Revenue maximization in social networks through discounting

Social networking has become a part of daily life for many individuals across the world. Widespread adoption of various strategies in such networks can be utilized by business corporations as a powerful means for advertising. In this study, we investigated viral marketing strategies in which buyers are influenced by other buyers who already own an item. Since finding an optimal marketing strategy is NP-hard, a simple strategy has been proposed in which giving the item for free to a subset of influential buyers in a network increases the valuation of the other potential buyers for the item. In this study, we considered the more general problem by offering discounts instead of giving the item for free to an initial set of buyers. We introduced three approaches for finding an appropriate discount sequence based on the following iterative idea: In each step, we offer the item to the potential buyers with a discounted price in a way that they all accept the offers and buy the product. Selling the item to the most influential buyers as the opinion leaders increases the willingness of other buyers to pay a higher price. Thus, in the following steps, we can offer the item with a lower discount while still guaranteeing the acceptance of the offers. Furthermore, we investigated two marketing strategies based on local search and hill climbing algorithms. Extensive computational experiments on artificially constructed model networks as well as on a number of real-world networks revealed the effectiveness of the proposed discount-based strategies.