One of the world’s largest sporting goods retailers with 1500+ stores across more than 45 countries needed its decision-making to be flexible and responsive to retain its leadership position. To be able to do this, it needed real-time analytics of store visitors, which is typically run using footfall data. However, the client wanted to link footfall data with POS data to improve store performance effectively and increase overall customer satisfaction.
After evaluating several vendors, the company chose Indium Software to implement a cognitive analytics solution leveraging computer vision to achieve its goal. Indium enabled this by using the existing security cameras:
The data collected from these cameras was used to analyze customer behavior at specific zones within the store. This was done by building comprehensive dashboards with real-time data refresh.
Indium built a neural network model by training various classes of the object or person, using 2000+ images for image processing and video analytics with maximum accuracy. By creating and testing annotations using sample videos, it was able to constantly improve the accuracy (over time, accuracy went up to 80%). Within the data points that were gathered, outliers were identified.
Since the visitor statistics was linked to the POS systems, they were able to track the conversion rate, improve product placement and implement cross-selling tactics. The solution had an easy-to-use interface that provided all stakeholders with better insight into customers’ behavior as well as shelf zone analysis.
As a result, the retailer was able to experience a 15% increase in customer satisfaction.
According a report published by MarketsAndMarkets, the broader market for computer vision-enabled solutions is expected to grow from USD 15.9 billion in 2021 to USD 51.3 billion by 2026 at a CAGR of 26.3%. Of course, this includes the use of computer vision for factory inspections, audits, quality and safety management, etc.
Computer Vision, simply put, refers to enabling computers to identify and process objects in images and videos the way a human brain does. It helps computers to “see and understand” content in photographs and videos. By integrating AI capabilities to computer vision, we’re now able to conduct analytics on data from images and videos.
Today, it can be used to identify an item with 99% accuracy as against 50 percent a decade ago.
There are use cases for cognitive analytics and computer vision in a variety of industries such as:
Indium is a technology solutions company with deep expertise in digital, data engineering, data analytics services. With its global presence serving customers ranging from innovative product startups, Fortune 100 and global enterprises, Indium’s key differentiators are its specialization in:
Indium Software has a team of cognitive analytics experts who work on the following areas:
We use FCN (Fully Convolutional Network), Mask-RCNN (Region-based CNN), and Detectron2 for image and video analytics; and deep neural networks and Optical Character Recognition (OCR) for text analytics and speech recognition.
Our range of solutions includes:
To know more about how Indium can help you build your cognitive analytics model integrating it with computer vision, contact us now.
By Uma Raj
By Uma Raj
By Abishek Balakumar
Indium Software is a leading digital engineering company that provides Application Engineering, Cloud Engineering, Data and Analytics, DevOps, Digital Assurance, and Gaming services. We assist companies in their digital transformation journey at every stage of digital adoption, allowing them to become market leaders.