Numbers Don’t Lie: What Website Analytics Tell About Human Behavior
While numbers don't lie, the conclusions you draw from them can still be incorrect.
Look at the second graph (website visits peaking on Dec 24th for giftcertificatefactory.com). The author concludes the following from this peak: "People favor doing things at the very last possible moment."
But this is nonsense. Such a conclusion would require a model for how people's preferences affect web page statitics. If you don't have a model, your intuition is going to fool you. Let me illustrate this with a simple example:
Assume that in our model world you have two kinds of people: Early-Buyers and Late-Buyers. Early-Buyers buy presents on a random day from Dec 1 to Dec 20. Late-Buyers on the other hand buy presents on a random day from Dec 21-24. Assume that 80% of people are Early-Buyers and 20% of people are Late-Buyers.
If you looked at the number of presents bought per day, you would see that the rates are 25% higher in the days from Dec 21-24. Your intuition will tell you: "People favor buying presents late". But that is not true, because in our model world 80% of the people are actually Early-Buyers!
Now, to explain the web page statistics shown in the article, we would need a more elaborate model; but constructing such a model and working with it is difficult, and that's why people avoid thinking about models, just post raw numbers, and then write whatever their intuition tells them, and then claim that it must be true because "numbers don't lie".
Hey Tommy! Im Tjerk your former collegae.
Anyway nice article. However your conclusions are not well founded. For example. what if majority of the traffic to your Gift Certificate Factory came from a website that went offline at the point in the graph. Then there is a different correlation.
Also i have never seen a huge drop like that. Are you sure there are no other reasons for the drop?
The peak for your MBI site might also be because some other guy linked to it from a favority website. This is often the cause for spikes like that.
Just saying, that the conclusions you make might feel right, you can never be sure with only the graphs. So its a bit of speculation.
With no discredit to the author (because this is a decent piece) but numbers sometimes do lie. Outliers? Correlation != causation?
You know what really shows human behavior? Speaking to someone face to face.
I remember being absolutely amazed after seeing the same number of unique visitors hit my site two days in a row.
I was expecting something more along the lines of this:
Human dynamics revealed through Web analytics