Transforming Retail and CPG with Generative AI Solutions

With a high level of competition, lower R&D budgets compared to other industries, and the constant need to produce new goods, the retail and consumer packaged goods (CPG) sector is challenging to innovate within. However, the rapid evolution of generative AI is impacting all industries, including retail and CPG, demonstrating that early adoption of innovation is crucial for gaining a competitive edge. This is supported by recent statistics showing that over 50% of retailers plan to implement AI and ML within the next year, primarily to enhance customer experiences both physically and virtually. In this article, we will explore the benefits of generative AI in consumer packaged goods and retail, examine generative AI applications, and provide real-life examples of how these technologies are transforming these sectors.

What are the benefits of generative AI in retail and CPG?

Generative AI offers numerous benefits to retail and CPG. Below are the key areas of positive impact.

Enhanced customer experience

Generative AI enhances the customer experience in three key ways, by:

Personalizing shopping experiences
Improving product recommendations
Providing interactive AI-driven customer services

Optimized supply chain management and operational efficiency

By implementing predictive analytics, generative AI in supply chain management helps with accurate demand predictions and streamlines operations, thereby boosting operational efficiency and improving inventory management.

Increased sales

Organizations that leverage AI solutions often gain a market advantage, leading to increased sales figures and a competitive edge.

6 use cases of generative AI solutions for retail and CPG

The real-life examples below illustrate how generative AI is transforming retail and CPG in multiple ways.

Use case #1: Sephora

Sephora strived to enhance customer engagement and satisfaction by offering a more interactive and personalized shopping experience. Solution The Sephora gen AI-powered Virtual Artist app uses facial recognition technology to allow customers to virtually try on an extensive range of makeup products. The AI analyzes the user’s facial features and recommends suitable products. Result The innovation has significantly improved the customer experience by making it more interactive, leading to higher engagement and increased sales.

Use case #2: Google

Nearly 60% percent of online consumers feel dissatisfied with an item they shopped for online because it looked different than expected. Google aimed to enhance online shopping experiences by providing customers with a realistic preview of clothing items so that users can see whether a piece is right for them before they buy it. Solution Google implemented a virtual try-on feature for women's tops from popular brands, powered by generative AI. This technology analyzes user photos and overlays the chosen clothing items onto the image, giving a realistic preview. Result This feature reduces the need for physical trials, enhances customer satisfaction, and decreases return rates by helping customers make better-informed purchase decisions.

Use case #3: Amazon

To maintain its competitive edge, Amazon aims for continuous improvement of product recommendations and customer interactions. Solution Amazon integrated generative AI into its recommendation engine to analyze customer preferences, past purchases, and browsing patterns in order to offer tailored product suggestions. Additionally, Amazon uses AI-powered chatbots to offer instant customer support and resolve inquiries efficiently. Result The AI solutions have led to higher customer satisfaction, increased repeat purchases, and strengthened customer loyalty.

Use case #4: Walmart

Walmart faced challenges in efficiently managing its supply chain and inventory to meet customer demand. Solution The company implemented a generative AI-powered chatbot developed by Pactum for vendor negotiations. This AI-driven retail software uses historical trends, competitor pricing, and material costs to negotiate terms with suppliers for smaller contracts. Result The AI-driven approach has resulted in a 68% supplier deal closure rate since 2021, preferred by 75% of suppliers over human negotiations, and delivered an average of 3% cost savings on negotiated contracts. This innovation has enhanced operational efficiency and reduced procurement costs.

Use case #5: Carrefour

Carrefour aimed to enhance customer engagement and deliver customized shopping support. Solution Carrefour introduced a ChatGPT-based intelligent assistant called Hopla. This chatbot provides instant product recommendations tailored to budgets, menu ideas, and dietary preferences. It acts as an intelligent concierge, providing prompt support on products, services, and store information. Internally, Carrefour uses generative AI for things such as drafting invitations and analyzing quotes. Result Hopla has made the shopping journey more engaging and personalized, significantly enhancing the customer experience.

Use case #6: Procter & Gamble

Procter & Gamble aimed to restore productivity levels to pre-COVID standards and identify significant cost-saving opportunities within their supply chain. Solution P&G launched its global Supply 3.0 initiative, focusing on building supply chain resilience through automation and data analytics. This initiative aims to increase visibility into savings opportunities and foster collaboration with retailers across the entire supply chain. Key implementations included:

Optimized truck scheduling: using data and machine learning algorithms to minimize driver idle time.
Using AI for optimizing fill rates, dynamic routing, and sourcing optimization.

Additionally, P&G is advancing its AI capabilities through its “AI Factory,” which has improved data scientists' speed and efficiency. The AI technology is also enhancing product innovation, such as digital scent creation for better product development and design processes. Result The P&G efforts are expected to result in $200 million to $300 million in savings.

The future of generative AI in retail and CPG

The implementation of generative AI and ML is spreading quickly through the industry. Below is a diagram that shows the most popular areas for AI implementation within retail and CPG. A table titled "Use Case" and "Percentage" lists AI use cases and their adoption rates, from personalized recommendations (66%) to real-time metaverse translation (12%), including generative AI in retail.

A table titled "Use Case" and "Percentage" lists AI use cases and their adoption rates, from personalized recommendations (66%) to real-time metaverse translation (12%), including generative AI in retail.

Source: Nvidia State of AI in Retail and CPG: 2024 Trends The recent report by Nvidia, which surveyed over 700 respondents from retail and CPG, revealed four key trends driving the surge of AI in this industry:

1. AI reduces operational costs

AI has proven to increase annual revenue and reduce operating costs for a significant number of retailers. By enhancing operational efficiencies, AI helps retailers meet evolving customer preferences, address labor shortages, and drive sustainability efforts.

2. Generative AI transforms customer experience

Generative AI is being used for multimodal shopping advisors, adaptive advertising, personalized product recommendations, product tagging, cataloging, and automated customer service. Retailers view generative AI as a transformative tool for revolutionizing customer engagement and optimizing marketing strategies.

3. Increasing investments are being made in AI

Retailers are significantly increasing their investments in AI infrastructure, with more than 60% planning to enhance their AI funding over the next 1.5 years. This increase reflects the industry's recognition of AI’s potential to enhance efficiency, reduce costs, and drive growth.

4. Focus is shifting to emerging technologies

In order to enhance consumer engagement and efficiency, retailers are considering metaverse. Additionally, there is a focus on leveraging AI to provide convenience and personalized experiences in brick-and-mortar stores, while also addressing challenges related to technology and AI talent.

To wrap it up

In this article, we’ve explored the benefits of generative AI in consumer packaged goods and retail, examined generative AI applications, and provided real-life examples of how these technologies are transforming the industry. The power of generative AI is here, and with new and emerging technologies streamlining operations and enhancing consumer experiences, an innovative revolution is inevitable. At Kanda Software, we look beyond the hype of AI and ML to translate these technologies into quantifiable and customized solutions, particularly in the retail and CPG sectors, where sales figures are directly linked to customer experience. Improve the operating efficiency of your company by uncovering hidden data value, consumer intelligence, and seamless consumer journey solutions with the help of Kanda’s team of experts. Contact us today.

Transforming Retail and Consumer Packaged Goods (CPG) with Generative AI