Evaluation of the Efficacy of the Combined Viral Marketing Method with the Network Clustering Method and Comparing the Results
Subject Areas : Technology Managementfereydoun ohadi 1 , mehrnoosh mohammadi 2 , Mohammad Jafar Tarokh 3
1 -
2 -
3 -
Keywords: Viral Marketing Centrality Graph Social Network Clustering,
Abstract :
In a Competitive Market, Understanding Customer Demand and Effective Advertising is one of the most Important Factors in Survival. Extend the Internet and virtual networks have provided a great opportunity for companies to advertise, and thus studying electronic marketing methods and models is of great importance. One of the newest marketing methods is viral marketing that is based on mouth-to-mouth advertising and has a lot of power. Viral marketing relies on the principle that on any social network, a number of users have high power and influence on others, and by identifying them and creating good advertising messages, They can be used to effectively marketing. Therefore, The identification of important users is considered the most important activity in viral marketing. In this regard, various studies have been conducted to identify users using a variety of graph-based and publish-based methods. In this research, the capabilities of both methods have been used and by Using a semi-localized centrality criterion based on graph-based methods and Markov clustering model based on propagation methods, a new hybrid model for user clustering and identification of key users presented. The results show higher correlation between the proposed method and the SIR standard and, therefore, its higher efficiency than other methods used in the research.
1- بيژن، علمي؛ مجتبي، رمضاني، بررسي جامعه‌شناختي تاكتيک‌هاي مؤثر بر بازاريابي ويروسي و نقش آن بر رفتار خريد، مجله مطالعات جامعه‌شناسي، شماره 10، صفحات 125 تا 144، 1390.
2- محمدعلی، محمودیار، یک مدل مبتنی بر گراف برای ارزیابی اعتماد در جوامع برخط، پایان‌نامه جهت اخذ مدرک کارشناسی‌ارشد، دانشکده مهندسی صنایع، دانشگاه صنعتی خواجه نصیرالدین طوسی، 1395.
3- احد، جعفرزاده، ارائه مدل بازاریابی ویروسی در شبکه‌های اجتماعی با استفاده از تئوری گراف، پایان‌نامه جهت اخذ مدرک کارشناسی‌ارشد، دانشکده مهندسی صنایع، دانشگاه صنعتی خواجه نصیرالدین طوسی، 1395.
4- سوسن، روح‌روان؛ منیره، حسيني، بررسي فرصت‌هاي بازاريابي ويروسي موبايل در ايران، هفتمين همايش ملي و اولين همايش بين‌المللي تجارت و اقتصاد الکترونيکي، تهران، انجمن علمي تجارت الکترونيک ايران، 1392.
5- نسترن، حاجی حیدری؛ امیر، خانلری؛ حمیدرضا، ریحانی، شناسایی و تعیین اهمیت عوامل مؤثر بر بازاریابی ویروسی در حوزه نرم‌افزارهای کاربردی موبایل، مدیریت فناوری اطلاعات، 9(2)، 237 تا 252، 1396.
6- Kaple. M, Kulkarni. K, Potika. K, Viral Marketing for Smart Cities: Influencers in Social Network Communities, IEEE Third International Conference on Big Data Computing Service and Applications, DOI: 10.1109/BigDataService. 2017.46, 2017.
7- Hung T. Nguyen, My T. Thai, and Thang N. Dinh, A Billion-Scale Approximation Algorithm for
8- Maximizing Benefit in Viral Marketing, IEEE/ACM TRANSACTIONS ON NETWORKING, No. 181, Vol. 23, 2017.
9- Li. Y. M, Lai. C. Y, Chen. C. W, Discovering Influencers for Marketing in the Blogosphere, Information Sciences, Vol. 25, Issue. 4, pp. 5143-5157, 2011.
10- Kaplan, A. M, Haenlein. M, Two Hearts in Three-Quarter Time: How to Waltz the Social Media/Viral Marketing Dance, Business Horizons, 54(3), 253-263, 2011.
11- Zhuo-ming. R, Jian-guo. L, Feng. S, Zhao-long. H and Qiang. G, Analysis of the Spreading Influence of the Nodes with Minimum K-shell Value in Complex Networks. Acta Phys. Sin, 62 (10), Article 108902, 2013.
12- Zeng. A and Zhang, C. J, Ranking Spreaders by Decomposing Complex Networks, Physics Letters A, 377 (14), pp. 1031–1035, 2013.
13- Berger. J, Iyengar. R, How Interest Shapes Word-of- Mouth Over Different Channels, The Wharton School of the University of Pennsylvania, 2012.
14- Berger. J and Milkman. K. L, What Makes Online Content Viral? Journal of Marketing Research, 49(2), 192–205, 2012.
15- Long. C, Wong. R. C-W, Viral marketing for dedicated customers. Information Systems, 46, 1-23, 2014.
16- Oliveira. M, Gama. J, An Overview of social Network Analysis, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol.2, No.2, pp.99-115, 2012.
17- Han. J, Pei. J, Kamber. M, Data Mining: Concepts and Techniques, Third Edition , Elsivier, MA, USA, 2011.
18- Bauer. f, Lizier. J. T, Identifying Influemtial Speareders and Efficiently Estimating Infection Numbers in Epidemic Models: A Walk Counting Approach, EPL Europhysical Letters. No.99, Vol.6, pp.68007, 2012.
19- Rad. A, Benyoucef. M, Towards Detecting Influential Users in social Networks, E- Technologies: Transformation in a Connected World, 5th International Conference, pp. 227–240, 2011.
20- Rodrigues, H. S., & Fonseca, M. J. (2016). Can information be spread as a virus? Viral marketing as epidemiological model. Mathematical Methods in the Applied Sciences, 39(16), 4780-4786.
21- Ahmed, H. M. S. (2018). A Proposal Model for Measuring the Impact of Viral Marketing Through Social Networks on Purchasing Decision: An Empirical Study. International Journal of Customer Relationship Marketing and Management (IJCRMM), 9(3), 13-33.
22- Wang, Y., Vasilakos, A. V., Ma, J., & Xiong, N. (2015). On studying the impact of uncertainty on behavior diffusion in social networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(2), 185-197.
23- Kaple, M., Kulkarni, K., & Potika, K. (2017, April). Viral marketing for smart cities: Influencers in social network communities. In 2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService) (pp. 106-111). IEEE.
24- Helal, N. A., Ismail, R. M., Badr, N. L., & Mostafa, M. G. (2016). A novel social network mining approach for customer segmentation and viral marketing. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 6(5), 177-189.
