TC Show Off Ep 14: A look at Infinite Analytics’ flagship product SocialGenomix

In the fourteenth episode of TC Show Off, a weekly show that provides startups a platform to showcase themselves, their business models, the core USP etc., Infinite Analytics’ head of business development and marketing for India Radhika Shah showcases how the startup’s flagship product, SocialGenomix, provides an advanced personalisation engine for the web.

“We use advanced personalisation techniques and advanced semantic technologies to improve product discovery, increase customer engagements and conversions. The way this works is we acquire social data of the users from social media networks, look at their click stream behaviour on e-commerce sites as well as their past transactional data. We then merge all of this data into the ‘social genome’ and analyse it,” said Radhika.

“Our patent pending algorithms then match it with the product catalogue of e-com sites, which in turn helps us predict user intention in order to serve them recommendations in real-time, as well as come up with user insights which help marketers to target them with better offers,” she added.

The startup basically creates a social genome of a user, based on his/her structured and unstructured data on social networks. The product uses predictive analytic tools, Natural Language Processing (NLP) and machine learning to predict user behaviour for e-commerce, media & content, travel and enterprise businesses.

Co-founded by two MIT graduates Akash Bhatia (CEO) and Purushotham Botla (CTO), Infinite Analytics has its genesis in a class taught by Tim Berners-Lee, the inventor of the World Wide Web. The startup was founded in June 2012 and its clientele includes companies like Comcast (media), Zovi, Nordstrom and Being Human (apparel), and Roomstory & Croma (home goods), among others.

When asked about how the startup plans to tackle the challenge of leading e-commerce players implementing similar algorithms in-house rather than out sourcing it to companies like Infinite Analytics, Radhika said, “A lot of players are trying to build a recommendation engine in-house, but it’s largely focused on the user’s click stream behaviour. It does not take into account their social data, past transactions, and macro economic data etc.”
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