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Hello everyone, I am the Phantom of the Rain. The previous article was written because it was seen in the McKinsey methodology, "based on facts, strict structure, with the assumption-oriented", personally feel that this method is very suitable for our SEO workers, the previous article "Empty son rain: seo more need to do based on facts" is from this method, Today next for you to introduce SEO we need to do the 2nd: strict structure.
Structural structure, knot is the meaning of the combination of structure is the sense of construction, we want to SEO all the knowledge and events and search engines, Web site dynamic combination, this is the previous article I repeatedly mentioned. In the SEO, we should pay attention to the structure of the construction, which includes SEO knowledge structure, SEO thinking structure and data analysis structure of SEO, and so on, we have to do is to make these structures as far as possible solid integrity, do not let these basic things error.
1, SEO knowledge structure
SEO knowledge structure is well established, one is for the site of the SEO knowledge, one is for the SEO knowledge outside the station. The optimization of the station includes code and tag optimization, content optimization, url optimization, keyword layout optimization, inner chain layout optimization, outside the station optimization including the outside chain construction, domain name host factors. These things can form a strong knowledge of the network structure, each point is not easy to master, the use of better. Here is an SEO full solution map You can refer to, there is not a comprehensive place, you can add yourself:
SEO Full Solution Diagram
2, SEO thinking structure
Any SEO need to do thinking control knowledge, let knowledge into effect, because only to do this can get rid of the state of SEO workers, so the SEO thinking structure is particularly important. Here are some classic ways of thinking:
A, do SEO first to do a good job of the foundation, the more basic more easy to ignore, the front for everyone recommended the "Baidu Search Engine Optimization Guide 2.0", I believe that if you seriously study well can certainly do SEO.
B, learn from competitors, from the industry boss. Competitors do well, there must be his extraordinary place, we have to find out through data analysis of competitors operating means, first imitation after innovation, and finally achieve beyond. Any one industry boss, as long as it is to do SEO, absolutely is done well, we find his winning tips can be used to other industries, copy 8630.html "> Sometimes it is easy to succeed."
C, the right SEO work plan. SEO is a long process, for each stage of the grasp is very important, what time to do what, this is a very real problem, many stationmaster are aimless blind to do, so very passive, also very easy to encounter problems, planning can clearly know what to do and can do.
D, all to save time, improve efficiency mainly. No matter what site SEO, all the work should pay attention to grasp a principle, save time, improve efficiency, because for SEO, time is really money, ranking the earlier, the site can bring more revenue.
3, SEO data analysis structure
In general, we have three methods of data analysis, one is exploratory data analysis: There is a good idea, such as the site for keyword density tolerance in the end is how much, we can adjust the keyword density to see the analysis of the search engine tolerance. The second is the model selected analysis, we have one or several methods of operation, the elaboration of a detailed plan, and then through a stable cycle of testing, to determine the effect of these types of operations, and then give up those unreasonable operation. The third is based on the inferred analysis of the existing data. Every day we can get a lot of information from search engine and user's visit to the website, and also get the information in the dynamic of the website, and analyze the information, analyse the problems, and form a set of reasonable theories. Basically, around these three-point analysis methods, we can build a powerful data analysis structure.
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