Custom Python scripts are much more customizable than Excel spreadsheets. This is good news for SEOs — this can lead to optimization opportunities and low-hanging fruit. One way you can use Python to uncover these opportunities is by pairing it with natural language processing. This way, you can match how your audience searches with your...READ POST
While top website analytics packages offer pretty much anything you might needto find actionable data to improve your site, there are situations where we need to dig deeper to identify vital information.
One of such situations came to light in a post by randfish of Seomoz.org.He writes about the problem with most enterprise-size websites, they have many pages with no or very few incoming links and fewer pages that get a lot of incoming links.He later discusses some approaches to alleviate the problem, suggesting primary linking to link-poor pages from link-rich ones manually, or restructuring the website.I commented that this is a practical situation where one would want to use automation.
Log files are a goldmine of information about your website: links, clicks, search terms, errors, etc…In this case, they can be of great use to identify the pages that are getting a lot of links and the ones that are getting very few.We can later use this information to link from the rich to the poor by manual or automated means.
Here is a brief explanation on how this can be done.
Here is an actual log entry to my site tripscan.com in the extended log format: 220.127.116.11 – – [29/May/2007:13:12:26 -0400] “GET /favicon.ico HTTP/1.1″ 206 1406 “http://www.whois.sc/tripscan.com” “SurveyBot/2.3 (Whois Source)” “-”
First we need to parse the entries with a regex to extract the internal pages — between GET and HTTP — and the page that is linking after the server status code and the page size.In this case, after 206 and 1406.
We then create two maps: one for the internal pages — page and page id, and another for the external incoming links – page and page id as well.After that we can create a matrix where we identify the linking relationships between the pages. For example: matrix = 1, means there is a link from external page id 15 to internal page id 23.This matrix is commonly known in information retrieval as the adjacency matrix or hyper link matrix.We want an implementation that can be preferably operated from disk in order to be able to scale to millions of link relationships.
Later we can walk the matrix and create reports identifying the link-rich pages, the pages with many link relationships, and the link-poor pages with few link relationships. We can define the threshold at some point (i.e. pages with more or less than 10 incoming links.)