Modeling the function of advertising reviews from media ads on the YouTube channel
-
Author(s)Tetiana BludovaLink to ORCID Index: https://orcid.org/0000-0001-7011-5645
,
Andrii Chuzhykov
,
Kateryna Leshchenko
-
DOIhttp://dx.doi.org/10.21511/im.15(3).2019.03
-
Article InfoVolume 15 2019, Issue #3, pp. 26-41
- Cited by
- 823 Views
-
151 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
The use of display advertising enables companies to reach target audience and show them your ads on specific sites, which increases sales. In particular, YouTube video ads can reach a wide audience and attract many customers. One of the most important issues in the development of display advertising is the analysis and modeling of advertising reviews from media advertisements and practical tools with the aim of taking
synergistic effects of advertising reviews. The purpose of the paper is to research the synergistic effects of advertising reviews in the system of successive advertising display. The study used a statistical analysis of empirical data, in particular statistics of annotations of video clips demonstration; viewing a video clip, and also approaching ad rotation time series using the Fourier series. The synergistic effects of advertising reviews in the system of successive advertising display are revealed. The approximation of time series of advertising reviews in the synergistic system of successive advertising display is presented. The analytical periodic function of advertising reviews, which takes into account manifestation of the short-term current effect of video advertising messages, is developed. The functions of advertising reviews in the Fourier series are presented, which allows to simulate video advertising messages as a reaction in the form of revision annotations in media advertising.
view full abstract
- Keywords
-
JEL Classification (Paper profile tab)М300, M390
-
References25
-
Tables3
-
Figures15
-
- Figure 1. Structural chart of the production process from launching the video advertising-to-advertising company report
- Figure 2. Level of annotations completion for all types of video advertising launch
- Figure 3. Annotation statistics of video clip demonstration for 21 days of browsing for various network connection devices over the Internet
- Figure 4. Dynamics of annotations in video clips demonstrations during the period of December 16, 2017 – January 5, 2018
- Figure 5. Gender peculiarities of viewing a video clip for 21 days for people aged 18-24 years
- Figure 6. Gender preferences of viewing video clip during 21 days for observation by persons aged 25-34
- Figure 7. Analysis of length of viewing a video clip for 21 days for mobile phones and tablet PCs
- Figure 8. Analysis of length of viewing a video clip for 21 days of observation for computer and TV
- Figure 9. Analysis of duration of viewing advertising content of a video clip during 21 days of observation on YouTube
- Figure 10. Analysis of video clip views during 21 days of observations on various YouTube pages
- Figure 11. Effects of advertising reviews (dashed lines) in the In-Stream advertising system
- Figure 12. Effects of advertising reviews (dashed lines) in the TrueView Video Discovery (In-Display) advertising system
- Figure 13. Synergistic effects of advertising reviews (dashed lines) in a consistent advertisement broadcasting system: In-Stream and TrueView Video Discovery (In-Display)
- Figure 14. Approximation of time series of advertising reviews (dotted lines) in the system of successive advertising: In-Stream та TrueView Video Discovery (In-Display) (the OX axis is time, and on the OY axis – views)
- Figure 15. Visualization of 9 additive components of function (8)
-
- Table 1. Video advertising effects
- Table 2. Benefits of video advertising on the YouTube channel
- Table 3. Statistics of annotations in video clip screening for the period of 21 days of browsing
-
- Dilley, J., Maggs, B., Parikh, J., Prokop, H., Sitaraman, R., & Weihl, B. (2002). Globally distributed content delivery. IEEE Internet Computing, 6(5), 50-58.
- Eelen, J., & Seiler, R. (2015). Creative media use increases online sharing of your ad (but seems less effective for your brand). Advances in advertising research, VI, 291-308.
- Florin, D., F., Vyas, S., Asad, A., Ion, S., Dilip, J., Aditya, G., Jibin, Z., & Hui, Z. (2011). Understanding the impact of video quality on user engagement. Proceedings of the ACM SIGCOMM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 56(3), 362-373.
- Fumi, A., Pepe, A., Scarabotti, L., & Schiraldi, M. (2013). Fourier Analysis for Demand Forecasting in a Fashion Company. International Journal of Engineering Business Management, 5.
- Giles, D. (2010). Psychology of the Media. London: Palgrave Macmillan.
- Hilmes, M. (2011). Institutions: conflict and change. In The television history book (pp. 50-67). London: British Film Institute.
- Hollander, M., Wolfe, D., & Chicken, E. (1973). Nonparametric statistical methods (3rd ed.). New York: John Wiley and Sons.
- Howkins, J. (2007). The creative economy. How people make money from ideas. London: Penguin Books.
- Hutter, K. (2015). Unusual location and unexpected execution in advertising: A content analysis and test of effectiveness in ambient advertisements. Journal of Marketing Communications, 21(1), 33-47.
- Krishnan, S. S., & Sitaraman, R. K. (2014). Understanding the Effectiveness of Video Ads: A Measurement Study.
- Küng, L. (2009). Strategic management in the Media. From Theory to practice. London: Sage Scannel, Paddy. Media and Communication. London: Sage.
- Larson, R. C. (1987). Perspectives on queues: Social justice and the psychology of queueing. Operations Research, 35(6), 895-905.
- Lehmann & Romano, J. (2005). Testing statistical hypotheses. Springer Verlag.
- Lin, C., & Kim, T. (2016). Predicting user response to sponsored advertising on social media via the technology acceptance model. Computers in Human Behavior, 64, 710-718.
- Liu, X., Dobrian, F., Milner, H., Jiang, J., Sekar, V., Stoica, I., & Zhang, H. (2012). A case for a coordinated internet video control plane. Proceedings of the ACM conference on Internet measurement conference, 359-370.
- Nelson, R. (2009). Analysing TV Fiction: How to Study Drama. In Tele – Visions an introduction to Studying Television (pp. 74-92). Glen Greeber. London: British Film Institute.
- Nygren, E., Sitaraman, R. K., & Sun, J. (2015). The Akamai Network: A platform for high performance Internet applications. ACM SIGOPS Operating Systems Review, 44(3), 2-19.
- Oktay, H., Taylor, B. J., & Jensen, D. D. (2010). Causal discovery in social media using quasi-experimental designs. In Proceedings of the First Workshop on Social Media Analytics. In Proceedings of the ACM conference on Internet measurement conference, 1-9.
- Pierre, Guillet de Monthoux. (2013). The Art Firm: Aesthetic Management and Metaphysical Marketing. Kyiv: Companion Group.
- Rauwers, F., Remmelswaal, P., Fransen, M., Dahlén, M., & van Noort, G. (2018, August 31). The impact of creative media advertising on consumer responses: two field experiments. International Journal of Advertising: The Review of Marketing Communications.
- Reijmersdal, E., Rozendaal, E., & Buijzen, M. (2012). Effects of prominence, involvement, and persuasion knowledge on children’s cognitive and affective responses to advergames. Journal of Interactive Marketing, 26(1), 33-42.
- Scannell, P. (2008). Media and Communication (303 p.). Paddy Scannell. London: SAGE.
- Shunmuga, S., & Sitaraman, R. K. (2011). Video stream quality impacts viewer behavior: inferring causality using quasi-experimental designs. In Proceedings of the ACM conference on Internet measurement conference (pp. 211-224).
- Shunmuga, S., & Sitaraman, R. K. (2013). Understanding the Effectiveness of Video Ads: A Measurement Study. ACM Internet Measurement Conference (IMC). Barcelona, Spain.
- Stádník, B., Raudeliuniene, J., & Davidavičienė, V. (2016). Fourier Analysis for Stock Price Forecasting: Assumption and Evidence. Journal of Business Economics and Management, 17, 365-380.
