# Detailed Explanation for Time on Page and Time on Site

## 1. How to calculate time on page and time on site?

Assume that an user visit your homepage. Some analysis tools have already marked it as an visitor. Then he or she visit other 2 pages(Page2 & Page3) and leave your website as shown below:

What we want to know is:

• TP = Time on Page
• TS = Time on Site

Also assume that the user visit the site at 10:00:

For Page 2, the visitor stay from 10:01-10:05, 4 minutes. Then the visitor come to page 3. If he find that the page 3 meets his need or it isn’t valuable, he will leave the website.

So, what is the residence time on page 3? Because we don’t know when the visitor leave the page 3, even some website analysis tools cannot calculate that. As a result, it could be shown as follows:

• TP (homepage) = 1 min
• TP (Page 2) = 4 min
• TP (Page 3) = N/A
• TS = 5 min

To be honest, such data are made no sense given the correct time. We don’t know how long does the visitor take in page 3.  The accuracy about time on page or time on site is related to bounce rate and exit rate. As a result, the higher the bounce rate or the exit rate, the less accurate the resident time.

## 2. What is the use of time on site/page?

First lets see an example for Taobao:

 Site Avg. Page Visit Avg. Visit Time Conversion Taobao 30 3 min 10% Tmall 10 1 min 2%

The main reason that causes the difference is: Taobao likes a supermarket. Visitors do not have a clear goal about what they want to buy. Finally they probably purchase something in such supermarket. Tmall likes a mall. User probably go their with a goal. They go straight to shopping then leave quickly. From the perspective of user experience, they have different shopping behaviors. So it doesn’t make sense to evaluate a website only through the time on page/site.

## 3. How to make use of the data about time on page/site?

### 1) Judge the user experience

We already know that we cannot know the time on page which visitor leave. We only know the time on page that visitors have the next step behavior. However, we can use the data for each page before and let alone the last page. If the time on search result page is too long, maybe user cannot find what they want quickly. If the time on list page is too long, maybe our filter is lack of humanization. If the time on some product pages is too long, maybe the content is too much.

### 2) Track loyalty customers

Sometimes users order nothing but they view some pages for a long time. As a result, we can try to track their ID and give some recommend to them. Some online chatting software already support that function. Of course it needs more customer service.

### 3) Pop up a dialogue box

Some Chinese prefer online chatting than email. Just as most of Chinese eCommerce websites like JD, Tmall and Suning have online customer services. If a customer visit a website for a long time, sometimes we can pop up a dialogue box to chat with them.

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