Expand or Perish: CPG Companies Focus on Securing Their Data Analytics to Foster Sustainable Growth

After years of higher interest rates and inflation, consumers have made it clear that their tolerance for price increases is at an end. CPG companies understand that their future relevancy will be dependent on developing newer emerging markets, and that it’s time to shift from a survival mindset into growth mode. It’s an exciting moment, but not without risks. 

To win not just in value but volume, CPG companies must prioritize operational efficiency, embrace innovation, and develop long-term resilience.

Any transformative journey begins with a plan, and for growth-minded CPG companies that means an honest assessment of current capabilities. Do we have the right support to manage a global expansion? How will we attract new customers? Is our supply chain prepared? Answers to these questions require close examination of company data, but it’s important to remember that data isn’t just an endpoint. 

Knowledge Is Power

The ancient Greeks relied upon a fighting style known as a phalanx, formations of warriors with long spears closely bunched together behind interlocking shields to create a protective wall. The strength of a phalanx was its cohesion — if that outer defensive barrier broke down, threats could get in and compromise the unit. 

In a similar way, CPG brands must tighten up around the edges of their own organization and use their data to spot structural inefficiencies before moving into new territories, thereby increasing their likelihood of success.

Here are three examples of how building a more resilient data analytics infrastructure can be crucial for managing complex global operations, optimizing supply chains, and tailoring campaigns to diverse markets:

1. Successful Innovation Prediction

Post-pandemic consumer behavior is tough to predict, and expanding into unfamiliar markets only adds to the stress and uncertainty facing companies as they try to meet short- and long-term demand. CPG leaders have long used demand forecasting to manually study historical sales data and try to anticipate fluctuations, but there are limits to what a human can do. These techniques mostly rely on historical experiences and are typically lagging indicators.

Now, thanks to recent advancements in artificial intelligence and machine learning, CPG companies can crunch huge amounts of data to gain better insights into changing consumer preferences and create products that have a higher probability of success.

In our experience, applying AI-driven NPD prediction can reduce errors by 50%-50% and translate into a reduction in lost sales and product unavailability of up to 35%. Through real-time data analysis integrated from multiple sources like point-of-sale systems, social media, and macro-economic indicators, CPG companies can quickly adjust to changing demands and train their models to spot future trends. 

A good example of this comes from PepsiCo, which uses an AI-driven tool called Tastewise to uncover what people are eating and why. By following the phygital consumer on social sites to their favorite specialty e-commerce retailers like Sephora, we look at a wide variety of sources (60-plus million consumer touchpoints), to tap into the pulse of the local populace and then align their logistics to accommodate.

2. Optimized Inventory Management

Inventory management is complementary to demand forecasting, and uses predictive analytics to improve data sharing between stakeholders (i.e. suppliers, distributors, and retailers). CPG companies are familiar with the balancing act required of moving goods across the world — too much product leads to waste and lost revenue, while understocking can result in shortages and upset customers. But with powerful new AI technology, businesses are learning to accurately analyze consumer demand for specific products. 

Further evidence of the importance of using data to better inform supply chain dynamics comes from the CPG giant Procter & Gamble. In late 2022, P&G announced its global Supply Chain 3.0 initiative, which aims to boost its supply chain resilience through automation and data analytics. The venture has been a success so far, with the company recently noting that Supply Chain 3.0 has enabled them to work more collaboratively with retailers on the totality of the supply chain instead of piecemeal optimization. 

P&G also noted that it’s using data and machine learning algorithms to “optimize truck scheduling to minimize driver idle time, as well as leveraging AI tools to optimize fill rates and for dynamic routing and sourcing optimization.”