How to do data analysis on WeChat Official Account? Share 6 key points!

In fact, the meaning behind the data is logic and reasoning. In other words, data is a tool that provides guidance for your work. Whether you are doing your job well or not, the data will tell you where the problem lies; if you want to achieve an operational goal, the data will tell you the way and method. Make good use of data and your work will get better and better.

Below are some data analysis methods I have summarized based on my work experience. I hope it will be helpful to you who are new to operations. Even if it allows you to begin to examine the importance of data analysis, then this article is valuable.

Data analysis methods

First, open the backend of the official account . Today, we will focus on analyzing the "Statistics" column on the left side of the backend.

1. Pay attention to the source

In the user analysis column, focus on all sources (you can pull down to see more channel sources).

Through data analysis of the sources, it can be concluded that the current number of new members of this public account mainly comes from: the public account name in the picture and text page, business card sharing, and scanning of QR codes. This data shows that you can focus on promotion on these channels in the future.

Data Reasoning:

Public account name in the picture and text page: follow-up guide in tweets.

Business card sharing: When making sales calls, sales personnel guide users to follow the official account.

Scan the QR code: Technical personnel or operation and promotion personnel have directed traffic on the promotion platform.

Data guidance:

1. It would be very painful for a public account to not have a fixed open source channel. Data analysis will clarify your open source channel.

2. Make rational use of open source channels and design them in a targeted manner, such as increasing publicity efforts in open source channels through activities and various operating methods.

This can avoid the loss of manpower, material and financial resources caused by unclear channel direction. At the same time, growth mechanisms can be designed on effective channels to achieve the goal of increasing users.

2. User attributes

Pay special attention to user attributes, where you can view the user's gender ratio, language distribution, etc.

Users' province distribution, city distribution, terminal distribution, and model distribution. (This is clear at a glance when you open the backend, no screenshots are provided)

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. All of these will help us to carry out targeted operations when creating content, cater to user preferences, and create content that users like.

1. Hourly News

In the data analysis of public accounts, a single picture or text is actually of little value, so we need to analyze 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 public account in 24 hours a day. Through the hourly report, you can see the traffic trend 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. (Highlight the key points)

Because I chose the public account traffic trend chart of the last month, the days 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 that during the period from 10th to 24th, when no articles were sent, the traffic nodes of the official account were like this.

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

This shows that the highest traffic points of the official account may be at 15:00 and 8:00. With this conclusion, we can adjust the timing of tweets. Don't make subjective assumptions or guess user habits based on your own personal habits. Data is the most important evidence.

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 that these data can be manipulated artificially. For example, if you organize an activity to encourage people to leave comments and like them, the data for this article will naturally be high.

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

A: Without any promotional activities, we can analyze the types of content that users like through reading numbers and forwarding rates, and continue to optimize on this basis.

B: In the case of activities, review the activities , analyze the data , summarize the advantages, find the shortcomings, and thus optimize the activity methods.

The menu bar of a public account can be positioned as the functional attribute of the public account. Make good use of the menu bar to set up a public account menu with a high click-through rate .

If it is a shopping-related public account, the menu bar is basically a shopping entrance. Here you can use the menu bar to classify the website's categories, publish coupons, shopping activities, etc., to facilitate users to shop directly.

For functional public accounts, you only need to embed the functions directly in the public account menu bar.

For other types of public accounts, set promotion priorities according to the needs of project development. It is best to focus on categories that you want users to see frequently and that can interact with users and increase user stickiness.

So the question is, how to do data analysis on the menu bar?

There are submenus in the menu bar of the official account. Reasonably design the content of the classified official account submenus, understand the real needs of users through the click rate of the menu bar, and then make corresponding adjustments based on the data.

It is recommended that when setting up the menu bar, the contents of the submenus should be classified at the same level, so that we can understand which category is more popular with users.

For example, operations: products, operations, marketing, promotion…

Music Education: Piano, Violin, Guitar…

Through classification, we investigate users’ interests, so as to better operate content and produce content that users are interested in.

Therefore, I personally suggest that the setting of the menu bar should be linked to the product development stage. I have seen that the menu bars of some public accounts have not been changed for a long time, or they are simply links to historical messages. Personally, I feel that it is a waste. Making good use of the menu bar can help us better understand user needs and help us with content operations.

1. Hourly News

The hourly data can be used to analyze the users’ concentrated access time. During this time period, we can better allocate the work of customer service staff. 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 understand the users’ main doubts or needs and prepare a good FAQ.

The main purpose of this part of data analysis is to improve customer service efficiency through FAQ.

As for the remaining interface analysis and web page analysis, they involve professional techniques, so I will not go into details here.

The data analysis conducted in the above four aspects can be summarized as follows based on its guiding significance for work.

User analysis: find key user attributes and main open source channels;

Graphics and text analysis: find key traffic points and summarize content rules;

Menu analysis: find out the user's interest points and rationalize the menu to fully integrate with product positioning;

Message analysis: find out the time when users visit the website in high concentration, make full use of keywords, and prepare FAQ.

Data analysis is something that runs through an operator's entire career. The most important thing about data analysis is to find patterns and use them for iterative work. An operator must have data analysis methods and awareness, because it has great guiding significance for operational work.

If you can follow the method I recommended today to do weekly and monthly data analysis, I believe you will be able to sort out your ideas quickly. Of course, there may be some potential clues and menus hidden in the data that can help you make things better, waiting for you to discover and explore.

What I’m sharing with you today is the data analysis of the official account. Later I’ll share with you methods of activity data analysis, course data analysis, etc.