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Retail stores tap into AI to integrate online, offline buyer experiences




Large retail chains are seeking to enhance customer satisfaction by using artificial intelligence (AI) tools such as machine learning and computer vision to study consumer behaviour online and in physical stores, in an effort to offer them the best products and experiences.

Bengaluru and San Francisco based AI solutions provider Algonomy has deployed an AI-based decision engine called Xen AI for Pantaloons, a multi-brand retail chain owned by Aditya Birla Fashion and Retail Ltd. Xen AI selects the most optimal experience for every interaction in real-time, based on the customer’s profile and stage in the buying journey. For instance, if a lady customer browses for a peach dress online and later visits the store to try it, a store associate uses an app to assist her better based on her preferences, behavioural data, searches and past purchases, said Bhavna Sachar, director, product marketing at Algonomy. The idea is to use AI-based personalization to offer tailored omnichannel experiences to customers, she said.

“There is a strong desire and action towards breaking down the artificial separation between stores and digital, that leads to broken journeys and fragmented experiences for the customer, and efficient operations for the retailer,” Sachar said.

Gurugram-based AI startup Staqu has seen a significant increase in demand for its retail analytics solution that leverages computer vision to provide insights to stores. “Demand from retail has grown very fast after the pandemic. The reason is simple—they are competing with e-commerce,” said Atul Rai, chief executive officer and co-founder of Staqu.

Rai said e-commerce stores are better positioned to capture data on customers and leverage it to display relevant products and deals. They know when users visit the website and what they are doing on it. “Offline stores do not have access to that sort of data. All they know is how many sales happened. The data they have is not sufficient to understand customer needs and plan sales and marketing activity,” he said.

Staqu’s retail analytics solution offers features such as footfall analytics that taps into feed from in-store cameras to keep track of footfall at a particular store. It also offers demographic analysis factoring elements such as customers’ gender. It also offers planogram analysis to find out customer heat map in a large store.

According to Rai, Staqu has deployed these solutions in several stores. “We are also in talks with Starbucks and Future Retail,” he added.

While the efforts to leverage AI to offer a richer customer experience in retail stores have intensified after the pandemic, challenges remain. Large retail stores have been using customer relationship management (CRM) and data management for years. According to Rajat Wahi, partner, Deloitte India, many of these solutions are now leveraging AI, which helps in building better knowledge and capability. However, the challenge is “how do you capture that initial customer data and how do you make that user friendly for customers and shoppers”, he said.

For large retail stores, capturing a lot of data on consumers can also prove to be tricky with the growing awareness about data privacy and the impending data protection laws.

Keeping these data-related concerns in mind, solution providers such as Algonomy said that while providing services, they use an anonymous identifier that has no ability to connect back to an individual. Store assistants and other users’ access can be configured as per the merchant’s defined levels. “Typically, they would have access to the AI-generated recommendations—products they are likely to be interested in, cross-sell/ complete-the-look items as well as brand, category and other affinities.”

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