Location and behaviour data platform, Near, has become the world’s largest source of intelligence on human behaviour since its founding in 2012, with key customers including Mastercard, Nike and Telstra.

Australia was one of the first markets to launch and since 2012, the company has established its headquarters in Singapore and now has a global presence.

Near co-founder, Shobhit Shukla said the platform incorporates rich intelligence on people and places using location data that reflects individual lifestyle choices and behaviours and in turn, can help businesses better understand their customers.

“For example, the intelligence may help retailers choose the next location for a store opening as they can analyse foot traffic in particular areas,” he told Retailbiz.

“The data can add value to businesses by connecting online and offline channels and by providing a unified view of the customer purchase journey, including when consumers are shopping, how frequently they are shopping, and where they are shopping. This not only helps business owners make more strategic decisions but also enables them to personalise their product offering and remain relevant.”

A critical aspect of the platform is artificial intelligence (AI) to employ customer profiling and produce targeted ads for customers. Although the data is being used for innovation and experimentation, user privacy does come into the equation, but Shukla assures me that the data is consent-based. “We use this data to invest in case studies and leadership ideas,” he said.

Near recently raised AUD$143 million (US$100 million) through a Series D funding round and will be using the funds in two major areas, according to Shukla.

“We will be using the funding to acquire new data systems to further analyse customer purchase behaviour in terms of the types of products they are purchasing, the times they are purchasing, the location where they are purchasing, as well as their likes and dislikes. We will also be using the funds toward our golden marketing strategy and continued R&D investment in machine learning.”