How can a website use statistics to improve usability?

Question

Have you noticed that almost all facebook links have a ref query string?

I believe that with this ref , facebook somehow track and study the behavior of its users. it may be their secret recipe for usability.

So, I'm trying to do the same, change http://a.com/b.aspx to http://a.com/b.aspx?ref=c and register every click in the 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 this is a good start. I just don’t know what to do with this data. Is there a suitable methodology for processing this data?

Answer 1

Here the second answer, the former, concerned only statistics of response time.

The ref query string allows you to identify sources, especially people in the conversion sequence . That way, you can make statements such as “N $ revenue” from users who clicked the link X on page Y. Now you can try changing the link X to X ₁ and see if it increases revenue from this page. This will be your The first step in Testing A / B and Multivariate Analysis : Google Website Optimizer is a tool specifically designed for this purpose.

Answer 2

Research has shown that quick answers are a way to improve more than just website usability. It’s also a way to improve your conversion rate or overall site usage.

• Amazon tests showed that every 100ms an increase in Amazon.com load time reduced sales by 1%
• Experiments at Microsoft on Live Search showed that when pages with search results slowed down for 1 second: a) Requests for each user decreased by 1.0%, and b) clicks on an ad for each user decreased by 1.5%

People just don't want to wait. Therefore, we track the response time percentages for our sites. In addition, a good visualization of this data helps measure performance optimization efforts and monitor server status.

Here is an example generated using Google Chart:

Percent left, response time, color coded http://j.mp/3bTf36

It looks bad! Response times> 4000 ms certainly indicate performance problems that have a significant impact on usability. From time to time, the 800 ms percentile (which we consider to be a good indicator for our applications) was 77%. Usually we try to get 80 ms percent at 95%. So it seems that there is some serious work ahead ... but the image is good, isn't it ?;)

Answer 3

It’s good that facebook uses them to monitor the use of the user interface (I believe), so they see where people click more (link to a logo or profile), and they consider changing the user interface accordingly to improve interaction.

You can also use it to view common patterns in use. For example, if people follow a certain chain profile -> birthday -> present -> send , you might consider adding a function or function to "send the present" to your profile when it is people's birthday. Just a thought.

Answer 4

To make the most of your site’s statistics, you need to think about what your users are trying to achieve and what you want to achieve. These are the goals of your site.

For an e-commerce site, this is inconvenient. Typical goals may include:

• Search for a product and search for information about it.
• Buy a product.
• Get help from someone.

You can then use your statistics to find out if people complete the site’s goals. To do this, you need to gather information about visitors together so that you can see all the pages on which they were.

As soon as you can see all the pages the user visited and the sequence that they saw in them, you can see what they were doing. You can look for dropped points where they were going to buy something, and then not. You can determine that the searches were not successful. You can do all kinds. You can then fix these problems and look at the statistics to see if this helped.

The statistics you collect are a good start, but collecting good statistics and comparing them is complicated. I would suggest using the existing statistics package that I personally use in Google Analytics, but there are others.