1. Compare the effects of multi-channel, multi-content delivery

The same content, multi-channel delivery, you can find the target group to focus on, but also to determine which channel is suitable for what content, but also do not use the content of a channel, to understand the target group to find the preferences, so as to facilitate more focused output and optimization of the content

2. find the problem, timely adjustment and optimization of content

through the data comparison, title, picture, content, etc.

3. reaction to the effect of content promotion and provide decision-making reference

two content operation need to analyze the data

1. content production, content operation through the PGC editorial production content and UGC users to produce content mode Stronger content production capacity

③ Content release frequency: graphic update frequency = total graphic update volume per time cycle / time cycle

④ Content generation user volume: for community-based product content operators, in addition to focusing on the total volume of content release and release trends, we also need to focus on the amount of users involved in content production and user content production time. Release (weekly and daily observation cycle)

2. Content Exposure

3. Content Clicks: When users see the content they are interested in in the product, they will click on the content they want to read and consume it

①Content Clicks: The total amount of content shopped in a certain period of time, which embodies the number of people who are effectively served by the content, including both product users and non-product users, is the same as the amount of in-station exposure and the amount of non-product users, which is the same as the amount of in-station exposure and the amount of in-station consumption. users, with the station exposure and content publishing channels

② average number of clicks: average number of clicks on content = number of clicks on content / number of content

③ average content volume trend: in the same time period, the change in the volume of reading of your content, reflecting the quality of the content update whether to improve the content, content influence whether to accelerate the expansion of the content, as well as whether to have found the ④Content click-through rate: for a single piece of content, content click-through rate = number of content clicks/content exposure, reflecting the attractiveness of the content title to the user, and for content features, content click-through rate = number of content clicks/total UV of the feature, reflecting the penetration rate of the overall feature in the product

⑤Content reading times: PV content reading times represent the overall content advertising inventory, the higher the number of readings indicates that the future commercial value of this content feature will be greater

4. content reading

Users click on the specific content, they will spend time to read it, for the articles they like to read it, for the articles they do not like to give up, of course, the user can also be read more than one article in the product

Online time: the total amount of time a user spends reading content during a session, which is an effective indicator of content quality

Reads per capita: the number of times content is read/clicked on, reflecting the stickiness of the content, where the more reads per capita, the higher the stickiness of the content in the feature

Content production users: the number of users who read content, the more they read it, and the more they read it.

The amount of content production users: for a single piece of content reading data indicators, it is equal to the number of people to complete reading the article divided by the total number of people reading the article, reflecting the quality of content writing, usually the higher the rate of content completion of the reading rate, indicating that the higher the degree of user-friendliness

5. content comments

is the key to the interaction between the user and the content behavior, a large number of comments on the interaction means that the content In-depth, but also reflects the user of your content loyalty is higher

Content comment number: in the statistical time cycle, there has been a content comment behavior of the total number of users (de-emphasis, including out-of-site users)

Content comment rate: in the statistical time cycle, there has been a content comment behavior of the user of content to read the number of clicks on the proportion of the number of people (content comment rate = the number of content comments / content clicks)

Content comment rate: the number of content comments / content clicks on the number of people (content comment rate = the number of content comments / content clicks)

Content comment volume: refers to the total number of content comments in the content function in the statistical cycle, usually the higher the comment volume means the more users like the articles in the function

Content per capita comment volume: content per capita comment volume content comment volume/content comment number, per capita comment volume refers to the average comment volume of each commenting user in a certain period of time. comment interaction

Average content comment volume: average content comment volume = content comment volume/content graphic volume, the average content comment volume refers to the average level of content interaction in a certain time period,

Content comment volume trend: refers to the same time period inside your content consumer activity whether there is an increase in the number of comments upward trend shows that your content is more and more The highest content comment volume: the highest content comment volume refers to the best state of the content function in the past period of time in the graphic text reflects the content operation students in this period of time at the highest level

6. content sharing

Content forwarding: when the user fully recognizes the viewpoint of the article or because of some kind of benefit incentive, it will produce the content

Three how to optimize content operation through data analysis

Meng said: the course should be repeated to listen to, complete the homework to apply, to see if it is good in practice