured methodologies, and a more strategic mindset. In summary the 2010’s looked like this:

Many of the above-mentioned tools remain widely used today such as Teradata, Oracle, SAP, Blue Yonder, SaS, Tableau, among others. But there is a key characteristic they all share: they are not systems, but standalone tools each designed to solve a specific problem or execute a narrow set of tasks.

What’s Missing in the current RGM data ecosystem?

The ability to deliver instant insights and decision acceleration across the entire data estate with generative and AI tools that cut the time for research and testing from weeks to days letting marketers act while the window is still open.

• Hyper-personalization that drives measurable growth with next best offers with less marketing effort;
• Early signal detection & early proactive innovation driven by LLM mining of social, search and creator platforms;
• Efficiency & cost optimization at enterprise scale because AI-powered insights engines replace repetitive analysis.

So why do the last ten years matter?

Because now we are in the age where the transition from EDW to data lake, to Lakehouse to the Databricks Data Intelligence Platform has come of age. Databricks’ Data Intelligence platform removes the challenges of slow and siloed data, inaccurate insights, and fragmented views of the market.

With Databricks, insights can be generated in real-time, or as fast as your business needs it — with all data: images, video, POS, social media, and channel, etc. unified in a single location and format. It breaks down silos by integrating legacy data from on-prem EDW’s but also sources like Synapse, Snowflake, BiqQuery as examples. The result is faster, more accurate decisions, and a true end-to-end view of the customer, the market and business.

How has this transformation impacted RGM?

Channel optimization is the process of aligning pricing, promotion and product strategies across the physical and digital channels to maximize customer reach, revenue and profitability. With legacy systems from the 2010’s, this was nearly impossible as they were not designed to handle the data volume nor the unstructured data now being gleaned across the digital channels. To a channel manager, sales director or ecommerce lead, the Data Intelligence Platform solves a multitude of challenges that legacy systems do not:

• Inconsistent pricing across channels
• Channel conflict between DTC and retail
• Limited visibility into channel-level performance
• Misalignment between online and in-store customer experiences
• Unoptimized assortment strategies by channel
• Fragmented or uncoordinated promotional planning

A modern Data Intelligence Platform addresses these gaps by enabling:

• Cross-channel performance analytics
• Pricing harmonization
• Channel-specific assortment optimization

The result is a unified, agile approach to channel strategy that adapts in real time to consumer behavior and market dynamics.

How a Data Intelligence Platform helps enable use cases

Key components of the Data Intelligence Platform that enable these use cases include:

• Open-source Delta Lake which unifies data in a central location eliminating the needs to copy or move data across systems
• Unity Catalog for enterprise level governance and lineage
• ML Flow for machine learning elasticity models
• Autoloader and Lakeflow for real-time feeds
• AI/BI Genie turning your static dashboards into GenAI powered insight machines.

Forecasting and demand planning is a major cornerstone of RGM that enhances accuracy of demand forecasts and inventory alignment using real-time data and predictive models by improving forecast accuracy, inventory turnover, stockout rate, and service level performance.

By turning to the Data Intelligence Platform, CPG’s can realize 10–20% reduction in inventory costs and 5–15% improvement in forecast accuracy by implementing use cases that incorporate external signals, machine learning forecasts, and real-time alerts to proactively balance supply and demand.

Demand planners, supply chain managers, and sales directors now do not have to cope with the common challenges that are not addressed with legacy systems: low forecast accuracy, stockouts and overstocking, reactive rather than proactive inventory management, lack of integrated demand signals, siloed sales and supply planning, or high inventory holding costs. Instead, they gain an integrated, intelligent view of demand — and the tools to act on it in real time.

How the Databricks Data Intelligence Platform helps!

The Databricks Data Intelligence Platform enables forecasting and demand planning use cases through a unified set of advanced capabilities including; Databricks Feature Store for demand drivers, AutoML for forecast modeling, Delta Live Tables for real-time data ingestion & transformation, Unity Catalog for linage, governance and traceability of ML and GenAI models, and the Lakehouse for historical and streaming data transformation and integration.

Category managers, brand managers, and revenue growth strategists struggle with product assortment & mix optimization challenges that legacy systems merely tolerate. These include SKU proliferation and complexity, under-performing products consuming shelf space, low velocity in key categories, or misaligned pack-price strategies - all stemming from the limitations of the older systems.

Modern use cases turn to Databricks SQL for SKU-level dashboards, Delta Sharing for inclusion of syndicated data, MLflow for mix optimization models, and Lakehouse for integrating POS, + loyalty, basket data that leverages POS and panel data to rationalize SKUs, enhance pack-price architecture, and prioritize high-margin items in their quest to optimize the product portfolio by aligning assortment and pack-price architecture with customer needs and profitability targets.

Together, these tools help optimize product portfolios by aligning assortment and pricing strategies with customer needs and profitability goals.

Polestar Analytics Expertise + Databricks Data Intelligence Platform – What is means?

Polestar Analytics has adopted the Databricks Data Intelligence platform to power three innovative solution offerings for the market:

• Profit Pulse: An integrated suite of ML and visualization solutions addressing pricing, promotion and trade spend optimization. The solution has seen wide adoption across a CPG landscape
• Data Nexus: A data engineering tool that speeds delivery of composable data models which are the foundation of all consumption layers – visualization, machine, and Generative AI and agents
• AgentHood: A data science solution leveraging Agent Bricks allowing drag and drop creation of agents, an agent marketplace and an agent orchestration tool.

Polestar Analytics has delivered strong customer value as demonstrated by:

What will the future hold? Here are our predictions:

• RGM will become truly real-time with the incorporation of OLTP data (Lake Base)
• Granular customer segmentation will be unified across channels (television, phones, search, and beyond)
• Agents will assist and then perform routine tasks such as grocery shopping and will – fundamentally transform segmentation and selection processes. Agents have the potential to remove the emotional elements from everyday purchasing decisions, streamlining the customer experience.

Follow Polestar Analytics for more thought leadership or at polestarllp.com