scholarly journals Strategi Komunikasi Melalui Viral Marketing dan Diversifikasi Terhadap Penarikan Pelanggan

2021 ◽  
Vol 5 (2) ◽  
pp. 82-89
Author(s):  
Ana Ramadhayanti
Keyword(s):  
Viral Marketing

Penelitian ini terdiri adri tiga variabel yakni Strategi  Komunikasi Melalui Viral Marketing (X1),  Diversifikasi (X2) dan Penarikan Pelanggan (Y). Adapun tujuan dari penelitian ini adalah untuk mengetahui pengaruh Strategi  Komunikasi Melalui Viral Marketing dan Diversifikasi Terhadap Penarikan Pelanggan. Penelitian menggunakan metode penelitian kuantitatif dengan memperoleh data melalui penyebaran kuesioner. Adapun olah data menggunakan sofware SPSS 25. Hasil analisis nilai Sig uji F sebesar 0,000 hal ini menunjukkan bahwa Sig uji F 0,000 > α (0,05) dengan uji F menunjukkan bahwa variabel bebas Strategi  Komunikasi Viral Marketing   (X1) dan Diversifikasi  (X2) secara simultan berpengaruh terhadap variabel terikat Penarikan Pelanggan (Y). Sementara itu jika dianalisa dengan uji regresi menujukkan Nilai korelasi (R) yang dihasilkan adalah 0,965, sehingga dapat disimpulkan bahwa terdapat hubungan yang sangat erat antara Strategi  Komunikasi Viral Marketing   (X1) dan Diversifikasi  (X2) dengan Penarikan Pelanggan (Y). Dengan melakukan penelitian ini diharapkan dapat menarik produsen lain dalam melakukan Diversifikasi produk. 

VIRAL MARKETING IN SOCIAL NETWORKS

2019 ◽  
Vol 5 (5) ◽  
Author(s):  
Elena Gerasikova ◽  
Milena Ischenko ◽  
Olga Saenkova ◽  
Nataliya Yasenkova
Keyword(s):  
Social Networks ◽  
Viral Marketing

Techniques of Viral Marketing

2019 ◽  
Vol 118 (6) ◽  
pp. 97-99
Author(s):  
Arockia Jeyasheela A ◽  
Dr.S. Chandramohan
Keyword(s):  
Social Media ◽  
Real Time ◽  
Word Of Mouth ◽  
Viral Marketing ◽  
Consumer Perception ◽  
Key Word ◽  
The Right ◽  
A Company

This study is discussed about the viral marketing. It is a one of the key success of marketing. This paper gave the techniques of viral marketing. It can be delivered word of mouth. It can be created by both the representatives of a company and consumer (individuals or communities). The right viral message with go to right consumer to the right time. Viral marketing is easy to attract the consumer. It is most important advertising to consumer. It involves consumer perception, organization contribution, blogs, SMO (Social Media Optimize), SEO (Social Engine Optimize). Principles of viral marketing are social profile gathering, Proximity Market, Real time Key word density.

THE IMPACT OF VIRAL MARKETING ON CONSUMER INTEREST ON INDIHOME PRODUCT

2019 ◽  
Vol 7 (1) ◽  
pp. 103-107
Author(s):  
Yani Restiani Widjaja ◽  
Ruth Alexandra
Keyword(s):  
Viral Marketing ◽  
Consumer Interest ◽  
The Impact

Seeding Strategies for Viral Marketing Using Network Hub: The Moderating Effect of Network Density

2020 ◽  
Vol 31 (5) ◽  
pp. 231-247
Author(s):  
Kang Jun Choi ◽  
Soon Pyeong Kim ◽  
Jae Young Lee ◽  
Tae Hyung Pyo
Keyword(s):  
Moderating Effect ◽  
Viral Marketing ◽  
Network Density

scholarly journals A Survey of Information Cascade Analysis

2021 ◽  
Vol 54 (2) ◽  
pp. 1-36
Author(s):  
Fan Zhou ◽  
Xovee Xu ◽  
Goce Trajcevski ◽  
Kunpeng Zhang
Keyword(s):  
Information Diffusion ◽  
Research Work ◽  
Viral Marketing ◽  
Graph Representation ◽  
Digital Information ◽  
Information Cascades ◽  
Challenges And Opportunities ◽  
Scientific Papers ◽  
Popularity Prediction ◽  
Types Of Information

The deluge of digital information in our daily life—from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising—offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades. Abundant research efforts, both academic and industrial, have aimed to reach a better understanding of the mechanisms driving the spread of information and quantifying the outcome of information diffusion. This article presents a comprehensive review and categorization of information popularity prediction methods, from feature engineering and stochastic processes , through graph representation , to deep learning-based approaches . Specifically, we first formally define different types of information cascades and summarize the perspectives of existing studies. We then present a taxonomy that categorizes existing works into the aforementioned three main groups as well as the main subclasses in each group, and we systematically review cutting-edge research work. Finally, we summarize the pros and cons of existing research efforts and outline the open challenges and opportunities in this field.

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