The online display advertising market is big. Yet as much as 90 percent of the available online display ad inventory in a day goes unsold, largely because untargeted display produces unprofitable results for most advertisers and also because buyers and sellers can't always be matched up in time. But recent advances in online advertising infrastructure have created advertising inventory exchanges and platforms to automate and enhance real-time inventory buying (RTB) of inventory with sophisticated algorithms.

Each of the over 15 billion daily RTB transactions require data for optimum performance and economic buying, and Jay Meattle's Shareaholic has created a powerful platform to deliver targetable data and audiences to these advertisers, a demand side platform (DSP) for selling ad inventories.

Display advertising inventory in the past was bought on blind faith but the advertisers today can buy inventory directly on the exchanges in real-time based on customers' buying behaviors and other such patterns. But a prerequisite to purchase via DSPs is the need for data. And Shareaholic is providing one of the largest caches of targeting data in the world. Over two million people have downloaded Shareaholic. com's browser plug-ins, which enable content sharing through services like Facebook, Tumblr, Twitter, Google+ or plain old e-mail.

Advertisers, when subscribed, get access to targetable audiences based on interest and intent signals mined from users viewing habits, what and who they share with, and how influential they are. A case for this targeting capability, for example, would be to allow technology advertisers to target ads to only those people who share content about the iPhone and have more than one clickthroughs. It has been found that the performance, as measured by clickthrough rates, with this targeting capability was much higher than display ad buying without such targeting data. And Shareaholic's idea of building an intelligence layer based on sharing habits of users helps advertisers to make better buying decisions.