How to calculate site dwell time and page dwell time, calculate site dwell time _php Tutorial

How to calculate site dwell time and page dwell time, calculate site dwell time

Site Dwell times (time on site, hereinafter referred to as TS) and page dwell times (time on page, hereinafter referred to as TP) are important indicators of user experience analysis and traffic quality monitoring. But very few people know how the average time to visit a website (Average times on site) is calculated. Whether using the Competitive intelligence analysis tool or some kind of web analytics solution (data obtained in the Web Analytics solution, either weblog or JavaScript), few people know how much the average site access time is calculated.

Therefore, writing this article is to explain how TP and TS are calculated.

Case 1:

Someone has visited your website homepage, and your web analytics tool has started to count 1 sessions for this visitor. The visitor then browses to the other two pages and then leaves your site (either by closing the browser or by typing a different URL in the Address bar or by clicking on a link to another site on your site ...). For the sake of simplicity, we take this process as a session.

What we want to know is as follows:

Tp = The user's dwell time on a page (Times spent on a page);

Ts = Total dwell time of the user on the entire site (spent on the website).

Let's say this session starts from 9 o'clock:

Currently, all web analytics tools are able to pinpoint exactly how long a page access request has occurred, but that's not enough to explain how much TP and TS are, as we need more information:

The user did not bounce (jump), click on the first page of a link to jump to 2, the current website analysis tool can also get page 2 open time, and can know is the same user, so can easily calculate first page TP:

Tp (Home) = 9:05–9:00 = 5 minutes.

There is something on page 2 that attracts the user, so the user continues to visit Page 3

From the look:

Tp (P. 2) = 9:30–9:05 = 25 minutes.

The user exits from Page 3 and ends the session:

So, how long did this user stay on page 3? The problem arose because none of the current web analytics tools crawled the user's "timestamp" from Page 3 o'clock, so we couldn't calculate how long the user stayed on page 3! So:

Tp (p. 3) = 0 minutes.

Because the next page request time is not available! The Site Analysis tool does not know what the user's dwell time on the last page of the session is, as is true for most web analytics tools.

Let's use to indicate the dwell time for each page of the Web Analytics tool statistics:

Tp (Home) = 5 minutes

Tp (page 2) = 25 minutes

Tp (Page 3) = 0 minutes

So, what is the duration of this session's visit to the entire site?

Ts = 30 minutes

Is it reasonable?

I think it may not be reasonable, because you do not know how much time the visitor spends on the last page, so the time statistics given to you by the website analysis tool will generally be less than the time the user actually stays on the site.

Case 2:

How does the time on site and on page be computed when browsing the site with multiple tab (tab) browsers?

Firefox's multi-tabbed page view has earned it a reputation, but it's a hassle for computing time on page and on site. When the user opens a link to the same website in another tab, that is, when browsing the same site through two tab pages, what does time on page and times on site look like?

This situation is confusing the calculation of the time by the website analysis tools.

is a popular user browsing site scene, through this scenario we can understand the impact of multi-tabbed page browsing ...

• A user came to the first page, and then in the New tab opens a link on this page, when the homepage occupies a tab page, click the link new open page 4 occupies another tab page.
• At this time, users browse page 4 after the page 4 is not closed, back to the first tab to continue to browse the homepage.
• In the process of browsing the homepage, the user clicked on another link on the homepage, jumped to page 2, but no new tab was opened.
• Then, the user switches to Page 4 tab, click the link to go to page 5, on page 5, close the current tab.
• Then, the user clicks on page 2 link to Page 3, of course, or the same tab. Finally, the tab is closed on page 3 and the session ends.

How does time on site calculate in this case? Different Web analytics tools have two ways of calculating this "multi-tab" browsing behavior.

Way One:

The Web analytics tool will record this multi-tab browsing situation by tab, which is the following calculation:

Statistical results output: 2 access procedures (i.e. 2 sessions), one tab per session.

Session1 (In the Home page tab):

Tp (Home) = 5 minutes

Tp (page 2) = 25 minutes

Tp (Page 3) = 0 minutes

Ts (entire access duration for the same tab) = 30 minutes

Session2 (tab in page 4):

Tp (page 4) = 6 minutes

Tp (page 5) = 0 minutes

Ts (entire access time for new open tab) = 6 minutes

In this case: 2 session,1 Uvs (Unique Visitor) are recorded in the report of the Web Analytics tool.

Way two:

Some Web analytics tools combine this multi-tab into the same access process to eliminate the impact of multiple tabs (tab pages).

Or the example above, but we're transforming the way we do it--marking the same process, and different colors representing different tabs.

Statistical result output: an access process (i.e., 1 sessions) that contains two tabs during this visit. The entire access process is re-organized as a "timestamp".

This session:

Tp (Home) = 1 minutes

Tp (page 4) = 4 minutes

Tp (page 2) = 2 minutes

Tp (page 5) = 23 minutes

Tp (Page 3) = 0 minutes

Ts = 30 minutes

Which statistical method do you think is more reasonable and which way to prefer?

Be sure to ask your web Analytics service provider which of the two ways to calculate the time and access process for multiple tab (tabbed pages) browsing.

Now more and more people are using multi-tab browsing, so the choice of which way will have a huge impact on your website analysis of the final data output-there is no doubt that the two methods of statistical final data will certainly have a very obvious difference.

Solution:

get page (tab) Close time (the page closes either by closing the Browser or tab page, typing a different URL in the address bar, or by clicking on a link to another site on your site ...). ）

By the tribal State (Beijing) Technology Co., Ltd. independent research and development of the latest User experience visual analysis tool "Bang Analysis", to obtain the page closing time, by calculating the page opening and closing times, you can easily and accurately get page dwell time and session time.

This session:

Tp (Home) = 5 minutes

Tp (page 2) = 25 minutes

Tp (Page 3) = 1 minutes

Tp (page 4) = 6 minutes

Tp (page 5) = 3 minutes

Ts = 31 minutes

Page dwell time is accurate, but how big is the meaning? We will discuss the next article in detail, please pay attention!

Reprinted from: http://www.bangfx.com/research/?p=651

Calculate the time to stay on a webpage

Use the JS code bar to write two functions a life in the Web page load is called, in this function to do a timer, get to the current system time one in the page exit call and then in the Exit function inside the page stop timer and then do a time operation with stop time minus page load time We can figure out how long the web stays.

How to effectively increase the page dwell time?

One, the study of the three role of residence time:
1. The longer the site stays, the more the search engine evaluates the site (weight).
2. Study the duration of a single page, to understand the user's access behavior, and to improve the page.
Stay time is just a factor for search engine to consider the quality of a website
3. Page design is reasonable, which is also the most reflective of the browsing habits of supplies, know that the information is useful to users!
Second, the home stay time is not the longer the better, the list page (entrance) is not the longer the better, the product page stay longer the better.
1, the home page should be concise, or more easily see what he wants, easy to understand, clear classification.
2, the list page to have the filtering function, the purpose is lets the user find the content which he wants quickly.
1. The richness of the content: Product details, multi-shot products, comments, similar products (picture and text combined to reduce the user's reading fatigue)
2. Text content readable, font size
3. Expand the user's needs: Open a different directory, related recommendations

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