ANLY482 AY2016-17 T1 Group1: PROJECT FINDINGS/Video
For the video analysis, we will exclude the videos shared by Facebook users to SGAG’s timeline as it is not part of the performance measurement of SGAG’s video posts. We will examine the performance of click-to-play and auto-play video by the length of video view. There are 3 different measurements for the length of video view:
- “Video view” in which a video was viewed for more than 3 seconds
- “30-seconds view” in which a video was viewed for more than 30 seconds or to the end, whichever came first
- “95% views” in which a video was viewed to 95% of the video length
Top Video Posts for Auto-Played
Figure 4.14 shows the top posts for auto-played video based on number of times the video was played, the top post for video view (left), 30-seconds (middle) and 95% view (right).
The video of our Prime Minister, Mr Lee Hsien Loong has the highest number of played times at 687,560. The top video for 30-seconds view shows a helpful foreign worker in removing a fallen tree and this video has been played for 341,146 times. The video of parkour depicting a climbing manner down in a carpark, and it has been played for 293,602 times.
The theme for the 3 videos are as follow: the video of PM Lee is funny and cheering, the second video shows a helpful and kind foreign worker and the parkour video shows a training technique aiming to overcome obstacles which is trending among the youngsters. Although the theme appeared to be different, the length of the 3 videos were all at around 40 seconds.
Top Video Posts for Click-to-Play
The 2 posts shown in Figure 4.15 were identified as the top posts championing the number of played times, for video view, 30-seconds view as well as 95% view. The video depicting “cool” parkour tricks and it was played 205,961 times, 178,346 times and 153,713 times for video view, 30-seconds view and 95% view respectively.
The video of Christian Lee (on the right) also attained high number of plays across the 3 different views measured. The video of our Prime Minister, Mr Lee Hsien Loong was another video with higher number of plays for video view and 30-seconds view. The 3 top videos identified share common similarities, for which the 3 posts are related to current happening or trending in Singapore, as well as Singaporean spirit and pride.
Video Retention Rate
Video Retention Rate in our analysis is described as the percentage of video viewed on average. The measurement of average video view gives an overview of audience retention on a specific video post. This information provides SGAG a better understanding of video length to audience retention.
In our analysis, we found out that on average, videos shorter than 35 seconds are viewed for longer. However, this figure only serves as a gauge to an “optimal” video length based on audience’s attitude. The actual performance of a video still largely depends on the interesting content of the video.
Video Retention for Top Video Posts in Auto-Played and Click-to-Play
Of the top video posts identified in Auto-Played and Click-to-Play, we would like to examine whether the length of the more popular videos result in longer audience retention.
Our analysis in Figure 4.16 shows that there is no correlation between the popularity of a post and average view length. A post may have very high number of plays but low average video view if video length is too long. This can be seen from the highlighted part of Figure 4.16, the 2 popular posts with longer video length have lower average video view. On the other hand, posts with shorter video length have lesser gap between the average view and its actual length.
Video Posts with Most Engagements
Figure 4.17 shows the 3 video posts with most engagement. The post showing Chinese new year greetings remix of our Prime Minister, Mr Lee Hsien Loong has the highest number of engagement at 380,919 followed by Christian Lee post with 345,529 and parkour video with 312,119. The top 3 video posts as mentioned above, are more related to the current trends in Singapore or Singaporean’s pride. As a result, they are discussed among the audience.
Video Posts with Most Viewership
Viewership in our analysis is defined as the percentage of impressions that translated into views (total video views/total impressions generated). In analysing the performance of videos, it is crucial for us to measure the conversion percentage as it allows us to analyze the factors that impacts the percentage of users who decide to watch the video. These factors may include post message, preview thumbnail and content at the beginning of the video.
The 2 posts shown in Figure 4.18 were the top posts with most viewership in both auto-played and click-to play category. For auto-played category, the video (on the left) that features beer pong challenge has a viewership of 76.15%. As for click-to-play, the video was about transporting of MRT train carriages and it achieved a viewership of 85.81%.
The content at the beginning of the beer pong video plays a huge role in the viewership conversion, the game was entertaining and exciting at the beginning, that it is able to retain its audience. Meanwhile, the title of the second post which is “What the!!! Is this really how MRT train carriages get transported around??” was attractive and relatable to most audience, that it attracted the audience to watch the video and find out how are their daily commuting transportation being transported around.
Video posts with Viewership for 30 Seconds
After finding out the viewership for each video, we analysed the number of viewers that watched 30 seconds of the video or watched till the end of the video. With this, we can have a rough idea on how engaging the video content is, that it drives the audience to continue watching the video.
The 2 posts shown above in figure 4.19 represent the top video posts with 30 seconds viewership for both auto-played category (left picture) and click-to-play category (right picture). Top video for auto-played category which shows cracking of eggs achieved a viewership of 89.69% while that for the click-to-play category which features train carriages transportation gained a viewership of 85.81%.
Problem Definition
In measuring the performance of SGAG’s video posts, number of unique Facebook users who view the video is used. However, SGAG is unsure of which factors to focus on to increase the number of video viewers. With the number of unique video viewers on Facebook as the KPI, several potential factors that may influence the number of video viewers have been identified.
Analysis Methodology Multiple Linear Regressions in JMP can be achieved by using the Fit Model function by selecting the response variable (Y axis) and the explanatory variables (X axis) which explain the changes in response variable. In this case, we would like to see how number of video viewers changes as a result of change in the potential factors, thus video viewers is the response variable on Y axis and other factors in the X axis. As video length, time, day of week and public holidays are categorical variables, it will be added into the model only when the multicollinearity has been eliminated from the model.
From figure 14, we can see that 93% of variability of the response variable can be explained by the explanatory variables. Although the higher the R-Square value, the more accurate a model is. We will attempt to examine the multicollinearity issues among these factors before finalizing the model.
From Figure 15, we can see that variable Lifetime Post Consumers by Clicks to Play, Report Spam per thousand user and the Lifetime Post Stories by Like, Comment and Share have a VIF over 8. Next, we will perform Clustering Analysis to eliminate multicollinearity.
In Figure 17, it is shown that Lifetime Post Stories by Share is the chosen explanatory variable which have the lowest intra distance among the variables in cluster 1 given only one cluster in this case. Hence, we will remove variable Lifetime Post Consumers by Clicks to Play, Report Spam per thousand user and the Lifetime Post Stories by Like and Comment and rerun the model again:
As seen in Figure 18, there are no more explanatory variables with VIF over 8. As we are using 95% confidence interval, explanatory variables with Prob>|t| more than 0.05 are considered insignificant to explain the response variable. Variable No of negative feedback per thousand users, Hide All Clicks per thousand user and Hide Clicks per thousand user will be removed from the model as they are least significant in explaining the changes in Video Viewers.
At the same time, categorical variables such as video length, video originality, time category, day of week, and public holiday will be included in the model to evaluate its significance to explain changes in Video Viewers.
Results
This model can explain about 92% of the variability of the response variable (Video Viewers) can be explained by the various independent variables (Figure 20). All the explanatory variables in the final model have VIF value less than 8 and have a p-value (Prob>|t|) of less than 0.0001 which indicates a strong model (Figure 21).
The equation below illustrates the relationship of the explanatory variables to Video Viewers: Video Viewers = 2.3761 (intercept) - 0.1920 (Log[Unlike page per thousand user) + 0.1019 (Log[1-Lifetime Post Stories by Share]) + 0.7311 (Log[Lifetime People Who Have Liked Your Page and Engaged with Your Post])
To further examine the effects of the independent variables on the dependent variable, the equation which consists of values which were previously transformed by logarithm function is converted back to the original values through the use of exponential function. Through the profiler in Figure 22, we will be able to see the degree of linearity of each independent variable towards the dependent variable, as well as the degree of sensitivity of the response variable to the adjustments of the explanatory variables.
