Abstract
Viral marketing has been one of the main marketing modes. However, theoretical study of viral marketing is still lacking. This paper focuses on the problem of developing a cost-effective dynamic discount pricing strategy for a viral marketing campaign. First, based on a novel word-of-mouth propagation model, we model the original problem as an optimal control problem. Second, we show that the optimal control problem admits an optimal control and present the optimality system for solving the optimal control problem. Next, we solve some optimal control models to get their respective optimal dynamic discount pricing strategies. Finally, we examine the effect of some factors on the maximum marketing profit. These results contribute to gaining insight into viral marketing.
Highlights
Viral marketing, known as word-of-mouth (WOM) marketing, is an effective marketing mode, in which the marketing information spreads in the form of WOM among customers [1]
This paper focuses on the dynamic discount pricing (DDP) problem, i.e., the problem of developing a cost-effective dynamic discount pricing (DDP) strategy for a viral marketing campaign
Dynamic discount pricing (DDP) problem: For a marketing campaign launched by a merchant, develop a dynamic discount pricing strategy to maximize the profit of the merchant
Summary
Known as word-of-mouth (WOM) marketing, is an effective marketing mode, in which the marketing information spreads in the form of WOM among customers [1]. To accurately evaluate the cost profit of a viral marketing campaign, we have to gain a deep insight into the laws of WOM propagation [7] For this purpose, in recent years some WOM propagation models based on homogeneous networks have been proposed [8,9,10,11,12,13]. The node-level epidemic modeling technique has been applied to areas as diverse as malware spreading [23,24,25,26,27,28], rumor spreading [29, 30], and cyber defense [31,32,33] To our knowledge, this epidemic modeling technique has not been employed to characterize the propagation of WOM over arbitrary networks.
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