The modern reality is making people use the Internet more frequently in all areas of life. People are getting used to a fast lifestyle, this is why it is so urgent to find an effective online marketing tool for the new online generation. This will help companies better promote their products in the digital age.
Roman Rashydovich Tolstyakov and Natalya Vasilyevna Zlobina at the Tambov State Technical University wanted to build a predictive model to improve online video marketing.
Roman and Tolstyakov applied the synergetic approach to their study. They used a linear model to represent the view counts with the factors of days and the current status of the virus. Then, they classified the user and put them into 7 groups. Finally, they had a predictive model based on their 176 social network data and evaluation.
Results showed that the predictive model should be based on the concentration of the users. They also tested the efficiency of the predictive model using real-world data.
Roman and Tolstyakov also showed the likelihood of sharing when different kinds of people received links from various sources.
This paper gave a formalized model for predicting the virus development of the video. It showed ways to evaluate social activity online. The paper also suggested useful topics that can use the predictive model for companies in different industries. “The experiment shows highest activity correlates with the number of active and interested users. The experiment allowed us to verify the coefficients of the activity of each user group. In the future, we are planning to create the predictive model of the viral video life cycle with a focus on days where the activity occurred in the communication medium and the range of the criteria for each group of users.” The author concluded.
To read the full text of the study: https://sciresol.s3.us-east-2.amazonaws.com/IJST/Articles/2016/Issue-46/Article124.pdf
Tolstyakov, Roman & VasilyevnaZlobina, Natalya. (2016). Improving the Quality of a Viral Video Marketing Campaign with a Predictive Model. Indian Journal of Science and Technology, 9(46), 1-13.