How to use public account data? It is enough to analyze these 4 points!

The WeChat public account backend contains detailed data on tweets, fans, etc., but many operators are not quite sure how to analyze them. The author of this article explains in detail how to analyze and utilize WeChat public account data from four aspects: users, pictures and texts, menus, and messages.

I remember one time, I asked a senior who was experienced in the new media industry on WeChat: "Teacher, what kind of data does WeChat public account mainly analyze?"

Then haha! The teacher ignored me.

Actually, it is understandable. You have not paid tuition and you don't have any status. Why should they use their time to answer your questions?

So I had to study it myself. I also read some articles on how to study public account data, but I always felt that the benefits were not great.

For someone who is completely insensitive to data, looking at the densely packed numbers is simply confusing.

what to do? Who told us we are in operations?

The question is, how to analyze the public account data

I remember that my leader assigned me the task of analyzing the WeChat background data every week and submitting the data into a weekly report.

So the weekly reports submitted at that time were generally written like this: articles, number of readings, number of new followers, number of unfollowers, and total number of fans.

Through some simple methods, copy and paste the background data and write out the data that everyone can see .

Is it called data analysis?

Obviously, such data analysis cannot help us at all, and such data work is meaningless.

So what should we do to use limited data to analyze the key points behind it? Your boss and peers won’t teach you this. You can only accumulate experience on your own or take paid courses.

Of course, what I want to say now is just a summary after working for a period of time, which may not be correct. I hope it will be inspiring to you who are new to operations.

First, open the background of the WeChat official account, and you will see a lot of data. We focus on the statistics column on the left side of the background:

1. User Analysis

1. Pay attention to the source

In the user analysis column, mainly look at the source of attention:

Through the analysis of this data, I concluded that the current new members of this public account mainly come from two modes: business card sharing and scanning QR codes. This data represents your open source channels .

Speculation:

• Business card sharing: When selling, sales personnel can guide users to follow the official account.
• Scan the QR code: Technical staff and e-commerce operators have directed traffic to the website.

Therefore, there are many new users through these two channels.

Know how to use this data:

1. Make your open source channels clear. It will be very painful for a public account to not have a fixed open source channel.
2. Make rational use of open source channels, design them in a targeted manner, and increase publicity efforts on the original basis through activities, e-commerce operations, and various other means.

This can save manpower and material resources, design growth mechanisms based on effective channels, and increase users.

2. User attributes

Pay special attention to user attributes, which play a guiding role in content operations .

Here you can view the gender ratio and language distribution of users:

The user's province distribution, city distribution, terminal distribution, and model distribution: (This is clear at a glance when you open the backend, so I won't take screenshots one by one)

If the data obviously favors a certain group of people based on gender and region, targeted marketing can be carried out when creating content. For example, there are more women than men, and more people in the north than in the south.

These all help to roughly cater to user preferences when creating content.

2. Graphical Analysis

1. Hourly News

In the data analysis of the official account, a single picture and text is actually of little value, so we have to click on all the pictures and texts.

Open the hourly report, this is the data we want to focus on analyzing. The hourly report represents the traffic of your official account in 24 hours a day. Through the hourly report, you can see the traffic trend chart of your official account.

This data is worth collecting carefully, at least on a monthly basis.

In the process of analyzing changing data, we must first identify the constants and then use variables to compare them in order to find the patterns.

Because I chose the public account traffic trend chart for the days in the past month when no posts were made. On days when posts are published, the traffic trend graph will artificially lean toward a certain node, making the analysis less meaningful.

This is the approximate data of my analysis of a stage. I first observed the data for 24 hours every day and found the key traffic points of each day.

Mark the key points that appear every day and make a table.

You will see:

During the period from 10th to 24th, when no articles were sent, the traffic nodes of the official account were as follows:

18 o'clock appears 4 times, 15 o'clock appears 6 times, and 8 o'clock appears 6 times.

This means that the highest traffic points of your official account may be at 15:00 and 8:00.

Find out the traffic points of your official account, and then you can adjust the tweet time. Don’t rely on your subjective will to think about when users are free to read WeChat and when is the best time to post.

Use data to find traffic points, then test using traffic points to find the most appropriate time to tweet.

2. Reading volume

The analysis of reading volume mainly includes: reading volume, forwarding volume, likes, comments, etc. The reason why these are not discussed in detail is mainly because they are controllable by humans. For example, if you organize an activity to encourage people to leave comments and like them, the number of views, likes, and comment rate of this article will naturally increase.

Therefore, when doing content analysis, it is necessary to make clear the unity of constants and conduct analysis:

• Without any activities, we can analyze the types of content that are popular with users through the number of readings and forwarding rates, and continuously optimize the content.
• In the case of activities, by analyzing the data , we can find out the types of activities that are popular with users, the shortcomings of the activities, how to improve the activities, and optimize the activities.

In short: when analyzing data, it is necessary to analyze variables based on unified constants and conduct large-scale analysis to be valuable.

3. Menu Analysis

This column in the menu of the official account can be positioned as the functional attributes of the official account.

If it is a shopping-related public account such as JD.com , Vipshop , and Happy Cake, the basic menu bar is a shopping entrance.

• Shopping public accounts: This menu bar can be used to classify the categories of the website, making it easier for users to shop.
• Functional public accounts: just bind the function directly to the public account menu bar.
• Other types of public accounts: Based on your personal preferences, set what you think is important, what you want users to see frequently, and what can create stickiness with users. It is best if the menu is functional.

Now the question is, how to use the data analysis in the menu bar?

There are submenus in the menu bar of the official account. Reasonably design and classify the contents of the official account submenus. Through the click rate of the menu bar, you can understand what users care about and what they care about, and make corresponding adjustment plans.

It is recommended that when setting up the menu bar, the contents of the submenus can be classified at the same level, so that it is easier for us to understand which category of products is more popular with users.

for example:

• Clothing: women's clothing, men's clothing, children's clothing...
• Literature: Emotional column, story column, current affairs column…

By classifying products, we can investigate what users are most interested in, so as to better operate content and produce content that users are interested in.

Therefore, I personally recommend that the menu bar should be used in conjunction with the product so that it can play its full role. I have noticed that the menu bars of some public accounts have not been changed for a long time, or they are simply links to historical messages. I personally feel that this is a bit of a waste.

If used properly, the menu bar can help us better understand users and improve content operations.

4. News Analysis

1. Hourly News

In terms of news analysis, let’s first look at the hourly report.

If it is a service-oriented public account such as finance, investment and financial management , you can use the hourly report to find the user's concentrated access time, and better allocate customer service personnel during the user's concentrated access time. The specific analysis is consistent with that mentioned above.

2. Message keywords

Regarding the message keywords, this is where we should focus our attention.

Through keyword analysis, we can find out the main doubts of users, prepare FAQs, and efficiently save customer service work through FAQs. This is the main purpose of our data analysis in this part.

As for the remaining interface analysis and web page analysis, Bei Xiaoxiao is not a technical professional and cannot analyze them at the moment, so I will not elaborate on them.

V. Conclusion

To sum up, I think that as a public account operator , if you can analyze the above four aspects of data well and analyze the data in each aspect thoroughly, you can basically be considered qualified and good. This way, when we make analytical reports in the future, we don’t have to just write down the number of readings, number of fans, number of new followers, number of unfollowers, and other superficial information.

Data analysis mainly starts from four aspects:

• User analysis: find key user attributes and main open source channels;
• Graphics and text analysis: find key traffic points and identify content patterns;
• Menu analysis: find out what users are interested in and rationalize the menu and fully integrate it with products;
• Message analysis: find out the time when users visit frequently, find keywords, and prepare FAQ.

The most important thing about data analysis is to find patterns and use them for iterative work.

The above sharing is over. I wish we can go further and further and become more and more successful on the road of new media operations !

The author of this article @北小小 is compiled and published by (Qinggua Media). Please indicate the author information and source when reprinting!

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