A talk on Web Analytics (i)

The beginning of 2017 was still in the mobile era, as the number of smartphones in the past few years has been growing at a high speed. This year's Google IO conference, Google has said to move from mobile first to AI first, followed by the Alpha go to 3:0 defeated the world champion Cathay Associates, announced the advent of the smart era. So, it's a little too outdated to talk about web analytics. Of course not, because the AI era did not suddenly appear, but from the internet era came. Even the heyday of deep learning algorithms evolved. It is only when we know it that we can do it.

1. Web Analytics Acquaintance

and analytics The earliest attachment is in the university, I choose the major is the Department of Intelligent Science and technology, then is sophomore, 2006, is also our professional start of the second session, at that time I think AI particularly cool, I want to learn some of this knowledge. Anyway, that time we have a relationship, in school we need to learn the data-related processing problems, such as pattern recognition, machine learning-related courses, think that learning is very interesting, if these are data analysis, that is, and data analysis of the acquaintance. We have a laboratory that is a visual and auditory laboratory, and we spend a lot of time concentrating on the processing of visual data and auditory data, and think that AI is mainly doing the work. At that time, the recognition rate of visual hearing is not so high, if you want to say a little intuitive feeling, it is to feel that AI is biased toward the study of the nature of more.

09 graduation, into the million Yangxin to do wireless network optimization related work, and data analysis has a certain connection, but actually mainly to do the application system development.

The back and the Web was in 2011, when the change of home units into the Internet start-up company work, and was pulled to do some web-related development work, at that time began to and web and search and web analytics began to have real contact. It is also there that the trend behind being moved into the mobile side of the development.

2, the understanding of Web Analytics 1:piwik

What is web analytics anyway? [Baidu Encyclopedia]web Analytics (Website analysis), is a Web site visitor behavior Research, in business application background, especially refers to the use of information collected from a website, to determine whether the site layout in line with commercial objectives.
This stage of my understanding of web analytics stays in the Piwik phase. At that time we do the site, behind the need for a monitoring site to monitor the user in our site to make the relevant behavior, I admire a genius engineer introduced Piwik this east, at that time, we through Piwik can see the site every day each page is visited, the situation of the page inside jumps , external traffic, search conditions. I feel particularly magical.
So, I'm going to go back to my little white case, and learn from Piwik what is web analytics and what needs to be done to do web analytics. 2.1 Open Piwik Demo, the title on the left is divided into dashboard, visitors, Actions, referrers, goals, Preminum. It is well understood that Piwik's analysis focuses on the visitors, the behavior of visitors, the sources of visitors, and the goals of the site. So I think Piwik and similar sites (web Analytics sites), the focus of the matter is basically this, focus on the site users and user behavior research, focus on the site to achieve the goal. By Piwik the above analysis and monitoring user and user behavior, to achieve the goal of the site.
2.2 It is convenient to see the first dashboard is related to the visitor, such as 1, the visitor real-time situation, 2, the visitor some summary statistics, 3, the visitor's past time trend, 4, the visitor's geographical location, 5, the visitor's source (search engine, direct access), is not to say, The key point of web Analytics is the user

2.3 Visitor Analysis: Analysis of the various dimensions of the visitor: device, System version, Access time (client time, server time), participation, browsing log, and so on. Engagement is the most important link to analyze the retention of users, and many optimizations are initiated for the user's retention.

2.4 Behavioral Analysis: page access, landing page, exit page, page title, site search, links, downloads, events, content

2.5来 Source Analytics: Search and keywords, websites and social, competitor information.

2.6 Goals: In the above analysis, there are many indicators, each individual indicator optimization can become a target, or even multiple joint to become a goal, such as traffic, such as page length, bounce rate, exit rate, participation and so on.
2.7 Advanced Features: A/B testing, thermal and behavioral recording playback, funnel, tabular analysis, media analysis, user flow, search engine keyword performance, and so on, this can be counted as the advanced step of web analytics. Each feature is specifically optimized for the one or two targets of Wang Chang.

3. Conclusion

Piwik is a window that leads me into the world of understanding data analysis.

I didn't know it back then. Piwik is just one of the web analytics systems, as well as the well-known Google Analytics, and the point of service embedding is also important. Every system has its good place and has its problems.

The following section I want to talk about Web analytics (next) Proficient WebAnalytics2.0 this side of the book and a little feeling.

Then the mobile end of the development quickly, we also quickly abandoned the web-side above the feature update, the focus shifted to the mobile side. After a few years of doing some mobile work, I can talk about analytics on the mobile side.