Abstract
Influencer marketing is an effective kind of digital marketing. It is useful to reach target audiences, and brands will be exposed to more valuable online consumers. The system for managing the influencer marketing campaign on a social network is very necessary to increase the effectiveness of an influencer marketing campaign. In this paper, a method for designing a management system for this marketing campaign is proposed. This system can collect data on the social network and extract information from data to detect emerging influencers for the brand to run the campaign. It works based on the measures of amplification factors, the passion point of a user with the brand, and the ability about content creation. This management system is also the foundation to establish commerce activities and build an advocate community of the brand. The built system shows the results of the campaign as a visual report in real time to support the brand giving the decision. The system has been tested in the real-world influencer marketing campaign and got positive feedback from the brands.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Deiss, R., Henneberry, R.: Digital marketing for Dummies, Wiley (2016)
Tabellion, J., Esch, F.: Influencer marketing and its impact on the advertised brand. In: Bigne, E., Rosengren, S. (eds.) Advances in Advertising Research X, pp. 29–41. Springer, Wiesbaden (2019). https://doi.org/10.1007/978-3-658-24878-9_3
Vo, L.: Mining Social Media – Finding Stories in Internet data, William Pollock (2019)
Zimmerman, J., Ng, D.: Social Media Marketing All-in-One, 4th edn. Wiley, Dummies (2017)
Levin, A.: Influencer Marketing for Brands. Springer, NewYork (2019). https://doi.org/10.1007/978-1-4842-5503-2
Laroche, M., Habibi, M., Richard, M., Sankaranarayanan, R.: The effects of social media based brand communities on brand community markers, value creation practices, brand trust, and brand loyalty. Comput. Human Behav. 28, 1755–1767 (2012)
Mediakix: Influencer marketing 2019 - Key statistics from our influencer marketing survey. https://mediakix.com/influencer-marketing-resources/influencer-marketing-industry-statistics-survey-benchmarks/. Accessed 11 Aug 2020
Riquelme, F., Gonzalez-Cantergiani, P.: Measuring user influence on Twitter: a survey. Int. J. Inf. Process. Manage. 52(5), 949–975 (2016)
Huynh, T., Zelinka, I., Pham, X.H., Nguyen, H.D: Some influence measures to detect the influencer on social network based on Information Propagation. In: Proceedings of 9th International Conference on Web Intelligence, Mining and Semantics (WIMS 2019), Seoul, Korea (2019)
Hiips. https://hiip.asia/influencer/. Accessed 11 Aug 2020
7Saturday. https://7saturday.com/en/index.html. Accessed 11 Aug 2020
Activate: https://try.activate.social. Accessed 11 Aug 2020
Huynh, T., Nguyen, H., Zelinka, I., Dinh, D., Pham, X.H.: Detecting the influencer on social networks using passion point and measures of information propagation. Sustainability 12(7), 3064 (2020)
Nguyen, H.D., Huynh, T., Luu, S., Hoang, S., Pham, V., Zelinka, I.: Measure of the content creation score on social network using sentiment score and passion point. In: Proceedings of 19th International Conference on Intelligent Software Methodologies, Tools, and Techniques (SOMET 2020), Kitakyushu, Japan (2020)
Valdiviezo, O., Sánchez, J.A., Cervantes, O.: Visualizing sentiment change in social networks. In: Proceedings of the 8th Latin American Conference on Human-Computer Interaction (CLIHC 2017), Guatemala (2017)
Tidke, B., Mehta, R., Jenish Dhanani, J.: Consensus-based aggregation for identification and ranking of top-k influential nodes. Neural Comput. Appl. 32, 10275–10301 (2020)
RabbitMQ. https://www.rabbitmq.com/. Accessed 11 Aug 2020
ZeroMQ. https://zeromq.org/. Accessed 11 Aug 2020
Kafka. https://kafka.apache.org/. Accessed 11 Aug 2020
Pham, X.H., Jung, J., Hwang, D.: Beating social pulse: understanding information propagation via online social tagging systems. J. Univers. Comput. Sci. 18, 1022–1031 (2012)
Nguyen, H.D., Huynh, T., Hoang, S.N., Pham, V.T., Zelinka, I.: Language-oriented sentiment analysis based on the grammar structure and improved Self-attention network. In: Proceedings of 15th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2020), Prague, Czech Public (2020)
Kaas, R., Buhrman, J.M.: Mean, median and mode in binomial distributions. Stat. Neerl. 34(1), 13–18 (1980)
Wilson, E.B.: Probable inference, the law of succession, and statistical inference. J. Am. Stat. Assoc. 22, 209–212 (1927)
Acknowledgment
This research is supported by Vingroup Innovation Foundation (VINIF) in project code DA132_15062019/year 2019.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen, H.D., Nguyen, K.V., Hoang, S.N., Huynh, T. (2020). Design a Management System for the Influencer Marketing Campaign on Social Network. In: Chellappan, S., Choo, KK.R., Phan, N. (eds) Computational Data and Social Networks. CSoNet 2020. Lecture Notes in Computer Science(), vol 12575. Springer, Cham. https://doi.org/10.1007/978-3-030-66046-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-66046-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66045-1
Online ISBN: 978-3-030-66046-8
eBook Packages: Computer ScienceComputer Science (R0)