Modeling And Optimization Method Research Of Bundle-size Pricing For Online Content

With the growth of internet technology, it is of great convenience for customers to obtain and use online content, promoting the development of online content industry. There comes a critical decision problem for a service provider that is how to price online content. Since the online content can be transmitted without the traditional physical media, and the reproduction of online content is equal to copy, so that the session related to marketing and distribution is unnecessary, which causes the marginal cost to be cut to almost zero so as to create higher profit margins for service providers. However, the particular cost structure of online content creates difficulties for pricing. According to the existing theory and practice results, no effective online content pricing method could be found. To this point, we study the optimum decision of bundle-size pricing for online content. Under the bundle-size strategy, the online content service providers need to solve the following decision problem:(1) choose bundles of which size to provide to customers;(2) how to reasonably pricing for each bundle. First, the paper proposed a bundle-size customer perceived value model so as to establish a bundle-size pricing optimization model. The optimum decision problem belongs to a nonlinear mixed integer programming problem, and currently no method can work out the problem particularly well. So, the paper adopted genetic algorithm for the problem. According to the result of simulation experiment, the approach proposed in this paper can work well with the optimum decision of bundle-size pricing.