Before I wrote a series of articles on China WhatsApp Number List data analysis thinking, it said that before we do data analysis, we must figure out what the goal of the demand side is, and redefine the questions raised by the business side according to the goal. .
However, that article more emphasized the importance of goal thinking, not much about the method, and today I will talk about the basic ideas.
First of all, we can use the method of empathy to think from the perspective of the other party and what the other party’s needs are. However, empathy is not necessarily to meet the needs of the other party, but to find a balance between your own responsibilities and the needs of the other party.
Generally speaking, business students put forward data analysis needs, there are two situations:
The first one is that this classmate is going to report, and he comes to you because he lacks some data. As for the report, it is usually either an invitation for credit or a China WhatsApp Number List dumping of the blame.
We better be careful about this kind of demand. After we change positions, we know what he is going to do, but we don’t necessarily have to fully meet each other’s needs. Because the business side may want a very strange data caliber in order to do a good job in reporting, this may lead to misjudgment by the leader.
For example, if the retention rate of the function is not good, he will want to check the usage time. If the usage time is not good, then he will check whether the retention rate of loyal users has increased? If it still doesn’t work, then look at the change in the retention rate of users who have used it more than 3 times in the past for more than 2 minutes each time…
Next, we move to the second step and re-look at the whole problem from a global perspective. After the first step, we know why the business side cares about this problem, but the business side cares about this problem does not represent the whole problem, we also need to think about the overall situation.
For example, as mentioned above, if there is a way to take credit, the product classmates make a product revision. The data is very good, the retention rate, and the user’s active time have increased. We can’t rush to conclusions. We need to look at the overall impact of this business change from a global perspective.
There are two ways to think globally:
In short, he must find a point of growth to show that this thing is not a complete failure. If we take the numbers according to this caliber, then we will be blamed for data analysis when something goes wrong in the end. We need to be as objective and fair as possible for such reporting requirements, and we can provide some conventional indicator data instead of using some strange calibers.
But if this business is really good, we can also do some favors, such as marking some good conclusions in black and bold to make it easier for leaders to see. This will not affect your objectivity and fairness as a data analyst, but it can also be a smooth human relationship, which can make your future cooperation with the business side smoother