Page stay time and site time detailed

First, http://www.aliyun.com/zixun/aggregation/8343.html "> page stay time and site time is calculated?"

Suppose the user visited the home page of the Web site. The analysis tool marks the visitor as a visit, then the visitor browses to the other two pages (Page2 and Page3), and then he leaves your site. As shown in the following illustration:

What we want to know is:

Tp = time spent on one page TS = Total time spent on this site

If this user starts to visit the site from 10:00:

For Page2, the access time is 10:05-10:01, or 4 minutes.

Then the visitor came to the Page3 page and he found that the page could not meet his needs or that the content he was looking for had been found on the Page3 page. So the next step is to leave.

So how long did the visitor stay in Page3? Because we do not know the user in the Page3 specific departure time, we can not calculate the visitor in the end of Page3 stay for how long. Therefore, the Web analytics program does not know how much time visitors spend on the last page of the site.

The following figure shows the access time for individual page site Analysis tool Statistics:

TP (Home) = 1 minutes tp (PAGE2) = 4 minutes tp (Page3) = N/ats = 5 minutes.

It is easy to see from the above data that such data is not reasonable, because you do not know how much time the visitor spends on the last page, so the time statistics of the web Analytics tool will generally be less than the time that the user actually stays on the site.

The accuracy of site stay time is related to bounce rate and exit rate. The greater the bounce rate and the exit rate, the less accurate the average dwell time.

Second, the page stay time or site stay time what use?

If the page stay time and site stay time simply take out to see, then the significance is not very big, the main reasons are as follows:

These indicators are tactical and we are not aware of the impact of these data on the company's performance. Simple page stay time or site stay time does not reflect the specific revenue. These metrics require a lot of extrapolation, and the simple logic is that the larger the data the better, the data cannot directly infer whether a night page is good or bad. These indicators are mainly short-term data, with the development of the Internet, according to the conversation to measure indicators are now far from satisfying demand, long-term visitor behavior, customer life cycle and so may more reflect the problem.

Different time of stay needs to be judged by different logic. Let's take a look at the example of Taobao:

website Average visit time conversion rate Taobao 3,030 minutes 10% Taobao Mall 1010 minutes 2%

The main reasons for these different data are: Taobao is like a supermarket, visitors do not have a clear goal, into the supermarket but eventually will buy something, Taobao Mall more like shopping malls, to the mall users are mostly with a clear goal, they go straight to the target, fast search, fast shopping, fast to leave.

From the dimensions of user behavior, Taobao users in Taobao and Taobao Mall has different shopping behavior, like the same people in the mall and supermarket behavior affirmation is different, so Taobao Mall's page style more concise, more standardized service, business more quality.

Similarly, as a travel booking site, the user came to the site basically have a very clear booking requirements, we need to do is how to let users in the shortest possible time to find the right products.

Three, the page stays time and the website residence time data how to apply?

1, the user experience to determine the page

From the calculation of page dwell time we know that it is not possible for users to stay on the page for long before they leave, that is, the page stay time is in the case of follow-up behavior calculated, to have this part of the data available, that is, each set of pages of the stay time to determine the number of users stay high or low the main reason, For example: The user stays on the search results page for a long time, is not the search results can not meet user needs, the user in the list page to stay for a long time, is not our list of filter to do is not user-friendly, in the product end page to stay long is not because the end of the page content display too much or the user did not find the content he wanted.

2, Visitor Marketing

Record site stays longer, but in the end there is no order of users, the users of the marketing, the problem is that the user only in the case of the login we can get to the user ID, record the user ID after the program analysis, need to give the user what kind of content. The feasibility is not very high.

3, the active pop-up customer service pop-up box or preferential information

When a user stays on a particular page for too long, the Customer service box pops up. Active contact with visitors. Solve the visitor's doubts. Prompting the visitor to order. But this part of the user experience will not be very good, you can learn the Sina Weibo at the top of the page of the tip function.

4, when the conversion target to use

In the absence of a single process of the site, you can use the page or site to stay time to complete a goal, then calculate the target conversion rate, such as what the main phone as the goal of the site, if there are more than n people (a large proportion) to reach the end of the page without a single, direct phone booking, you can use the stay time as a goal.

Reference Links:

http://www.kaushik.net/avinash/standard-metrics-revisited-time-on-page-and-time-on-site/

http://www.roirevolution.com/blog/2008/05/time_on_page_and_time_on_site_how_confident_are_yo.php

Source: http://www.biaodianfu.com/tiome-on-page-and-time-on-site.html