This time, we will talk about how product managers encounter data abnormalities, how to do data analysis and find out the reasons. Once, as soon as I went to work on Monday, the leader asked, why did our App DAU drop by 30% this weekend? At that time, I had no experience, and I was in a hurry. I looked at the data, and looked for technical and consumer email list operational discussions. Fortunately, I found the reason. At work, product data anomalies occur from time to time, and interviews are often asked. In particular, changes in core data (such as new additions, daily active DAUs, orders, income, etc.) are the focus of our attention. Once the data is abnormal.
In this case, if you have no experience, do not know how to deal with it, and sometimes toss for a long time, you may not be able to find the reason. Later, I was in charge of the data statistics platform, often helping product managers and operational consumer email list analysis to answer various data changes. This gave me experience in data analysis. To analyze the abnormal situation of this kind of data, the conventional practice can be summed up in three steps. 1. Check common problems and confirm the accuracy of data The first step is to troubleshoot common problems to ensure that the data is accurate, and it makes sense to analyze it.
These problems are mostly caused by version updates , changes in statistical methods , and service failures . Therefore, you can first see if there are any updates to the front and back ends of the product. If there are updates, you have to check with consumer email list development and testing colleagues to see if these updates will affect the data. Secondly, find out whether the data is accurate and whether there is a possibility that there is a problem with the data statistics., the product manager needs to analyze the data, investigate the cause, and solve it as soon as possible to avoid greater impact and loss.