Modelling for Dynamic Growth of User Population of Products and Services Recently, technological advances have made possible the measure of daily (even hourly) user(sales) growth of a product or service. Here, questions comes: how users grow and how the promotion influence the user growth? Here, we develop a model that can describe the growth of the user population of a newly launched product or service, which can answer this question. To develop this model, we consider a network of interacting individuals, whose actions or transitions are determined by the states (behaviour) of their neighbours as well as their own personal decisions. This model leads to a simple growth equation connecting the growth of user population with the total number of prospective users, and the effects of peer influence and personal choice. Several reallife datasets of a variety of products and services have been analyzed. Results suggest that they all follow the proposed growth equation. The numerical procedure for finding the model parameters thus ensure the relative effectiveness, market size and promotional efforts to be estimated from the available historical growth data.

To Be or Not to Be: Analyzing & Modeling Social Recommendation in Online Social Networks Firms are now considering to offer rewards to customers who recommend the firmsu0027 productsservices in online social networks (OSN). However, the pros and cons of such social recommendation scheme are still unclear. Thus, it is difficult for firms to design rewarding schemes. Via empirical analysis of data, we first identify key factors that affect the spreading of a firmu0027s product in OSNs. These findings enable us to develop an accurate (i.e., with a high validation accuracy) mathematical model on social recommendations. In particular, our model captures how users decide whether to recommend an item, which is a key factor but often ignored by previous social recommendation models such as the Independent Cascade model. We also design algorithms to infer model parameters. Using these parameters in our model, we uncover conditions when social recommendation can (or cannot) improves a firmu0027s profit. These conditions help a firm to design rewarding schemes. Finally, we extend our model to an online setting and design reinforcement learning algorithms for a firm to dynamically optimize its rewarding schemes.