What has changed? This is the question that must be asked when something has changed. For better or worse. We must find the why behind things:
- When you are billing one day much more/less than normal.
- When suddenly your children can’t stop catching one cold after the next.
- When you’re training for a marathon and you keep getting injured over and over again.
- Etc.?
What I’m really interested in now is analyzing numbers, but in reality the systematics are similar.
From big to small
As is. You have to go from the most relevant to the least relevant.
The first question would be.
- “What happened?”
Answer:
- “We have billed less this month.”
Okay, now that we know the facts, let’s look at the next steps.
How much less have we billed? This netherlands whatsapp number data way we can determine the seriousness of the issue before moving on to the why.
Find out why with detailed data
To know the reasons we need to have more details. Here the question would be.
What data do we have?
That’s why it’s a good idea to have an essential communication tool in case of crisis Google Analytics installed. Now you can check the numbers and get to work.
But of course. You still don’t do anything with the data itself. If you say that “we have invoiced less” consciously or unconsciously you are comparing it with something.
So go into a little more detail about what you want to find out.
Compare
“I want to understand why we are billing less this month compared to last month.”
Okay, perfect. That already serves us a little better.
Now my question to you is this: Would it make egypt data sense to compare September with August because you want to understand why you suddenly billed more in September?
Not really, because I think we can all agree that in August people are on vacation and worry less about shopping online.
It would be better to compare the recent month with the previous year. This way the conclusions are more valid.
Exceptions
To draw any kind of valid conclusions, you also need to take into account exceptions. The Covid months, for example, were an event that caused online sales to explode. If you compare that cycle to another, it’s not a very fair comparison.
If you compare the costs of a business and you have an extraordinary cost for a fine of 10,000 euros in one year, then that would also be something that would not be taken into account. That type of data must be eliminated from the analysis.