I have a neet example too where you can see the following:
- The more users the slower the page
- The slower the page the more users abandon the page load (wether they click on the link before the page is completely loaded or the leave the page totally)
Example here: http://www.kiutalk.de/jk/abandonment_chart.png

If now the users leave the page before it is loaded completely it might not effect the conversion directly but everyone should be aware that users might not be tracked with all the fancy trackers which are located at the end of the list of objects to be loaded or where the postback is tied to the page completion event.

This said: Be aware of the fact that users leave early and your numbers might be incorrect.

Best regards
Heiko

and to http://www.hoc.net/~mike/source/page_load_rtt/Howto_Measure_Page_Load_Time.html

It’s seems to be better.

The first thing that comes to mind is that the delay was not artificially (and randomly) generated.

The faster pages (towards 1s) were those cached … which means they were the most visited and possibly with lower exit rate for other reasons other than time.

Meaning that there could be correlation but not causality between time and exit rate. And the conclusions could therefore be very skewed.

I browsed over the PDF but couldn’t read anything about how the sampling was done.

Any ideas anyone ?

the bounce rate has decreased from 25.91% to 21.45% and the conversions has increased.

I’d suggest you use the built-in functionality that does what you want instead (Number.prototype.toFixed).

if (typeof window.wgNow == ‘object’) {
//var pageTime = ((new Date() – wgNow) / 1000).toFixed(1) // Round to 1 decimal
//var slashtime = ‘/skin_name/’ + ((new Date() – wgNow) / 1000).toFixed(1).toString().replace(‘.’, ‘/’);
_uff = 0;
_uacct = ‘UA-xxxxxxx-42′;
urchinTracker(‘/skin_name/’ + ((new Date() – wgNow) / 1000).toFixed(1).toString().replace(‘.’, ‘/’));
}