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How CMR Group Leverages AI & Analytics To Drive Its Retail Business

How CMR Group Leverages AI & Analytics To Drive Its Retail Business

  • With an average accuracy of 92-98%, the AI system provided meaningful insights that helped CMR Group grow its business.
How CMR Group Leverages AI & Analytics To Drive Its Retail Business

CMR Shopping Mall, a subsidiary of the CMR Group, is a known brand in Andhra Pradesh with a strong presence in textiles, jewellery, and real estate. 

While pandemic has put a dent on the shopping mall business, CMR is picking up momentum, with an average footfall of 4,000-10,000 every day. However, as a large retailer, CMR Shopping Mall’s technology adoption was subpar. Due to the scarcity of skilled workforce amid pandemic, the retailer had to bear the brunt of fraudulent activities and inefficiency in its supply chain management.

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Moreover, CMR Shopping Mall was beset by price wars and was struggling with tax structure complexities. Thus, the retailer needed to deploy a single point solution that can allow them to view all stores live from a single dashboard, measure footfall and trends, and identify shoplifters.

To address these issues, CMR turned to Veda Labs, an AI-powered retail analytics company. We spoke to Mohan Balaji Mavuri, the Director of CMR Group and Veer Mishra, the Co-Founder & CMO of Veda Labs, to understand how Veda’s AI platform uses real-time analytics and consumer insights to help CMR grow their in-store conversions.

“No matter which store, each retailer needs a solution that allows them to view each camera live from a remote location, irrespective of which vendor has installed the surveillance infrastructure in the store,” said Mishra. “Veda’s platform works amazingly for each of them and provides a unified live view on the dashboard for each camera on demand.”

Mavuri stated: “Having an overview of the footfall of each store enables us to know the trends and thus manage internal operations without any hassle.”

How Does It Work?

Veda’s AI-powered retail analytics suite, aka a single point edge device, works with existing CCTV cameras. The edge device gets connected to the existing surveillance infrastructure and starts creating an analytics pipeline to process all video feeds for business insights.

Once that’s done, Veda’s system uses computer vision and deep learning and supporting algorithms to analyse the number of people entering the mall’s premises, along with the timestamp on the peak traffic and information on the average time spent by each customer in the store. Each visitor crossing a predetermined area is counted as in or out in real-time and is displayed on a personalised dashboard.

Veda uses a 3D depth-sensing technology that measures every footfall bi-directionally in the mall, with an accuracy rate of 98.5%. Further, the probability and intelligent algorithms allow CMR to track each visitor’s movements to the mall. Post analysing the footfall, depending on each customer’s body type and facial parameters, Veda classifies each visitor based on their gender and age group using proprietary facial recognition technology.

The cameras detect the person’s face and body, and then share the detected image with the machine learning model that leverages deep neural networks and various algorithms to provide an output with a person’s age and gender. For this, a deep neural network has been trained to address tasks like predicting age, gender and other relevant attributes, distinguishing them from other people.

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Explaining the process, Mishra said, “Our AI system anonymously tracks each visitor entering the mall and maps his journey to present an overview of how the customers are navigating. It also provides heat maps of the overall floor, allowing CMR to understand the highest performing sections of the mall.”

Veda uses computer vision and machine learning to perform all the analytics on edge and sends alerts to the dashboard. Alongside, the existing CCTV footages are used by the edge processor, which has Veda’s tech stack for video analytics. The tech stack includes Github, Docker, Tensorflow, Pytorch, Open CV, Angular & Python.

The AI platform also uses unique person tracking mechanisms to identify repeat customers, helping them flag blacklisted visitors in real-time.

Wrapping Up

With an average accuracy of 92-98%, the AI system provided meaningful insights that helped CMR Group grow its business. The AI system allowed the management of the CMR Shopping Mall to know the gender and age-wise footfalls, average time spent by an individual customer in the mall, and identify the shoplifting activity in the mall in order to optimise their in-mall customer experiences.

With insights like footfalls and inventory consumption patterns, CMR Shopping Mall also managed to have better and improved inventory management. “We are planning to scale our deployment in the next phase at multiple locations for the CMR Group and working closely with their leadership in order to create more exciting use cases that enable them to grow more and allows us to serve them better,” concluded Mishra.

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