The point of the web analytics--those useful or useless KPI metrics

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Recently in the attention and collation of the Web site analysis of some of the knowledge, in which we find that we have some observations and methods to stay at the level of a few years ago, or just know the appearance and do not know how to draw from these data graphs of the guiding significance of the results we want to do some of our own learning notes and ideas to share here, I hope to grow together with you.

In the field of Web Analytics (Web Analytics), Avinash Kausnik blogs and books are a classic source of authoritative information that can guide you through practice and give you a lot of help. His blog is: http://www.kaushik.net/avinash/. His books include: Web Analytics:an Hour a day (Chinese version: the best web Analytics strategy for Web analytics-from experts, translated and published by Tsinghua University Press in September 2008), and a new book is the Web Analytics 2.0 (published in October 2009 and not yet published in Chinese), both of these books can be purchased on Amazon website.

Most of the articles in this series are based on Avinash Kausnik blog and book learning experience, so if you want to know more details, please refer to the above information directly.

Let's start today. In Web analytics, there are a few KPI metrics that we usually refer to, which are useful and which are meaningless.

Page Views (PV)

PV (Page view) is one of the most basic and important data for a Web site (as well as UV and visit). Strictly defined: PV refers to a request to download a page from a Web site. So as long as a request is made, whether or not you completely open (download) the page, will be counted in a PV. PV also has a saying is page impression or abbreviation for impression.

From the above definition, a website has more PV is good or bad? We usually assess the size and value of a site will be PV as a key core evaluation data, that is, the higher the size of the PV site the greater the value of the higher. This is usually based on online advertising and can be mined value opportunities to judge, a very High PV page or Web site naturally its advertising better sell, the value is correspondingly higher.

But for the site Department of higher PV, especially the single visitor PV is very high is good or bad is difficult to say. Perhaps your process design is lengthy, or the navigation is not clear so that visitors need a lot of clicks to find the content, so that the accumulation of high PV does not necessarily provide visitors with what value, so for visitors to buy the site's services/products is not meaningful.

So if you're still looking at the PV number of a single visitor, will your conclusion keep up with the behavior of your visitors? Think about what good ways to improve.

Exit most pages

If the site analysis has many visitors from a page exit, then this data can tell us what? The page was badly designed, or the visitor got lost, and of course it was the perfect page, the visitor solved their problem on the page, and then left with satisfaction. So unless this page is buying a service/product "payment success, thanks for patronage!" Such a core goal to achieve the results page (such page access is often only a small percentage), otherwise the simple data indicators can give a very limited significance.

Visitor Overview

Visitor Overview This column is rich in content, and some of the basic attributes are not likely to change for six months or even a year. This type of data include: Map coverage, browser, operating system, screen color, resolution and so on. If your site has no significant changes in geographic areas and populations, the above data usually only needs to be cared about every six months, while new browsers and new operating systems are released to pay attention to trends. After all, browsers and operating systems do not have an influential version every year, and visitors are less likely to update their operating systems and hardware.

Click Density Analysis

With this feature in the Web Analytics tool, you can display the number of clicks generated by standard links embedded on a Web page. The "Site overlay" feature in the "Content" column of Google Analytics. This feature helps us see how visitors use our site and whether they clicked on what we want him to click on. If not, what do they think is more interesting, more useful, what can we derive from it, and what are the new value conversion points?

Similar tools include a "hotspot map" tool, which was first developed by Crazy Egg. and "Site overlay map" is different from "hot map" is recorded in the page of the user's cumulative mouse click behavior trajectory, you can accurately monitor the visitor's mouse is how to click on the page elements. Therefore, this analysis of key pages such as: Home page, Landing page with Google Analytics can better observe the behavior of visitors.

The importance of qualitative data

The above examples of the points mentioned are quantitative data indicators, of course, there are many quantitative data. However, there is still a critical aspect of these large numbers-qualitative data. This is also in our website analysis is often missing a key aspect, usually this and the website analyst lacks the communication survey with the user, and the user goes not enough close related.

Even if we knew that 50% of the people had given up on the payment page, 90% of the people had left the page, but if there was no qualitative analysis, it would be difficult to understand why. and direct communication with the user, the direct inquiry is probably the simplest and most direct way to obtain the answer. Of course there are many ways to do this, such as usability testing, field trips, A/B testing, questionnaire research, heuristic evaluation, and so on. The following is a separate summary of this knowledge.

Today I've sorted out several common KPI assessment metrics, and I'll continue to share this topic later.