An Effective Model of Viral Marketing for e-Commerce Enterprises: An Empirical Study

Despite the widespread significance of digital marketing in disseminating information about products and services across a vast customer base via diverse networks, a noteworthy proportion of businesses still struggle to comprehend the crucial factors underpinning the success of viral campaigns. This study aims not only to bridge this knowledge gap but also to introduce an innovative framework that underscores various factors that amplify the potency of social networks and emphasizes an often-overlooked element in customer engagement: the psychological state of customers. Empirical validation of the framework was conducted using a sample of 135 respondents, which was analyzed using the structured equation modeling technique. The study's findings show that the strength of social connections (strong ties) and the psychological disposition of customers significantly shape the generation and viral dissemination of marketing content across diverse networks. The importance of this research lies in its potential application by commercial companies for conducting promotional and marketing campaigns. By leveraging the proposed model, businesses can effectively promote their products and services, thus achieving their strategic objectives and gaining a competitive advantage in an environment characterized by intense competition and constant change.   Doi: 10.28991/HIJ-2024-05-01-011 Full Text: PD

Revenue maximization for telecommunications company with social viral marketing

[[abstract]]Viral marketing, a marketing strategy that leverages the influence power in intimate relationship, has become more prevalent due to the popularity of online social networking services in recent years. Consumers are more likely to make a purchase based on social media referrals. Since marketing through social media and traditional channels may target on different audiences, how to maximize the revenue of a telecommunications company by employing different advertising ways and selecting initial users for advertisements is a critical problem. Therefore, in this paper, we formulate a new research problem, namely Cost-Aware Multi-wAy Influence maXimization (CAMAIX) to address the need mentioned above. We design a 1/2-approximation algorithm with various pruning and budget allocation strategies to solve CAMAIX efficiently. We conduct extensive experiments on a large-scale real dataset from a telecommunications company. The results show that our proposed algorithm outperforms the baseline algorithms in both solution quality and efficiency.[[notice]]補正完