DLF leverages IBM mobile analytics solutions to tap into customer insights

MUMBAI, INDIA: IBM has announced the start of a collaboration with DLF Ltd, one of the largest real estate companies in India.

The two companies shall deploy an innovative mobile-phone based solution that channels data insights from customers visiting DLF Promenade, a high end shopping mall serving the metropolitan New Delhi area.

The technology performs real-time analytics to convert data gathered from shopper's movements in the mall to provide meaningful interactions for smartphone users.

For example, once a user opts-in for the service; the solution performs deep analytics to understand consumer preferences based on location and interests in order to provide customized services via a mobile app. DLF is now using the solution to allow retailers in the mall to extend sales deals to shoppers via the app, based on footfall heat maps.

Dinaz Madhukar, senior VP and Mall Head, DLF Promenad, said: "At DLF, we understand that it is important to view today's customers through the lens of connectivity and collaboration - which enables them to know almost everything about every product and brand, ultimately deciding which retailers have earned their trust. Trust is widely recognized as the foundation of interpersonal, consumer-to-consumer or consumer-to-business relationships. We feel IBM's solution will help us better understand and connect well with today's customers."

On the backend, the collected data takes into account users' contextual preferences, matching the services they want with the time of day they shop and at which store, for instance. As a result, over 25 businesses in the mall are able to understand their customer's habits more effectively and customers are provided with more personalized services.

The solution leverages Wi-Fi sensor routers to determine the location of the user and their proximity to the store, allowing customers who have signed up for the mobile app to gain access to preferential services or discounted goods based on the shopping preferences and past behavior they have put into the tool.

For instance, a shop in the mall selling sports goods, using the IBM solution, can identify potential customers who may have a strong interest in sports. By gathering insights through the app, potential customers who fit the store's target market can be eligible for additional discounts should they choose to enter the shop.