Growth Hacking: Exploring the Meaning of an Internet-Born Digital Marketing Buzzword

This study attempts to explore and triangulate with different methods the essence, definition, and methods of the grassroots practitioner term growth hacking. In the qualitative first part of our study, we conduct 12 expert informant interviews and a case study. In the quantitative second part of the study, we conduct a quantitative Twitter analysis to gain additional insights on the findings from the qualitative first part. As a result, we differentiate growth hacking from traditional marketing strategies and create a growth hacking process model depicting a sort of consensus of what can be thought to be integral parts of growth hacking

A Social Network-Based Recommender System (SNRS)

Abstract. Social influence plays an important role in product marketing. However, it has rarely been considered in traditional recommender systems. In this paper we present a new paradigm of recommender systems which can utilize information in social networks, including user preferences, item's general acceptance, and influence from social friends. A probabilistic model is developed to make personalized recommendations from such information. We extract data from a real online social network, and our analysis of this large dataset reveals that friends have a tendency to select the same items and give similar ratings. Experimental results on this dataset show that our proposed system not only improves the prediction accuracy of recommender systems but also remedies the data sparsity and coldstart issues inherent in collaborative filtering. Furthermore, we propose to improve the performance of our system by applying semantic filtering of social networks, and validate its improvement via a class project experiment. In this experiment we demonstrate how relevant friends can be selected for inference based on the semantics of friend relationships and finer-grained user ratings. Such technologies can be deployed by most content providers.

The decision‐making process in viral marketing—A review and suggestions for further research

Viral marketing is used to widely distribute content. To achieve this goal, the basic decision-making process from content reception to interaction must be clarified. This paper examines the decision-making process of individuals in viral marketing using a new dynamic model. In addition, this work reviews the existing literature on viral marketing and structures to identify existing issues for further research. The decision-making process is basically divided into two stages. In the first decision stage, individuals decide whether content should be considered. When individuals agree to view the content, they decide in the second stage whether they want to interact with it. These two decisions are influenced by three factors: the framework conditions, content, and interaction aims. With the help of the decision model, this paper summarizes the most important findings from viral marketing research over the last 20 years. In addition, this work provides new opportunities for further research in the field of viral marketing