Data analysis is to adjust the title, adjust the cover, adjust the content, adjust the details, adjust the keywords, adjust the release techniques, adjust the type of work, adjust the way of explanation and so on.
So, when doing the data analysis of Shake Shack, from which angles should you start?
First, the data analysis of their own video data
The release time, the incremental number of likes, the incremental number of comments, the incremental number of retweets, and other dimensions to collect and organize the data
Here you need to pay attention to two ratios, which will help you a lot in the later operation and optimization of short videos.
The first is 10:1.
Your video should increase one fan if it has 10 likes.
The second is 100:5.
That's 5 likes for 100 views, which should be considered a moderate figure, though relatively speaking many "Netflix" videos may have a higher ratio, perhaps 10 or more likes for 100 views. There are also videos that do not reach this ratio. If a video has 1000 views, according to the normal ratio, there should be at least 50 likes, but the actual number of likes is only 23, that is, more people watch it, but not many people like it. Then we can determine that this video content needs to be optimized to increase the number of likes.
Second, peer video data analysis
Concerned about the data of other peers, from their release time, the total number of likes, the total number of comments, and the total number of retweets, and other data to select peers to publish the best works, learn and summarize their successful experience. The main reason for this is to improve your work in terms of themes, scripts, filming techniques, post-production, and so on.
At the same time, we also need to pay attention to the other 2 data of peer accounts:
1, the number of fans:
By paying attention to the number of peer jitterbug fans, we can probably determine the degree of jitterbug competition in this industry.
If the number of fans is very small, and the peers are very many, it shows a problem - peers are not doing it, then you have more opportunities.
The good operation of the jittery voice, the work is good, the fans are very easy to rise up,
If there are a lot of peers, everyone's fans are basically less than a few tens of thousands, more to say that is hundreds of thousands, millions, and that shows another problem - this industry is particularly easy to rise up.
In the jittery voice did not do up, generally either the method of the problem, or the problem of the industry sector.
Remember, in the jittery voice, only look at the work, not the person, not the industry, not the field!
2, the number of daily increase in powder
We actually need to be clear about how many fans our peers probably increase every day. Because this can be judged by the daily increase in the number of powder how his work, whether it has a reference value.
? Of course, don't underestimate others because they don't achieve much in the early stages. Every peer, there are points that we learn.
Third, the popular video data analysis
In addition to focusing on the data of peers, you also need to pay attention to the data of popular videos.
Which songs are the latest and hottest BGM, and whether they are suitable for supporting in the upcoming product.
And what topics are on the hot list, whether it is possible to rub the heat and so on. All need to spend time to do data analysis.
Only after these analyses are in place, we operate more easily, because whether it is a peer, or popular on the video, people do well is a bit of strength, a bit of highlights, these need to focus on our analysis.
Just rely on their own feelings can not, must be practical, and then through the data feedback, it is clear that your ideas are in place, whether the details are in place, these only through the data can be real feedback
Everything by the data to speak, so simple.
So, what tools to use to do the data analysis of Shakeology?
Here is a grand recommendation optimistic data, both account management, video monitoring, popular original sound, popular video and other multi-search data as one platform.
Data analysis is to adjust the title, adjust the cover, adjust the content, adjust the details, adjust the keywords, adjust the release techniques, adjust the type of work, adjust the way of explanation, etc..
So, when doing data analysis of Shakeology, from which angles should you start?
First, the data analysis of their own video data
The release time, the incremental number of likes, the incremental number of comments, the incremental number of retweets, and other dimensions to collect and organize the data
Here you need to pay attention to two ratios, which will help you a lot in the later operation and optimization of short videos.
The first is 10:1.
Your video should increase one fan if it has 10 likes.
The second is 100:5.
That's 5 likes for 100 views, which should be considered a moderate figure, though relatively speaking many "Netflix" videos may have a higher ratio, perhaps 10 or more likes for 100 views. There are also videos that do not reach this ratio. If the number of plays of a video is 1000, according to the normal ratio, there should be at least 50 likes, but the actual number of likes is only 23, that is, more people watch it, but not many people like it. Then we can determine that this video content needs to be optimized to increase the number of likes.
Second, peer video data analysis
Concerned about the data of other peers, from their release time, the total number of likes, the total number of comments, and the total number of retweets, and other data to select peers to publish the best works, learn and summarize their successful experience. The main reason for this is to improve your work in terms of themes, scripts, filming techniques, post-production, and so on.
At the same time, we also need to pay attention to the other 2 data of peer accounts:
1, the number of fans:
By paying attention to the number of peer Jieyin fans, we can probably determine the degree of competition in this industry.
If the number of fans is very small, and the peers are very many, it indicates a problem - peers are not doing it, then you have more opportunities.
The good operation of the jittery voice, the work is good, the fans are very easy to rise up,
If there are a lot of peers, everyone's fans are basically less than a few tens of thousands, more to say that is hundreds of thousands, millions, and that shows another problem - this industry is particularly easy to rise up.
In the jittery voice did not do up, generally either the method of the problem, or the problem of the industry sector.
Remember, in the jittery voice, only look at the work, not the person, not the industry, not the field!
2, the number of daily increase in powder
We actually need to be clear about how many fans our peers probably increase every day. Because this can be judged by the daily increase in the number of powder how his work, whether it has a reference value.
? Of course, don't underestimate others because they don't achieve much in the early stages. Every peer, there are points that we learn.
Third, the popular video data analysis
In addition to focusing on the data of peers, you also need to pay attention to the data of popular videos.
Which songs are the latest and hottest BGM, and whether they are suitable for supporting in the upcoming product.
And what topics are on the hot list, whether it is possible to rub the heat and so on. All need to spend time to do data analysis.
Only after these analyses are in place, we operate more easily, because whether it is a peer, or popular on the video, people do well is a bit of strength, a bit of highlights, these need to focus on our analysis.
Just rely on their own feelings can not, must be practical, and then through the data feedback, it is clear that your ideas are in place, whether the details are in place, these only through the data can be real feedback
Everything by the data to speak, so simple.
So, what tools to use to do the data analysis of Shakeology?
Here is a grand recommendation for optimistic data, both account management, video monitoring, popular originals, popular videos, and other multi-search data as one platform.