Optimization isn’t just a vendor buzzword anymore. It’s a fully definable and measurable outcome that cannot be achieved with antiquated techniques leveraged by one-size-fits-all AI systems.
Optimizing revenue growth is a top priority across the CPG sector today. Uncertainty driven by global economic headwinds, persistent inflation, supply chain challenges, and shifting buyer behaviors has intensified the importance of understanding how to systematically decode and navigate evolving conditions to drive increased revenue and profit.
For CPG organizations, foundational to that critical need is the ability to holistically optimize their top drivers of revenue growth management (RGM) by aligning pricing, promotions, media mix, and consumer product packaging with changeable market conditions. This has never been more complex amid the ripple effects of evolving consumer preferences, geopolitical tensions, climate change, and global population shifts – a primary reason why more than 75% of CPG manufacturers are struggling to manage total enterprise modern trade spend, and 70% of CPG executives are more stressed today than five years ago.
With complexity a constant, many organizations are prioritizing digitalized revenue growth optimization as a mechanism for weathering the storm. In the Promotion Optimization Institute’s 2024 State of the Industry Report, 80% of respondents said they were investing in digital solutions or analytical capabilities to support new revenue growth management (RGM) processes and dive deeper into optimized promotion, pricing, and pack growth analysis. The POI report also found 54% planned to adopt new trade promotion management solutions and 31% would embark on integrating automated pricing capabilities. The problem is many of these systems are marketed as “AI-enabled optimization solutions” that supposedly serve as the perfect tool for alleviating inflationary pressures and amplifying revenue.
However, in reality, that simply isn’t the case.
As artificial intelligence becomes increasingly integrated into the technology and business process fabrics of the modern enterprise, CPG leaders are learning that not all AIs can deliver true revenue growth optimization at scale. Optimization isn’t just a vendor buzzword anymore. It’s a fully definable and measurable outcome that cannot be achieved with antiquated techniques leveraged by one-size-fits-all AI systems.
In turn, it’s critical for organizations to understand the distinct capabilities of the AI revenue growth optimization tools they are adopting. Separating the wheat from the chaff in the world of advanced AI will make or break your ability to drive sustainable revenue, weather market volatility, and outpace industry competitors.
It’s All About Your Toolbox
Ensuring you have the right AI tools in your toolbox is worth its weight in gold when it comes to revenue growth optimization. For example, say you wanted to cut a block of steel. It could theoretically be accomplished with a hacksaw, except that would take years to successfully cut all the way through. Meanwhile an acetylene torch would slice through it in seconds.
The same goes for AI-enabled technologies. Most forms of AI utilized in CPG revenue growth optimization systems today cannot account for real-world market complexity. They leverage old linear regression techniques to solve a problem that is non-linear in nature, relying on traditional AI models that optimize one, two, three or four static constraints instead of the 28 or 30 constraints that CPG brands navigate daily. This leads to fundamental bottlenecks that hinder operational performance and ROI.
Generative AI (GenAI) is another example of this misalignment. CPG value chain use cases for GenAI applications do exist today, but revenue growth optimization isn’t one of them. This is because GenAI models rely on search engine-based techniques that are incapable of discerning the “garbage in from garbage out” problem. It’s like casting a massive fishing net into the ocean in search of a swordfish. You may catch one, but you’ll also reel in sharks, whales, tuna, plastic bottles, and a myriad of other things that are irrelevant to your desired outcome.
Facilitating a Math Problem
It’s important to remember that true revenue growth optimization is a high-dimensional math problem at its core. AI solutions that leverage glass-box machine learning are required for incorporating all the constraints and variables that enable optimization to deliver value for both the CPG manufacturer and retailer simultaneously. It ensures the system is designed to fundamentally understand the environment in which an organization operates and then autonomously implement and adapt decision-making strategies based on evolving conditions. Then, it can optimize key levers of revenue growth with prescribed pricing, trade promotion, media mix, and assortment recommendations aligned to consumer demand under conditions that are stressing the normal everyday price.
This accounts for navigating market uncertainty such as elongated supply shortages from an escalating geopolitical conflict or unexpected price hikes from a climate-related event. If a drought along the Panama Canal increases the cost of raw materials, the system can help determine a new optimal pricing structure that 1) accommodates for increased production costs while maintaining margins and 2) incentivizes consumers to select your brand over industry competitors.
Measuring the Impact
Determining the ROI impact of revenue growth optimization tools requires a comprehensive and calculated approach. First, focus on analyzing core KPIs such as net incremental increases in sales, profits, retail shelf dollars, and market penetration that is generated from your trade promotion spend. Performance across these four pillars will indicate the impact of your implementation strategy and identify areas of needed improvement.
The second major category is trade effectiveness ratio. For every dollar spent in trade, what average return does it produce? This is crucial for scaling the revenue growth optimization tools over time. Executing both facets in unison will position organizations to successfully navigate external volatility and capture market share over industry peers. A strong ROI isn’t just about numbers – it’s also about gaining a competitive edge in your segment.
Optimizing revenue across the CPG landscape is undeniably complex. While digitalization offers promise for simplifying it, enterprise leaders must delve deeper to separate effective tools from snake-oil solutions. Knowledge is power, and will ultimately elevate your brand above the pack.
