AWS for Industries
Transforming Stores Through Computer Vision: A Business Leader’s Guide
With physical stores still accounting for 80 percent of sales, the pressure to optimize in-store operations has never been more intense. The rise of ecommerce hasn’t just changed shopping habits—it’s created an urgent need for physical stores to deliver smarter, more efficient store experiences while reducing their technology footprint. Physical store companies commonly face the desire to enhance operations and shopper experience while maintaining disciplined capital investment. In our conversations with CTOs and COOs across retail, quick-service restaurants (QSR), and consumer goods industries, three consistent themes have emerged: a resistance to additional capital expenditure, a desire to minimize physical store engineering, and an interest in leveraging Computer Vision technology to transform operations. These leaders seek comprehensive visibility into shopper behavior and store operations across their physical locations, but often struggle with how to begin this journey using their existing infrastructure.
The business case for Computer Vision
Computer Vision technology is transforming the consumer industries landscape by turning existing security camera infrastructure into powerful analytics tools that drive measurable business value. By leveraging advanced Computer Vision capabilities, organizations can unlock critical insights across multiple dimensions – from optimizing store layouts based on shopper behavior to enabling data-driven workforce management, reducing wait times, and measuring in-store promotional effectiveness. This comprehensive approach helps stores enhance both operational efficiency and the bottom line through strategic deployment of existing assets.
The real-world impact of these implementations has been substantial and measurable. Stores using Computer Vision solutions have achieved impressive results, including 15-20 percent reductions in checkout wait times and up to 30 percent improvements in staff utilization through smarter task assignment and workforce management. By understanding shopper traffic patterns and optimizing product placement strategies, stores have significantly increased high-margin product sales and enhanced overall revenue performance. These concrete outcomes demonstrate that Computer Vision isn’t merely a technological upgrade—it’s a strategic investment that delivers meaningful returns across operations, shopper experience, and profitability.
Key use cases driving business value
Computer Vision enables three transformative capabilities that deliver immediate value to physical store operations. First, Shopper Journey Analytics provides unique insight into how customers interact with your space. By generating detailed heat maps of store traffic patterns, stores can optimize layout designs to maximize engagement and sales. This data-driven approach also enables premium space monetization through strategic product placement and helps inform merchandising decisions based on actual customer behavior rather than intuition.
Queue Management Intelligence represents the second key capability, addressing a persistent challenge for stores. Real-time monitoring of checkout areas and service points provides immediate visibility into wait times, while automated alerts notify staff of developing bottlenecks before they impact customer satisfaction. The system’s predictive staffing recommendations help managers proactively deploy resources where they’re needed most, ensuring optimal service levels during peak periods.
The third capability, Workforce Optimization, transforms how stores manage their most valuable resource—their employees. By analyzing customer traffic patterns, the system enables intelligent task assignment that aligns staff deployment with actual customer needs. Dynamic staffing models adjust to real-time conditions, while comprehensive performance metrics provide valuable coaching opportunities for team development. This data-driven approach to workforce management typically yields 20-30 percent improvements in staff utilization while enhancing both employee engagement and customer service.
These three capabilities work together to create a more efficient, responsive, and profitable store operations. By leveraging existing camera infrastructure, consumer industries can implement these solutions with minimal disruption while gaining maximum operational insight. Overall, physical locations can use Computer Vision technologies to support both “Reactive” (cloud-based video analytics) and “Proactive” (edge-based real-time detection) use cases. Figure 1 illustrates these use case scenarios.
Figure 1 – Reactive and Proactive Use Cases for Computer Vision in Consumer Industries
Strategic implementation considerations
For business leaders embarking on their Computer Vision journey, success begins with a pragmatic, structured approach. Our experience working with customers has shown that careful planning across three key phases yields the most successful outcomes.
The assessment phase begins with a comprehensive audit of your existing camera infrastructure. This evaluation helps identify both the capabilities and limitations of your current system while highlighting opportunities for enhancement. During this phase, we work closely with your team to identify the use cases that will deliver the highest business impact for your organization. Whether it’s optimizing store layouts, improving queue management, or enhancing security measures, defining clear success metrics ensures alignment with your strategic objectives from day one.
When it comes to implementation, we’ve found that one size doesn’t fit all. For new locations, a greenfield deployment allows for optimal system design from the ground up, incorporating the latest CV technologies and best practices. Existing locations often benefit from a brownfield enhancement approach, where strategic upgrades to current infrastructure can deliver significant value without wholesale replacement. Many of our most successful customers opt for a hybrid approach, implementing full-scale solutions in new locations while gradually upgrading existing stores based on business priorities, ROI potential, and existing hardware refresh cycles.
The investment strategy for CV implementation should be both practical and scalable. A store-by-store deployment model allows organizations to learn from each implementation, refining their approach while managing capital expenditure effectively. This measured rollout ensures that the architecture can scale efficiently across your network while maintaining consistent performance. By prioritizing deployments based on expected ROI, organizations can build momentum through early wins and create a self-funding model for continued expansion.
The path forward – deploying Computer Vision on AWS
AWS understands that every organization’s journey to CV implementation is unique. That’s why we’ve developed flexible deployment options that align seamlessly with varying business objectives and existing infrastructure. Our approach emphasizes practical considerations that matter to business leaders: minimizing upfront capital requirements through innovative financing options, maximizing the value of existing investments rather than requiring wholesale replacement, and ensuring solutions can scale efficiently as your needs evolve. Most importantly, we focus on accelerating time-to-value, helping you realize benefits quickly while building toward your longer-term vision.
- To accelerate your Computer Vision journey, AWS offers a robust portfolio of Partners and Professional Services support. Our AWS Partners specialize in store-specific Computer Vision solutions, bringing pre-built applications and extensive industry experience to help fast-track deployment and ROI. These partners offer everything from edge devices and camera integration to full-stack analytics platforms that seamlessly integrate with AWS services. Additionally, AWS Professional Services works alongside customers and partners to provide deep technical expertise, architectural guidance, and implementation support. This combination of Partner solutions, along with Professional Services expertise, helps customers navigate common challenges like legacy infrastructure integration, data security compliance, and phased rollout strategies. Whether you’re starting with a pilot program or planning a full-scale deployment, our Partners and Professional Services teams can help design and implement a Computer Vision solution tailored to your specific business needs and technical requirements.
- HAQM Kinesis Video Streams makes it easy to stream video securely from connected devices to AWS for analytics, machine learning (ML), playback, and other processing. HAQM Kinesis Video Streams automatically provisions and elastically scales all the infrastructure needed to ingest streaming video data from millions of devices. It durably stores, encrypts, and indexes video data in your streams, and allows you to access your data through easy-to-use APIs. HAQM Kinesis Video Streams enables you to playback video for live and on-demand viewing, and quickly build applications that take advantage of Computer Vision and video analytics through integration with HAQM Rekognition Video and HAQM SageMaker. HAQM Kinesis Video Streams also supports WebRTC, an open-source project that enables real-time media streaming and interaction among web browsers, mobile applications, and connected devices using simple APIs.
- People pathing using HAQM Rekognition offers powerful Computer Vision capabilities to track and analyze customer movement patterns in their stores through video analysis. By leveraging HAQM S3 for video storage, AWS Lambda for automated processing, and HAQM Rekognition, you can generate detailed heat maps showing high-traffic areas and understand customer behavior in unprecedented detail. This managed service solution streamlines the entire workflow from video capture to insights generation, making advanced analytics accessible without requiring deep Computer Vision expertise.
- AWS also provides builders with Solution Guidance shows how companies can harness in-store cameras and AI/ML capabilities to gain deeper shopper insights and enhance in-store experiences. Customers can automatically collect valuable analytics like heatmaps, dwell-time, and traffic flow. This approach empowers brick-and-mortar stores to better understand customer behavior, optimize layouts, and improve the shopping experience. By analyzing customers’ in-store journeys, you can make data-driven decisions to enhance customer satisfaction, increase conversions, and boost overall sales performance.
What’s next
Computer Vision has evolved from a cutting-edge innovation to a practical, accessible solution that delivers measurable ROI across operations, customer experience, and profitability. With AWS’s comprehensive suite of services (HAQM Rekognition, HAQM Rekognition Video, HAQM SageMaker, HAQM Kinesis Video Streams, HAQM S3, AWS Lambda), robust partner network, and Professional Services support, you can begin transforming your stores without requiring extensive capital investment or specialized technical expertise. Whether you’re looking to optimize store layouts, reduce wait times, or enhance staff productivity, the path to implementation is clear and achievable. Don’t let your competitors get ahead—contact your AWS representative today to schedule a discovery session and learn how we can help you unlock the power of computer vision in your stores. The technology is ready, the results are proven, and your existing infrastructure could be the foundation for your next competitive advantage.
Learn more about smart store solutions on AWS.