Purchase Prediction from Social Media. Methodology

Seminar paper from the year 2014 in the subject Communications - Public Relations, Advertising, Marketing, Social Media, grade: 1.3, University of Heidelberg (Computer Science), course: Seminar - Social Media Network Analysis, language: English, abstract: With a predicted volume of 439.7Bn in 2014 in Germany alone, the retail market bears large potential for generating additional revenues from marketing. With the decreasing effectiveness of classical marketing and even relatively new phenomena like online ads it becomes more and more important to find new ways to recommend products to customers. In e-commerce it is generally easier to target specific audiences by for example selecting ad spaces according to thematically fitting web pages. The fundamental difference to classical marketing approaches is the availability of data about the respective customer. Currently the most common approach is to mine frequent item sets from the purchase history of the customer population and recommend products to customers based on what other customers bought. In order to obtain more specific product predictions for a particular customer, more data about the respective customer is needed. It seems like a natural choice to dig for data in the rich pool of data generated by each customer himself by assessing their respective actions and content generated, especially on social media websites. The available data there is much more user specific than general purchasing behaviors of user groups and can potentially lead to very precise predictions about what the user is interested in and will buy. This paper first gives a brief overview over the development and research conducted on social media recommendation and behavior of online shoppers in general. Then the work of Y. Zhang and M. Pennacchiotti is presented. Finally, several possibilities for subsequent research based on previous work and the work of Zhang and Pennacchiotti are presented. Since the work presented in this paper is