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Behind network data analysis index of network log


The quantitative analysis of the commonly used

questionnaire, which can collect the user of the product of subjective feedback, it is affected by the questionnaire, cannot objectively reflect the user how to use the product, they encountered problems in the actual environment. And for the quantitative analysis of the website, the log file of the network server can reflect the current experience of the user truly, explain the deep characteristic of behavior, can improve product more effectively.

network log can help us answer a lot of questions, such as the user in what period of time to browse the site; more interested in what sector site; how to understand the website; many users will turn into a repeat user; what is the path to find the point of interest on the site; should be how to optimize the use of the process, improve the user experience etc.. To systematically analyze logs and get valuable user feedback, we mainly consider four aspects: aggregation metrics, session based statistics, user based statistics and click stream analysis.

1. aggregation metric

can be interpreted as merging and analyzing a large number of web data. Following the log data of a travel forum, the commonly used metrics are aggregated. In particular, this forum is purely fictitious, and data is also intended to illustrate the concept of fiction.

(1) web browser.

compared with the browsing time at the same time, we can get the trend of user’s attention. Figure 1 shows a tourism forum from June 2008 to December 2010 traffic changes, of which 09 years around June traffic increased sharply, in December 09 gradually leveled off, the curve may be related to changes in the forum marketing, design and so on, so it can take measures to bring the effect of witness.


Figure 1 views of a Travel Forum

(2) the amount of browsing within each day of each day.

from Figure 2, you can judge that users mainly browse the forum during the break time, so the forum should highlight the content of leisure and relaxation.


Figure 2 browsing time of each day of a Travel Forum

(3) web site browsing distribution of each plate.

can analyze the browsing distribution of specific plates, individual pages and similar page groups, and judge the interest points of users. Figure 3 shows that the forum users are mainly interested in Southeast Asia and japan.


Figure 3, the forum travel abroad version of the proportion of

(4) operating system and browser scale.

facilitates web sites to better accommodate operating systems and browsers. Figure 4 shows what users use >

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