Mikko Kärkkäinen, CEO, RELEX Solutions.
As commodity prices continue to fluctuate, the pressure on both retailers and consumer packaged goods (CPG) companies to maintain profitability has never been higher.
For example, the soaring cost of cocoa is affecting manufacturers and retailers alike, forcing them to reevaluate their pricing strategies for a variety of cocoa-based products. Meanwhile, consumers are growing increasingly price-sensitive. In this volatile environment, businesses must not only adjust their operations but also turn to technology to navigate these challenges effectively.
The key to success lies in leveraging advanced technology and data-driven strategies to understand core customers, apply real-time dynamic pricing strategies alongside promotions, optimize product portfolios and enhance supply chain agility.
Dynamic Pricing: Balancing Competitiveness And Profitability
Dynamic pricing, which adjusts prices based on factors like demand and market conditions, allows retailers to stay competitive while managing margin pressures. Through these capabilities, companies can respond effectively to market shifts, providing ongoing value to their customers—even when external pressures are mounting.
Today’s retail and CPG organizations are already tapping into data analytics to understand their core customers. AI-powered segmentation enables companies to quickly adjust pricing and product availability based on insights into local customer demand and purchasing patterns.
By analyzing big data from various sources, including customer loyalty programs and social media trends, companies can develop granular insights into what drives purchasing decisions. This becomes particularly important in a market where some consumers are struggling with inflation and financial constraints, while others are less affected and continue to spend freely.
Let’s explore three pricing and operational strategies that enable retailers and CPG companies to stay agile and respond to market shifts faster than the competition:
1. Leveraging AI To Balance Pricing And Promotions
Commodity price volatility requires a real-time, data-driven approach to pricing and promotions.
Retailers must identify which products are most price-sensitive and adjust pricing and promotional strategies accordingly. For instance, fluctuating dairy costs affect a range of staple products—milk, butter, cheese, whey—requiring careful price controls to keep essentials affordable. In contrast, less price-sensitive items like premium beauty products or specialty electronics may sustain higher margins, allowing more flexibility in their pricing.
AI-driven pricing optimization can allow businesses to adjust prices quickly, responding to real-time market conditions, competitor pricing and consumer demand much faster than traditional models. AI-powered promotions planning can also help by predicting the effectiveness of various strategies, ensuring they boost sales without harming long-term profitability or baseline sales.
2. Optimizing Margins Through Portfolio Management And AI
In the face of fluctuating commodity prices, optimizing product portfolios is essential to maintaining healthy margins. For example, major grocery chains often increase the availability of private-label products when commodity costs rise for staples like pasta, bread or dairy. By expanding private-label offerings, which tend to have higher margins and more controllable pricing, retailers can protect profitability and offer consumers affordable options in key categories.
By leveraging AI and predictive analytics, retailers and CPG companies can streamline their offerings, removing low-demand items that don’t contribute to profitability and increasing the share of high-margin and private-label products that offer better control over pricing and costs.
Machine learning models can analyze both historical and real-time data to predict shifts in consumer demand, enabling businesses to adjust production and supply chains with speed and accuracy.
3. Facilitating Data-Driven Supply Chain Agility
In a market driven by constant change, retailers and CPG companies must prioritize fast and responsive supply chain agility. Technologies like AI and digital twins are enabling businesses to act faster, helping them to simulate various “what-if” scenarios to optimize decision-making and implement adjustments as needed to stay ahead of competitors.
In response to volatile commodity prices, many retailers and CPG companies are now using digital twin technology to model their supply chains virtually. This approach not only helps them forecast the impact of rising costs on product availability and pinpoint areas for cost-saving adjustments but also allows them to react faster than the competition.
By refining replenishment processes and minimizing excess inventory, they’re better positioned to maintain stocked shelves and meet consumer demand.
AI-powered inventory management systems can further optimize stock levels, reducing the need for excessive safety stock and improving agility in response to changing consumer demand. Real-time data from Internet of Things (IoT) devices across supply chains and stores can provide immediate insights, helping retailers make quick decisions about replenishment, stock levels and promotions based on current conditions.
Getting Started With AI And Advanced Technologies While the advantages of leveraging AI and advanced technologies in retail and CPG are well-documented, businesses that understand and address the obstacles early are better positioned to integrate these tools effectively and drive meaningful outcomes.
Here are a few obstacles businesses face when adopting AI-driven dynamic pricing tools and strategies:
• Data Quality: Many companies struggle with fragmented data sources and silos. High-quality, integrated data enhances AI performance. Investing in data cleaning and integration can help to ensure better results.
• Security and Privacy: AI adoption raises data security concerns. Adhering to protection laws with encryption, access controls and audits ensures compliance and builds trust.
• Infrastructure Readiness: Legacy systems often lack scalability for real-time analytics. Moving to cloud platforms and scalable storage solutions prepares businesses for AI. Furthermore, dynamic pricing adjustments aren’t possible without electronic shelf labels. Companies need to invest in both hardware and installation when implementing electronic shelf labels.
• Skill Gaps: AI requires expertise in data science and machine learning, which is often limited. Workforce training and partnerships with academic institutions help bridge gaps.
• Change Management: Resistance to change hinders progress. Clear communication, employee involvement and pilot programs demonstrating AI benefits foster buy-in.
By proactively addressing these challenges, businesses can unlock the full potential of AI and advanced technologies. In doing so, they will better position themselves to overcome current market volatility and for sustainable success in an increasingly dynamic retail and CPG landscape.
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