All activity on your social media accounts contributes to your “social graph,” which maps your interconnected online relationships, likes, preferred activities, and affinity for certain brands, among other things.
Now MIT spinout Infinite Analytics is leveraging these social graphs, and other sources of data, for very precise recommendation software that better predicts customers’ buying preferences. Consumers get a more personalized online-buying experience, while e-commerce businesses see more profit, the startup says.
The neat trick behind the software — packaged as a plug-in for websites — is breaking down various “data silos,” isolated data that cannot easily be integrated with other data. Basically, the software merges disparate social media, personal, and product information to rapidly build a user profile and match that user with the right product. The algorithm also follows users’ changing tastes.
Think of the software as a digital salesman, says Chief Technology Officer Purushotham Botla SM ’13, who co-founded Infinite Analytics and co-developed the software with Akash Bhatia MBA ’12. A real-world salesperson will ask consumers questions about their background, financial limits, and preferences to find an affordable and relevant product. “In the online world, we try to do that by looking at all these different data sources,” Botla says. Read more