In the world of Revenue Growth Management (RGM), one of the greatest challenges companies face is finding the “perfect price.” This concept goes beyond merely setting a number—it involves balancing a series of factors to maximize both sales volume and profitability, without losing market share. But in an economic environment characterized by inflation, post-pandemic uncertainty, and abrupt changes in consumer preferences, the question becomes even more complicated: Is it really possible to predict price elasticity accurately?
At Wise Athena, we have delved deeply into this topic, consulting RGM experts like Christian Candiani [watch the full interview here] who offered his perspective on the challenge. According to Candiani, “In such a dynamic market, traditional elasticity formulas are no longer sufficient. Today, companies need to combine historical elasticity analysis with real-time data and use advanced predictive models to adjust their pricing strategies more effectively.”
What is Price Elasticity and Why is It So Hard to Predict?
Price elasticity measures how consumers respond to changes in a product’s price. Theoretically, an increase in price should reduce demand, while a decrease should increase it. However, in reality, consumers’ reactions to price fluctuations are much more complex. Price elasticity is influenced by factors such as competition, the availability of alternatives (like private label brands), and the perceived value consumers attribute to the product.
In today’s landscape, where consumers are more informed and price-sensitive, predicting how they will react to a price change has become increasingly challenging. Allan Gamboa, in his article “Pricing Estratégico: Tras la captura de valor con solidez competitiva,” explains that using dynamic elasticity is crucial in pricing decisions, as it accounts for constant changes in market conditions, competitors, and consumer behavior.
The Rise of AI and Machine Learning: The Ultimate Solution?
The advancement of Artificial Intelligence (AI) and Machine Learning has opened new opportunities for companies to analyze large volumes of data and generate much more accurate predictive models. Wise Athena uses these tools to help companies adjust their prices more efficiently, based on real-time data and historical consumer behavior. By applying AI, companies can capture more value by dynamically and precisely adjusting their prices.
The report by Boston Consulting Group (BCG), titled “Revenue Growth Management in the Age of AI”, published in 2023, supports this perspective, noting that using AI to predict price elasticity can improve margins by up to 15% in sectors like Consumer Packaged Goods (CPG). However, BCG also warns that relying solely on technology is not a magical solution. “While AI excels at processing large volumes of data, companies also need to understand market context and consumer emotions to make well-rounded decisions.”
The Challenge of Cross Elasticity
Beyond price elasticity, companies must also consider cross elasticity, which measures how a price change in one product affects demand for other products in the same portfolio. AIS Soto, an RGM expert, mentions in his interview with Wise Athena that “the impact of a price adjustment on a premium brand can trigger a domino effect on sales of more affordable brands within the same portfolio. This behavior not only affects sales but also the perceived value of the entire product lineup.”
This level of complexity in pricing decisions underscores the need for a more holistic and dynamic approach, considering not only the immediate effects of a price adjustment but also its long-term impact on competitiveness and value capture.
Avoiding Common Pricing Strategy Mistakes
Despite advanced technology, many companies still make mistakes when adjusting their pricing strategies. Vicente García, another RGM specialist, mentions that one of the most common errors is focusing solely on short-term objectives without considering long-term repercussions. “A price increase may yield immediate results, but if dynamic elasticity or competitors’ potential reactions are not taken into account, it can end up eroding consumer loyalty,” warns García.
Another common mistake is over-reliance on discounts and promotions, which may boost short-term volume but often come at the expense of value capture in the long run.
Final Thought: Balancing Technology and Human Insight
The solution isn’t simply adopting cutting-edge technology or relying solely on historical data. The most effective approach combines both: leveraging the predictive capabilities of AI while maintaining a deep understanding of market behavior and consumer preferences.
As Christian Candiani concludes, “Price elasticity is both a science and an art. The companies that balance these two dimensions are better equipped to maintain a strong competitive position and maximize their profitability.”
If you’re ready to dive deeper into pricing strategy, contact us at info@wiseathena.com. Discover how Athena’s AI-powered models bring precision to revenue growth management, helping you predict elasticity and drive value even in uncertain times. Our team of experts is here to tailor strategies that align with your unique goals.
Stay ahead of the competition and unlock the full potential of your CPG business. Let’s transform your approach to pricing and profitability together!
