The first step: first classify the claims made by the business openly and privately

For each type of problem, construct analysis hypotheses, convert business reasons into data logic, and let the data speak for itself .

Step 2: Eliminate excuses first

Let everyone focus on. Excuses often arise from: macro factors, external factors, and teammate factors. So here, the key is falsification. As long as their excuse for escape can be overturned. The best way to prove falsification is to give examples. It is also raining, so why can others resist it? Traffic is also difficult to obtain, so why can other business lines continue to grow?

The third step is to solve the white rhinoceros and eliminate obvious major impacts

For example, regulatory policies, company strategies, Factors such as major external environment will indeed play a major role in business operations, and these factors can only be accepted by ordinary small employees and cannot be changed. But! This important factor is reflected in data and has strict requirements.

Step 4: Solve black swans and eliminate obvious emergencies

If a true emergency occurs, it is easy to find the source of the problem. Positive: promotional activities, commotion among a certain group of users, new products launched? Negative: bad weather, emergencies, system downtime? Therefore, first eliminate single emergencies, find out the reasons, and then trace back to the previous ones. The situation is easy to explain clearly.

Step 5: Lock down the problem points according to the division of labor and then discuss the details

After solving the big problem, if you want to discuss more detailed issues, you have to lock down the department and appoint people first before discussing the plan. It has been shared before, so I won’t go into details here.

Step 6: Lock in the details

Please note that even if you focus on one action of a department, it is still difficult to figure out what business reasons caused the problem. Because in business itself, various factors are intertwined and it is difficult to separate them.

The editor of Qingteng will share with you here about how to do multi-dimensional data analysis. If you have a strong interest in big data engineering, I hope this article can help you. If you want to know more about the skills and materials of data analysts and big data engineers, you can click on other articles on this site to learn.