By Krishna Rangasayee, CEO and Founder, SiMa.ai
In the fast-paced world of retail, artificial intelligence (AI) and machine learning (ML) are transforming business operations. Infact, according to a market report, Global Artificial Intelligence in Retail Market was valued at USD 5.59 billion in 2022 and is projected to grow from USD 7.42 billion in 2023 to USD 71.23 billion by 2031, at a compound annual growth rate (CAGR) of 32.68% during the forecast period (2024-2031). These advanced technologies empower retail decision-makers with precise insights, enabling them to make meaningful actions that drive revenue generation more efficiently. One of the most impactful applications of AI and ML in retail is at the edge, where data processing occurs closer to the source of data generation rather than relying on centralized cloud systems. This approach not only ensures data security but also reduces latency, providing real-time insights for quicker decision-making.
In this article, we will explore six edge ML retail use cases that translate into significant cost savings, while maintaining data security and reducing latency. While loss prevention has traditionally been a key focus for AI in retail, there are numerous untapped opportunities that can fuel sales and help retailers cut costs, particularly as businesses scale to multiple stores equipped with digital signage. Here are some innovative use cases optimized for the smart retail edge:
Walk-in pattern & greeting support: Understanding customer walk-in patterns is crucial for optimizing store layouts and improving customer experiences. By deploying edge AI/ML applications, retailers can analyze foot traffic in real-time, identifying peak hours and high-traffic areas. This information enables store managers to allocate staff more effectively, ensuring that customers are greeted promptly and receive the assistance they need, enhancing overall satisfaction and increasing the likelihood of purchases
Unique Footfall Analysis: Accurate footfall analysis is essential for measuring store performance and planning marketing strategies. Edge AI/ML applications can differentiate between unique visitors and repeat customers, providing a clearer picture of customer behavior. This data helps retailers tailor their marketing efforts, personalize customer experiences, and ultimately drive higher conversion rates
Product “Test Drive” Analytics: Understanding how customers interact with products before making a purchase is invaluable for optimizing product placements and displays. Edge AI/ML applications can track customer interactions with products, such as how long they spend examining an item or which products they frequently pick up. This information allows retailers to make data-driven decisions about product placements, promotional strategies, and inventory management
Loss prevention: While loss prevention is a traditional use case for AI in retail, edge AI/ML applications take it to the next level. By monitoring store environments in real-time, these applications can quickly detect and alert staff to potential thefts, minimizing losses. Additionally, the real-time nature of edge processing ensures that data is analyzed instantly, allowing for immediate action when suspicious activities are detected
Personalised promotions: Edge AI/ML applications enable retailers to deliver personalized promotions to customers in real-time. By analyzing customer behavior and preferences, these applications can trigger targeted offers and discounts as customers browse the store. This personalized approach not only enhances the shopping experience but also increases the likelihood of conversions and boosts overall sales
Inventory management: Efficient inventory management is critical for reducing costs and ensuring that products are available when customers need them. Edge AI/ML applications can monitor inventory levels in real-time, predicting when items need to be restocked and preventing overstocking or stockouts. This optimization leads to significant cost savings and improves customer satisfaction by ensuring that popular products are always available
Deploying AI and ML applications at the edge in retail environments offers numerous benefits, including enhanced data security, reduced latency, and real-time insights that drive faster decision-making. By leveraging these technologies, retailers can achieve substantial cost savings, improve customer experiences, and stay ahead in an increasingly competitive market. The future of retail lies in harnessing the power of edge AI/ML to create smarter, more efficient, and more responsive stores that meet the evolving needs of customers.
