Session 8 Correlation in Social Media and SEO- a socio analytic application of RM Section B Group 6 Silpa Bahera (13PGP107)

Correlation in Social Media and SEO- a socio analytic application of RM

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Research Management (RM) is incomplete without statistical analysis. Today statistical techniques are an integral part of quality management systems, demographic information, population analysis, social marketing research, educational and medical research and quality management of real and modern enterprises etc. Socio analytics is another big application of statistical analysis which is helpful in SEO and web rankings.

Statistical analysis at a global level may not be very useful for any research. One needs to break statistical data into various micro levels then can do a cross tab analysis, chi-square test, correlation,  regression analysis etc category wise to find amazing results in terms of relations. Correlation leads to wonderful results about the strength of relation between various factors, especially in Socio analytics and SEO. It tells about potential factors of ranking, future opportunities, risk, SEO planning for a company’s visibility, also for the visibility of existing and new entrants of social media and search engines.

Correlation analysis studies the strength of two continuous variables that systematically change with respect to the each other. It is just a tool to measure the strength of a relationship between two variables, and it does not imply causation. Pearson’s product-moment coefficient range and significance is given below:


Correlation has given surprising results in market researchs and I am going to share some interesting findings regarding social media.

2012 searchmetric results of correlation:

(The study analyzed Google search results for 10,000 keywords and 300,000 web sites, as well as billions backlinks, Tweets, Google +1s and Facebook likes, shares and comments. )

1. Strong correlation between social signals of facebook and twitter with god rankings in Google’s index. correlation of Facebook shares= 0.37 (strongest).

2.   Main factors like quantity of text on a webpage, keywords in headlines and titles, have no effect in case of big brands. There is a strong negative correlation between them contradicting the traditional SEO theory.

3. Advertising factors leads to poor visibility. There is a negative correlation of -0.04. This pattern was strongest when there was a high percentage of Google AdSense ads.

4. Number of links is still matters, also matters the quality. Number of back links (links to a website from other sites) is still one of the most powerful factors. Correlation is +0.36. But the site needs to have links that look natural with smart choice of keywords.

5. keywords in the domain name is still the most important factor with a correlation +0.11.

2013 searchmetric results of correlation:

1. Brands rank higher on Bing the same way as Google

2. Backlink numbers are still most important for page ranks. Bing has a high correlation of 0.29 for this.

3. Number of likes, shares, comments etc are closely related to higher ranks.

Google+ correlation= 0.34

Facebook comments correlation= 0.32

Tweets correlation= 0.30

4. Search rankings need more quality:

Pages with more text have a positive correlation. E.g. Bing has 0.09.

Pages with number of images have a positive correlation of 0.08 (Google), 0.03(Bing).

5. On-page factors have a negative correlation because they are present in every page, but they are definitely necessary.

These correlation study and findings are highly important and helpful for social media sites and other websites’ visibility in search engine.

My personal opinion is apart from social shares, number of backlinks and traffic one can study correlation for various types of Facebook, twitter page content. Whether they contain any emotional content (positive or negative), emotional appeal, sexual content, news, jokes, popular posts etc which will lead to micro level of understanding of importance of factors in our research study.

Section B Group 6_Silpa Bahera (13PGP107)

Other Member:

  • Apurva Ramteke(13PGP068)
  • Chandan Parsad(13FPM002) 
  • Komal Suchak (13PGP086)
  • Rohan Kr. Jha (13FPM004)
  • Sushil Kumar (13FPM010)
  • Vivek Roy (12FPM005)
  • Vaneet Bhatia (13FPM008

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