Hypejar was founded in Montreal by myself, Won Jun Bae, Dylan Jude and Mike Kwon. Won Jun and Dylan are software engineers with programming experiences ranging from video games to web applications. Mike has been hacking the world of poker for the past several years and brings his knack for statistical analysis to the team. A licenced attorney in Quebec and Ontario, I am the “business guy.” We are all from McGill University, share a strong interest in new products and are tired of googling for information and product release dates.
The impending arrival of the new Tickle Me Elmo doll in 2006 sparked the idea for Hypejar. In the fall of that year, the doll was touted as the hottest item for that holiday season; it was virtually impossible to get your hands on one. Classified ads offered as much as $2,500 for one. In other words, there was an absurd amount of hypesurrounding it before its release. We wondered why in the era of social media was it not possible to quantify or at least have a better understanding of the demand for a product before it releases.
While there is no shortage of apps and websites that guide consumers’ decision-making in all sorts of niche markets such as RottenTomatoes, goodreads, gdgt, Yelp, TripAdvisor and Sortable, there is a void in available information about the future — no one compiles products that have yet to hit the market.
As well, aggregating demand for products in their pre-release stage has traditionally been done by the likes of mature market research firms using inefficient and outdated means. Hypejar aims to leverage social media to obtain those demand levels.
We currently have one of the largest compilations of information on upcoming mass consumer products across several categories including movies, music, video games, TV series, gadgets, automobiles, events, books and others. To allow for the large amount of content, Hypejar was built as a wiki, which makes it possible for users to contribute content. It is a social platform, making it possible to gather data on consumers’ levels of anticipation for given products based the interaction on the site.
A cross between Wikipedia and Pinterest, we plan to introduce brand new features in the next few months and to refine existing ones.