[SQL is required for some product managers and data product managers]

Say first conclusion

  1. No matter what kind of product manager, at least simple understanding of SQL, can read simple SQL code.
  2. For data, policy, OR AI products, writing SQL is a must.
  3. The good news is that SQL learning is cheap, cost-effective, and a lifelong skill that can be learned in a week.

Why do product managers need to know SQL — a new dimension of business capability growth

1. When we need to check the data, the technical staff is not enough, and it is always on schedule. Not as good as to read only permission oneself stem, take a number analysis dragon.

2. Behind the product logic and technology practice is the database design. The product SQL makes it easier to understand the operation principle of the technology, which enables me to think at a higher level and communicate with the technology more calmly.

3. Speaking data is the best way to prove the value of your work. Designing experiments and focusing on data are the strengths of the product. Grasp the basic syntax of SQL, work efficiency doubled.

In what scenarios does the product need to write SQL

Take an example from an interest community project I worked on. User growth was booming, and what about retention for those users that were acquired through precise channels? Further down, what is retention related to product design? I was wondering every day, what behaviors lead to high retention, how to design a product so that new users can accomplish those behaviors, and how to design features that facilitate those behaviors, can retention really be improved?

I came up with all kinds of experiments, so let me just give you some examples of A&B for you to understand. (The actual situation is not so straightforward. Often multiple factors lead to multiple results, and the ability of a product is to abstract the key points from the many factors.)

  • The relationship between the number of individual posts and retention
  • The relationship between the number of likes a user receives and retention
  • The relationship between the number of comments received by an individual user and retention
  • The relationship between post length and retention among users who post

Our team was not large at that time, and the technology did not have much time to assist the product to complete the work. We had to take the numbers by ourselves through SQL and observe the correlation between user behaviors.

Let me give you a concrete example

For example, in a content community app, we looked at the data and found that a newly registered user who posted on the same day had a 20% increase in seven-day retention compared to a user who didn’t post on the same day.

Based on this data, we can design a “novice task” function to promote new users to post. Let the new registered users, registered to complete the novice task, promote the first Posting.

So this kind of “novice task” promotes the Posting of users, users actively post, users do not post three types of users, how is the seven-day retention data? Is retention at least higher than non-posting users?

Even such a simple product design experiment, the general data analysis tool is difficult to be flexible statistics, not to say the actual experiment is much more complex than this. We write our own SQL, which is much more flexible. Get the results we want faster, and prove the value of our work better.

Learning SQL is very simple

Not every enterprise has developed its own data analysis tools or deployed and purchased third-party analysis tools. Even with the tools available, it is difficult to meet the ever-changing needs and erratic logic. We designed the test experiment, the company’s analysis tools may not be able to meet the needs. If you can grasp the data and analyze the data according to the experiment we designed, you will get twice the result with half the effort.

Basic SQL statements are not that complicated, so I must recommend “SQL Must Know”, which can be read in a few hours.

Understand basic statements and what SQL can do. Then see what data you want to take and try to write it in SQL. You learn best by doing.

For example, if you are the product of a content community, try writing a new user who registered on the same day and posted on the same day, compared to a new user who didn’t post on the same day and their next-day and seven-day retention.

As long as you can write SQL Karacloud can quickly build any enterprise internal tools

Here must recommend the Kara-cloud, Kara-cloud can quickly build a set of data Kanban, as long as you can write SQL, you can design experiments at will, take the number for comparison.

Recommended reading

  • User management system – User rights design from entry to mastery
  • How to design the financial account checking system — From 0 to 1 to build the actual operation of account checking system
  • How to design a great backend prototype?

Jiang Chuan, co-founder of Kalayun, b-side product manager, focuses on the implementation and construction of enterprise internal efficiency tools.