The Challenge
The India-based general merchandiser faced a unique challenge in their retail stores. Customers typically spent an average of 35 minutes shopping before reaching the Point of Sale (POS) for payment. However, by the time customers arrived at the POS, they had already made their purchase decisions. This not only resulted in missed opportunities for upselling and cross-selling but also meant that valuable shopping time had been underutilized. The retailer aimed to target repeat customers for these opportunities based on their profiles while they were still shopping, well before reaching the POS for payment.
The Solution
To overcome this challenge and optimize customer engagement, a comprehensive solution was implemented:
Repeat Customer Identification: The solution initiated with the identification of repeat customers using Computer Vision face recognition technology. Cameras were strategically placed at store entry points, exit points, and POS/cash counters. OpenCV Face Detection library was utilized in edge devices to detect faces and initiate image capture.
Dwell Time Tracking: The system tracked customer dwell time at various capture points and utilized face recognition powered by AWS Rekognition. Transaction records were stored in AWS DynamoDB for future reference.
Customer Profiling: Customer profiles were gradually built by linking POS order records in SQL Server with customer records in AWS DynamoDB. This linkage was executed nightly to minimize load on the POS system during peak business hours.
Real-time Offers: When a customer entered the store, the Computer Vision program triggered the customer’s profile and retrieved personalized offers from DynamoDB. Store associates were alerted through an Android app to approach the customer for upselling and cross-selling.
The Result
The implementation of this solution led to substantial improvements for the India-based general merchandiser:
In-Depth Customer Insights: The retailer gained valuable insights into customer behavior, including repeat visits, purchase patterns, exact dwell times, purchase conversion rates, demographic profiles, and brand preferences based on demographics.
Proactive Customer Engagement: Store associates were equipped with real-time customer profiles and offers, enabling them to approach customers with personalized upsell and cross-sell opportunities based on customer segmentation.
Enhanced Sales and Customer Satisfaction: By engaging customers with targeted offers and recommendations during their shopping journey, the retailer experienced increased sales and improved customer satisfaction.
This client success story illustrates how an India-based general merchandiser leveraged Computer Vision, AWS services, and customer profiling to optimize customer engagement, drive sales, and gain valuable customer insights.
Tools and Platforms Used
