With Tableau, it was really easy to aggregate the average number of days between inquiry and departure date for each month over a couple of years. A small side note: I hate the fact that bar graphs are so good at visualizing this data set. Bar graphs are boring, but effective.
I could end the post here. The graph clearly shows that inquiries made between April and July are on a much shorter notice than inquiries made during the winter months.
Why are the numbers still so high? I did not bother to calculate standard deviation, but of course the range of values is very high. My guess is that for July, a very simplified data set would look something like n = 4,4,4,4,160,160,150.
I was hooked and wanted to know more. How long does it take our customers on average to make the decision to book?
This validates my theory even further. In April, customers are surprised, like: "Whaaat? Easter holidays again?!" and need to book a cool trip on very short notice. During August, customers realize the weather in Germany is terrible and they become very booking-happy. I can't really explain the data in February, though. If anyone has an idea, I'd love to hear it.
One more metric that strengthens my theory is the average number of days between booking and departure.