How to find operational guidance to leverage user growth?

As an operator , you always need to keep in mind "three points": fulcrum, force point, and focus. Especially the person in charge of operational growth hacking , who is responsible for driving performance growth, cannot be careless at all.

Fulcrum: Data monitoring and analysis provide a reasonable basis for the team's trial and error and optimization;

Power point: With a fulcrum, the operations growth team will maximize the benefits of resource allocation and make every effort to overcome difficulties;

The key point: It is the object of our trial and error optimization, such as a page, a set of standardized processes, a functional point, or even a button or a marketing language.

This article mainly talks about the fulcrum, that is: how we can discover problems and find patterns through data analysis , and then convince the growth team or sister departments to fire in one direction, leverage growth, and maximize operational results.

In the process of dismantling and increasing knowledge in the "Han Li Late Night Chat on Operations" circle , how to find the "aha moment" of a product is a difficult problem, so I did a reasoning exercise. The most frequently mentioned case in the field of growth hacking is Twitter, so we use it to practice. The data are all simulated, and the actual situation will be extremely complicated. The main purpose is to understand the method. In addition, it is also recommended that you practice more according to this method outside of work, and you will make progress quickly.

Ahaha Moment: The moment when a product makes the user's eyes light up. It is the moment when the user truly discovers the core value of the product, that is, why the product exists, why they need it, and what they can get from it.

When users experience this moment, the probability of retention is high; on the contrary, the reason for user loss is that, first, the product itself has no value, and second, the product has valuable points but is hidden too deeply for users to find. Therefore, once we have found this value point (aha moment), we only need to optimize the product or operation strategy to guide users to reach this aha moment as quickly as possible, which will improve retention.

background

In the early days of Twitter , there was a serious problem with user retention . Many people registered and then left, but the small number of those who stayed became active users.

Then, Twitter's growth manager began to analyze the data, trying to find out why a small number of users stayed.

Through analysis, we get a conclusion (fulcrum): users who follow at least 30 people will become long-term active users.

How was this analyzed? Let’s use virtual data to deduce the process.

Reasoning process 1: Group analysis

Twitter groups users by the number of days they visit Twitter each month. As shown in the following table, users in a certain month are grouped by the number of days they visit, and the retention rate of each group in the second month is tracked (Note: if the amount of data is small, the number of days can be grouped)

The table looks complicated, but we can make it into a chart to make it clear at a glance:

Did you see it? 7 times is a turning point, and the retention rate tends to stabilize. Therefore, we can conclude that 90%-100% of people who visit at least 7 times a month will stay in the next month, which is a very high retention rate.

So, how big is the number of users who visit at least 7 times? Is it worth analyzing? See step 2.

Reasoning process 2: Determine whether it is worth analyzing

Re-divide users into three groups: "core users", "general users" and "silent users", and look at the proportions:

Core users: people who visit at least 7 times a month;

Silent users: People who abandon their site after their first visit.

General users: People who visit infrequently (other than those listed above)

Conclusion: About 20% of visitors are core users, which is worth studying. So, we start with the core users and see what common behaviors they have that are different from other groups.

Reasoning process 3: Correlation analysis

By analyzing the attention behavior of core users, we found that:

1. Most of them follow around 30, as shown in the figure below (for the sake of convenience, we assume it is 30 and the number of users is very large). 30 seems to be a turning point.

2. The user activity level (generally divided into high, medium and low) is related to the 30 people they follow back. Surprisingly, the highest retention rate is achieved by only 1/3 of the users they follow back, as shown in the following figure:

So, why is this?

Friends who do data analysis know that data can only tell us what happened, but not why it happened, so the reasons behind it require research or interviews. This is also why I chat with users in particular.

Reasoning process 4: Finding causal relationships

Twitter discovered the reason through a telephone interview (I won’t post the interview script, you can use your imagination):

Why would users who follow 30 people stay?

Only after you follow 30 people will there be continuous content to read in your information stream .

Why do only 1/3 of the users who return to our site stay?

• If more than 1/3 of the followers follow back, then Twitter is a social network (similar to Moments )
• If less than 1/3 of the followers follow back, then Twitter is a news website (similar to NetEase and Sohu News clients )
• Only when the ratio reaches 1/3, Twitter will have unique value → allowing people to keep abreast of the latest news in the circle.

So, Twitter found the fulcrum of 30 and the positioning number of one-third, and used "the number of users following others" and "the number of users being followed" as the two major growth levers. It clarified the direction of growth testing and directly entered the most thrilling trial and error optimization work in the field of growth hacking.

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

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