Web analytics: "Average site stay time" hidden traps

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In the previous understanding of the concept of pv,uv,visit, and know how many visitors to the site, the next key indicator is "average site stay time."

Because it looks like an easy to understand metric (not just how much time does a visitor spend browsing the site), Many managers, especially those from other non-Web professional departments, may have special favorites: Visitors spend more time on our site means the higher the stickiness of our site, our site provides visitors with more valuable content and services, our opportunity to transform the value of the visitors more. These ideas don't sound very wrong at first, but there are a lot of hidden pitfalls in the cute indicator of "average site stay".

How is the average site stay time measured?

When a visitor makes the first request to the Web server, a conversation is initiated for the visitor. From this point on, each request is logged in response to a request time in the course of the customer browsing the site. Like what:

Click 1:index.html (Time: 12:01)

Click 2:member.html (Time: 12:02)

Click 3:product.html (Time: 12:04)

Typically, a Web analytics tool calculates the time that a visitor spends on a page by calculating the difference in time marks between a page and the next page. In the example above, the user spends 1 minutes (12:01–12:02) on the home page (index.html) and spends 2 minutes (12:02-12:04) on the Member pages (member.html). So how much time does it take on the product page (product.html)? Since the user left the site from this page, we can not know the user specific departure point of time, may be just opened this page was a call to walk away from the computer, It is also possible that this page is too difficult to understand, causing users to see a long time did not see clear directly closed. As a result, the Web analytics tool typically defines the time it takes for petitioners on this page to be 0 minutes.

This is a big problem, and you'll find that a large portion of the site's visitors spend nearly 0 seconds of their time. Take my blog, or a lot of news site content page, most blogs are the most content directly on the home page, then most readers read the article content will not in-depth comments or replies, then they left directly.

Now that the cups appear, the Web analytics tool can only count for 0 seconds, so we don't know if they spent 10 minutes reading and thinking deeply, or just a few seconds away. Therefore, the visitor's last 1 page 0-second stay definition is a very cute trap. Therefore, if your site a large number of visits are a single page access, then you must have a large number of access time to 0 of the data, the site's average access time is not a lot of significance of the value. In this case, you can also eliminate the access data for a single page when you are counting, and the results can be quite different.

How to define the goal of average site stay time?

As mentioned at the beginning of this article, is this indicator higher and better?

If you are to help your customers complete their tasks as soon as possible (such as: Purchase, FAQ), then the goal should be the shorter the better; If you want your customers to participate in the interaction of the site, the longer the time will be the better.

Also note that the definition of this goal is more than just a simple "more" or "less" good question. We can achieve "more" by increasing the number of pages in a process or by setting more process barriers in the process of accomplishing goals for the user by reducing the number of pages. And these inappropriate means and methods will make the site overall user experience down, thus in other more critical data to cause greater losses.

So, this is just an indicator, understand it, and let it serve your ultimate value.

Original address: http://www.wenbin.me/webanalytics/web-analytics-time-on-site.html