Achieving Success in the CPG Industry Requires the Infusion of Technology at Every Stage of the Product Lifecycle

Author: Imran Saeed, Vice President, Global Head CPG, Course5 Intelligence

The CPG industry is a highly competitive space, with brands vying for consumer attention, shelf life and loyalty. It is also a complex one where several elements like product design, packaging, distribution and consumer experience play a part in making a product a success. However, factors like inflation, supply chain disruptions and changing consumer demand and preferences make it difficult for brands to stay profitable. Technology has helped to resolve these challenges, but it is usually implemented in silos. These silos are usually ‘data gaps’ along the product lifecycle that prevent a business from achieving its revenue projections. Integrating technology, enabled by AI/ML and Generative AI, into every aspect of the product lifecycle can help CPG brands plug these gaps and achieve the sales success envisioned for their products. 

Data-driven Decisions: Analytics plays a vital role at every stage of the product's lifecycle: design, manufacture, distribution, and customer support. It involves analyzing vast amounts of data to identify trends, measure performance, track consumer behavior, and forecast demand to make data-driven decisions. Coca-Cola, for instance, has applied machine learning (ML) algorithms for dynamic pricing. It adjusts prices based on real-time market conditions, consumer behavior, and competitor pricing to optimize revenue and stay competitive.

This shows that if a business has to deliver impact at scale, it must apply new AI-enabled technologies and rely on data analytics throughout a product’s life span.

Product Development

·         New product design: Product development and design teams can harness the power of Generative AI to generate concepts based on data gleaned from consumer preferences and market trends. This accelerates the innovation process and time-to-market, which increases the likelihood of success for new CPG products. It can also be used to implement sustainable practices. Kraft Heinz employs AI for product innovation. By analyzing market trends, consumer preferences, and competitor products, Kraft Heinz identifies opportunities for new product development, helping the company stay agile and respond to changing consumer demands.

·         A/B testing: In a highly dynamic market, altering a product, its packaging, and marketing campaigns can be a risky and costly affair. A/B testing, with AI and analytics, allows businesses to experiment and see what can yield more favorable results. For instance, it could be related to user experience, a change in messaging, packaging, pricing, and more. This allows businesses to make iterations faster to improve user engagement and conversions.

Manufacturing

·         Robotic Process Automation (RPA): Repetitive tasks are best handled by RPA to increase efficiency and allow humans to focus on more important tasks. When integrated with AI, it leads to an even more optimized RPA performance while handling routine tasks like order creation, invoice processing and inventory management.

·         Predictive maintenance: Generative AI is highly effective in analyzing sensor data and images to predict and alert teams of machine failure before it actually happens. Mondelez uses AI to monitor equipment data and ML models predict when machinery is likely to fail, resulting in minimized downtime and improved production efficiency.

·         Quality control: Product manufacturing can also benefit from the integration of Generative AI in manufacturing processes. Computer vision, coupled with AI algorithms, can detect defects and anomalies, and trigger alerts in real-time. Here, AI can be used to see responsible outcomes as well. Unilever, for instance, employs machine learning algorithms to assess suppliers' environmental and social performance to ensure that raw materials are sourced responsibly and align with the company’s sustainability goals.

Supply Chain

·         Demand forecasting: AI and analytics can predict demand based historical sales data, economic trends, weather patterns, etc., to prevent supply outages, reduce product waste, and improve customer experience. Procter & Gamble (P&G) is one such organization that uses AI to analyze historical sales data and external factors to predict consumer demand more accurately, leading to optimized inventory levels and reduced stockouts.

·         Supply chain optimization: Disruptions in the supply chain lead to serious losses for CPG businesses. Predictive and prescriptive analytics can even predict disruptions and offer advice on how to reduce lead times, avoid product damage and lower waste. Mars Inc., implemented machine learning algorithms to enhance visibility into its global supply chain—from manufacturing to distribution—for better product tracking, to reduce inefficiencies and ensure product quality.

·         Planogramming: AI can generate planograms based on large volumes of data that is related to sales performance, consumer behavior, and more. If it is real-time, then it allows businesses to adapt quickly—whether at a physical store or online—to increase product visibility and be more creative in the way they engage with consumers. 

·         Blockchain technology: The use of blockchain in the CPG industry can enhance transparency and traceability throughout the supply chain. With consumers becoming increasingly conscious of product origins and authenticity, blockchain ensures the integrity of the supply chain. When combined with AI, process automation can help further improve blockchain network performance.

Consumer Behavior & Experience

·         Market Basket Analysis: Identifying consumer buying patterns allows businesses to tweak their sales strategies. Insights gleaned from AI-enabled analytics allow sales and marketing teams to experiment with different tactics—like cross-selling, up-selling and product bundling—to increase conversion rates. Nestlé for one has personalized nutrition through data analysis and machine learning. It creates personalized nutrition plans for consumers based on their dietary preferences, health goals, and lifestyle, offering customized products and recommendations.

·         Consumer insights & feedback analysis: Amazon uses AI to analyze customer purchase history and behavior to provide personalized product recommendations on its platform. This improves the shopping experience and sales.

But more can be done. CPG companies can also leverage AI’s ability to analyze unstructured data like social media posts and customer reviews on e-commerce sites and consumer forums. Since feedback can come from a culturally diverse demographic, AI-powered natural language processing (NLP) allows brands to respond to their audience better. Insights gained from sentiment analysis allow brands to identify potential problems, initiate remedial steps, and tweak marketing strategies to improve customer experience.

CPG companies should not be afraid to explore new strategies and approaches. After all, technology is helping the industry reshape traditional business models, opening up new possibilities for experimentation and success. Since it is clear that a one-size-fits-all approach cannot be applied, organizations must assess options that will allow them to modernize processes in the most cost-effective and efficient way possible. That said, the onus also rests on more than one person. Stakeholders from every stage of the product lifecycle should be involved in the transformation. This will help eliminate data silos, reduce complexity and achieve faster go-to-market times, thereby allowing businesses to be long-term players in a competitive marketplace.