Automated algorithm based on buying history etc

I am currently looking for a programmer to create or preferably already have built an automated algorithm based on buying history, gender, location, clicking patterns in certain deal sectors and other features as well: Using subscribers' will click-through histories from [login to view URL] (which will be my new website starting next week.. You can go to [login to view URL] to look at my current website which is still in beta to get a better idea) website and e-mails, or potentially i would team up with Foursquare to leverage significant location-based consumer data, Dealdoc could learn that a certain subscriber actually prefers restaurants to yoga, despite his or her preference settings. This would not only provide more value to subscribers, as Dealdoc would cater to them rather than their self-images, but would bring significant additional value to small businesses offering daily deals by further distilling their target audience. In addition, this could allow Dealdoc's recommendations to adjust as subscriber's spending habits change. Since consumers do not always notice, or want to notice, changes in their spending habits, they are unlikely to go back and reset their preferences on Dealdoc. This has turned out to be one of the major shortcomings to advertising on Facebook, as people's "likes and interests" are often outdated. personalization emails: this should be based off of buying history, gender, location, and other attributes as well.. I look forward to hearing from you.