Optimizing the Effectiveness of Incentivized Social Sharing

Abstract

Social media has become an important tool for companies interested in increasing the reach of their products and services. Some companies even offer monetary incentives to customers for recommending products to their social circles. However, the effectiveness of such incentives is often hard to optimize due to the large space of incentive parameters and the inherent tradeoff between the incentive attractiveness for the customer and the return on investment for the company. To address this problem, we propose a novel graph evolution model, Me+N model, which provides flexibility in exploring the effect of different incentive parameters on company's profits by capturing the probabilistic nature of customer behavior over time. We look at a specific family of incentives in which customers get a reward if they convince a certain number of friends to purchase a given product. Our analysis shows that simple monetary incentives can be surprisingly effective in social media strategies.