By Stephen DeAngelis
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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 behavior 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, inflation, 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.
Many systems are marketed as “AI-enabled optimization solutions” that can effectively alleviate inflationary pressures and amplify revenue. However, in reality, that simply isn’t the case. As advanced analytics enabled by sophisticated mathematics and AI has become increasingly integrated into enterprises’ technology and business processes, it’s clear that not all mathematical techniques and AIs can deliver actual revenue growth optimization at scale. CPG leaders are learning that their definition of optimization is outdated and inaccurate. The industry has historically defined “optimization” as the use of yesterday’s regression modeling and business scenario simulations. They are realizing that these older techniques are merely forecasting techniques that do not optimize anything. They are also learning that Generative AI and neural nets do not perform optimization, but can be valuable techniques in assisting other components of an organization’s digital transformation journey.
The analytics landscape is rapidly changing. Advanced analytics companies need to help CPG partners build understanding and maturity on the use and specific application of these technologies within their operating models. Optimization isn’t just a buzzword anymore. It’s fully definable and its outcomes are determinable and measurable by balancing the constraints of both the CPG manufacturer and retailer simultaneously. This degree of constraint-based optimization and its tangible benefits cannot be achieved with antiquated techniques and infeasible AI systems.
In turn, it’s critical for organizations to understand the distinct capabilities of the statistical mathematics and AI-enabled revenue growth optimization tools they are adopting. Separating the wheat from the chaff in the world of advanced analytics and AI will improve your ability to drive sustainable revenue, weather market volatility, and outpace industry competitors.
It’s All About Your Toolbox
Ensuring you have the right sophisticated mathematics and 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 statistical models that optimize one, two, three or four static constraints instead of the two to three dozen constraints that reflect the real-world considerations that CPG brands navigate daily. This leads to fundamental analytic underperformance that hinders effective revenue growth recommendation generation and operational performance and ROI for both the CPG manufacturer and their retail partners.
Generative AI (GenAI) is another example of this misalignment. The CPG value chain has valuable use cases for GenAI applications, 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 and neural nets machine learning that simply do not perform optimization.
Optimization isn’t just a buzzword anymore. It’s a fully definable and measurable outcome that cannot be achieved with antiquated techniques and infeasible AI systems.
Facilitating a Math Problem
It's important to remember that true revenue growth optimization is a constraint-based, high-dimensional math problem at its core. Sophisticated mathematics and 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. This ensures the system is designed to fundamentally understand the environment in which an organization operates and perform true optimization and generate trade promotional calendars that drive value for the manufacturer and the retailer. Then, the next step is to optimize the other key levers of revenue growth management with everyday pricing, trade promotion, media mix, and assortment to produce holistic recommendations aligned with consumer demand under conditions that are stressing the normal everyday price.
This fit-for-purpose approach 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 assists in increasing the cost of raw materials, the system can help determine a new optimal pricing structure that 1) accommodates consumer packaging for increased production costs while maintaining margins, and 2) incentivizes consumers to select your brand over industry competitors via effective promotional techniques.
Measuring the Impact: Post-Event Effectiveness
Determining the ROI impact of revenue growth optimization tools requires a comprehensive and calculated approach. First, focus on post-event analysis of 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 have a strong grasp of the sophisticated mathematics and AI tools they are leveraging. Knowledge is power, and it will ultimately elevate your brand and company valuation above the pack.
