Whichit developed an innovative interactive advertisement unit that’s embedded into web content pages. The advertisement unit presents a poll or survey with four preset answers in the form of text superimposed onto an image, in a four quadrant format. Based on the gamified nature of the advertisement, user engagement clocks in at 90% to automatically generate a gateway to a conversion opportunity. How is this done? Their machine learning algorithm can profile every viewer, recording and processing user details to understand their online DNA and use that information to provide the most accurate commercial incentives, in real time, with the highest chances of conversions.
Originally started as a project in 2012 to source the opinion of the one’s network in response to questions starting with “which”, the technology proved promising to engage internet surfers on a viral level. A year and half later, Whichit was officially launched.
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Today, Whichit allows users to create unique rich media ad units in the form of polls, quizzes, trivia, surveys and more, called ‘Whichits’. Consumers can vote on their preferences via these interactive ad units and those preferences are collected and analysed to allow for real-time profiling of consumers. “Our customer campaigns have reached as high as an almost unheard of 90% Engagement Rate. Click-Through Rates too are 40x higher than traditional display advertising” explains Jonathan Gan, CEO and co-founder.
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Started by Jonathan Gan, Galit Gan and Yarden Jacobson, Whichit is the product of Israeli technical minds. Jonathan (CEO) is a former Israeli Air Force Major in the anti-aircraft company; Yarden (CTO) is a former technical consultant for SAP and the lead developer for Ness Technologies; and Galit, an experienced designer in UI/UX and 3D Imaging.
In recent news, Microsoft’s new collaborative 365^x Business Accelerator has chosen Whichit to join their inaugural class, located in Kfar Saba, Israel. Whichit has been in the spotlight before, winning ‘startup fo the year’ from Facebook in 2015, and soon thereafter drawing in a $250K grant after winning the UK R&D Funding Award to support the development of their innovative machine learning algorithms used in the user profiling process. The final algorithm employs several AI techniques, such as neural networks, clustering, hidden markov models and naive bayes classifiers.
