How can website hits statistics be helpful to improve usability?

Question

Have you noticed that almost every links in facebook have ref query string?

I belive that, with that ref, facebook somehow track and study their user behaviour. this could be their secret recipe of making a better usability.

So, I am trying out the same thing, change http://a.com/b.aspx to http://a.com/b.aspx?ref=c and log every hits into a table.

========================================================================
userid | page          | ref       | response_time | dtmTime
========================================================================
54321  | profile.aspx  | birthday  | 123           | 2009-12-23 11:05:00
12345  | compose.aspx  | search    | 456           | 2009-12-23 11:05:02
54321  | payment.aspx  | gift      | 234           | 2009-12-23 11:05:01
12345  | chat.aspx     | search    | 567           | 2009-12-23 11:05:03
.....  | ............  | ........  | ...           | ...................

I think it's a good start. I just don't know what to do with these informations. Is there any appropriate methodology to process these informations?

Answer 1

Well facebook uses them for user interface usage observation (I believe) so they see where people click more (logo or profile link) and they consider changing the UI accordingly in order to make interaction better.

You might also be able to use it to see common patterns in usage. For instance, if people follow a certain chain profile -> birthday -> present -> send you might consider adding in a function or feature to "send present" on their profile when it's that persons birthday. Just a thought.

Answer 2

Research has shown that fast responses are a way to improve not only usability of a website. It's also a way to improve conversion rates or site usage in general.

• Tests at Amazon revealed that every 100 ms increase in load time of Amazon.com decreased sales by 1%
• Experiments at Microsoft on Live Search showed that when search results pages were slowed by 1 second: a) Queries per user declined by 1.0%, and b) Ad clicks per user declined by 1.5%

People simply don't want to wait. Therefore, we track response time percentiles for our sites. Additionally, nice visualization of this data helps with measuring performance optimization efforts and monitoring server health.

Here is an example generated using Google Charts:

That looks bad! Response times of > 4000 ms certainly indicate performance problems that have a considerable impact on usability. At times the 800 ms percentile (which we consider a good indicator for our apps) was as low as 77%. We typically try to get the 800 ms percentile at 95%. So this looks like there's some serious work ahead ... but the image is nice, isn't it? ;)

Answer 3

Here's a second answer as the former was only about response time statistics.

The ref query string allows to identify the sources, especially of people entering a Conversion funnel. So you might make statements like "N $ of revenue come from users clicking link X on page Y". Now you could try to modify link X to X₁ and see if it increases revenue from this page. That would be your first step into A/B Testing and Multivariate Analysis. Google Website Optimizer is a tool exactly for this purpose.

Answer 4

To make the best use of your website statistics you need to think about what your users are trying to acheive and what you want them to achieve. These are your site's goals

For an ecomerce site this is failrly easy. Typical goals might be:

1. Search for a product and find information about it.
2. Buy a product.
3. Contact someone for help.

You can then use your stats to see if people are completing the site's goals. To do this you need to collect a visitors information together so you can see all the pages they have been to.

Once you can look at all the pages a user has visitted and the sequence they visitted them in you can see what they have been doing. You can look for drop out points where they were about to buy something and then didn't. You can identify product searches that were unsuccessful. You can do all sorts. You can then try and fix these issues and watch the stats to see if it has helped.

The stats you're collecting are a good start, but collecting good stats and collating them is complicated. I'd suggest using an existing stats package I personally use Google Analytics, but there are others available.