Data Analysis of Fizzo Free Novels

1: Fizzo volume mechanism Tomato volume , as the name suggests, means that the official fizzo official pushes a certain novel work through an intelligent algorithm of the whole app platform and pushes it to the

1: Fizzo volume mechanism

Tomato volume , as the name suggests, means that the official fizzo official pushes a certain novel work through an intelligent algorithm of the whole app platform and pushes it to the user’s mobile phone, thus using the professional name of Douyin Kuaishou to call it the display volume.

Impression push position (men and women frequency homepage, search page, category page, new book page, list page)

And how much this display is, only the Tomato system itself knows, because it is intelligent push, intelligent acquisition of traffic pool, intelligent acquisition of user behavior interests and habits.

If you study from the heavy volume mechanism, there are probably the following orders of magnitude

5,000 impressions, 10,000 impressions, 50,000 impressions, 100,000 impressions, 200,000 impressions, 500,000 impressions, 1 million impressions, 5 million impressions, 10 million impressions, 50 million impressions

However, the number of impressions is proportional to .

Tomatoes can be triggered in two ways:

1: Active volume (meaning that the work reaches a certain number of words, and the volume mechanism is actively triggered)

2: Passive volume (meaning that some data of the work reaches certain conditions to trigger the volume mechanism)

Example of heavy volume logic:

A restaurant has 100,000 members. A senior chef came to this restaurant and made a new dish. The restaurant will take this dish for a free taste test.

From 100,000 members, 10,000 ordinary users are selected to carry out omni-directional, omni-channel method, push information notification, and the free period lasts for one week.

2000 people received information on the first day (500 people are interested, 1000 people are not interested, 500 people are interested but have no time)

On the second day, 5,000 people received information (2,000 people are interested, 1,000 people are not interested, and 2,000 people are interested but have no time)

On the 3rd day, 3000 people received information (1500 people are interested, 500 people are not interested, 1000 people are interested but have no time)

3 days have passed, the actual number of people who came to the store for free tasting is: 4,000 people, 2,500 people are not interested, and 3,500 people (for potential users)

The free event date has not ended, and the restaurant once again sent information notifications to these 10,000 users.

The 4,000 people who have tasted it for free have filtered this information.

2,500 people were not interested, and when they saw the information notification again, they were still not interested.

3500 people who are interested in users who have no time

500 people came on day 4 (3500 people had no time or interest)

1000 people came on the 5th day (2500 people had no time or interest)

200 people came on the 6th day (2300 people had no time or interest)

100 people came on the 7th day (2200 people had no time or interest)

Summary: On a 7-day date, the total number of people who came to the store was 5,800, and users who were not interested or had no time: 4,200.

Attraction rate of dishes: 58%.

Dishes rejection rate: 42 percent

Among the 5,800 people who tasted the dishes: 3,200 people reported that the dishes were very good, 1,000 people thought it was average, and 1,600 people thought it was not good

The conversion rate of excellent reviews of dishes: 55%

Good conversion rate for dishes: 17 percent

The conversion rate of negative reviews of dishes: 27%

Finally: The restaurant got various data reports and saw that the data expressed by this dish fits the range of the main dishes. It is planned to start from 90,000 new users, and then draw 50,000 new users to continue pushing.

The above analysis only expresses the logic of personal analysis. If there is any objection, you can reply to the discussion. There will be more data analysis posts in the future.

No. 1: Tomato Volume Mechanism

No. 2: Tomato scoring mechanism

No. 3: The difference between the number of readings in the background of the tomato author and the number of readings on the cover page of the book

No. 4: Tomato author income

No. 5: The importance of suction

No. 6: think of it again

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