Tutorial: Analyzing trends through Web Analytics

-Akshay Ranganath

After a long break from the field of analytics, I am back to it! I was just waiting for the data to be collected on my blog to take this further.

In this article, I’ll try and explain the importance of trends and how you can derive valuable actionable (and curious) data from the metrics. The way the article is written is how the thought process went on in my mind. So, it’s not like a tutorial but a pointer on “how to think analytics”!

Methodology

All the data for this blog has been take from my personal blog titled Life Unraveled. The blog has been in existence for since 2004. The analytics tool that has been used is Google Web Analytics.

Questions addressed

The trends addressed are for the following two main metrics:

  1. Visitors who arrived from Referring Sites (1)
  2. Visitors arriving through an organic search (2)

Two time periods were used for comparison:

  • Period 1: November 1st to December 15th
  • Period 2: December 16th to January 15th

I know the two periods are not the same – but, I realized that only after I’d collated the metrics! So, please bear with me. Ideally, the periods should be of equal duration.

Following is the results from the data collected. Please see the attached excel sheet for the collection of complete data.

Referring Sites

Referring sites mainly have been Orkut and Blogger. So the comparison is for the three:

Metric Overall
Orkut
Blogger
Visits 36 (8) 16 (4) 9 (1)
Pages/visit 2.39 (1.12) 1.79 (1) 2.78 (1)
Avg Time on site 5 m 11 s (7 s) 2 m 39 s (0) 8m 59 s (0)
% New visits 47.22 (75) 68.75 (75) 0 (0)
Bounce rate 66.67 (87.5) 68.75(100) 77.78 (100)

Overall, this category has shown a four-fold increase in the number of visit and about 2 times improvement in the length of stay on site. The loyalty too has increased with fewer and fewer people ‘bouncing-off’ the site. All of this is good. The questions to answer here are:

1. Blogger has given me 9 visits with an average time of 9 minutes on the site – these guys are really interested in my site. But, who are they? And how did my blog feature on the blogger’s site? If I can crack this, I’ll be able to increase the volume and my audience.

2. Orkut has started to perform well. The feature of adding the blog RSS feed to the Orkut profile has proved to be a success. Yet, considering that I have almost 100+ contacts and I am still getting just 16 visits from Orkut (with about 10 new visitors), it is not so good. Somehow, the positioning of the feed URLs does not seem to be very prominent or visitors to Orkut are just interested in scrapping me and nothing else. I need to do more marketing of my blog on Orkut – after all these are the people who know me well – and technically should be on my blog for a longer duration of time!

Search Engine Referrers

Search engine and google are synonymous and this was true on my blog too. For the entire period of Nov 1st to Jan 15th, just one query had come from AOL – and this was the one I had tried! All of the visitors who entered the blog through search had used Google.The metrics for users entering from Google looked like this:

Metric 1st Nov – 15th Dec
16th Dec – 1st Jan
Visits 50 169
Pages/visit 1.28 1.45
Avg Time on site 0m 26 s 1 m 50 s
% New Visits 94 93.49
Bounce Rate 90 79.88

The overall number of keywords too had increased from 43 to 133. My blog had suddenly been noticed by Google. The best part is the combination of ‘time on site’ and ‘bounce rate’ combination. It shows that a visitor once having landed on the blog spent an average of 2 minutes (at least gave me a chance!) instead of simply moving away – as was happening in the previous time frame. The obvious question was – What were the keywords that were sending so many people to the site and how were they performing?

The search keys and the performance is attached as the excel sheet. If you look at it, I’ve classified it as the top 10 search keywords. The top 10 have been split into two groups:

  • top keywords by visits and
  • top keywords by average time on site

The reason for this classification was to gain a better perspective over the performance of the search terms. Just because more people land on my site by entering a particular keyword does not tell me much. But, patterns of key words where people entered a particular search key – and stayed on my site for a long time: these are the type of audience that I need to chase. Others, I need to improve.

Search Keyword analysis

The results for the search keywords are here in the excel sheet.

Analysis for period Nov 1st to Dec 15th

From the keyword analysis for the first period, (Nov 1st to Dec 15th), two things stand out:

  1. The term chankya neeti has performed very well – it has brought in 4 visits. On an average, these visitors have been on the blog for about 5 minutes. This implies they liked the search result and read the blog.
  2. By the 4 term, the average time on site drops to 0. This implies people were not happy with the results and they left almost immediately after hitting the site. Most of these search has been for the quotes on the book By the river Piedra, I sat down and wept. Somehow, the blog is not displaying the expected result.

Analysis for period Dec 16th to Jan 15th

Comparing this with the next period, there is a drastic change:

  1. The total no of search keys have increased from 43 to 143. Almost 30 of the key terms have given me an audience who have spent at least a minute on the blog – a drastic improvement!
  2. The most successful key (high visits + longer time) have now changed. Now they are for the quotes from the book The Zahir , etc.
  3. Comparing keys sending most visits v/s keys that lead to longer time on site, it is revealing that top quality key-terms have been one-off. Rather than the blog appearing consistently for a particular key, people are discovering the blog by chance. This is a good way to identify keys for Search Engine Optimization.

Some of the improvements that I could think of were these:

  1. People search ‘alchemist’ were disappointed. They did see the quotes from the book – but it is not organized. Hence, they left the blog.
  2. Top quality results have been for those blogs where the title and the URL are meaningful. For example, the book review blog for Social Intelligence has the title Book Review: Social Intelligence and the URL as http://rakshay.blogspot.com/2007/12/book-review-social-intelligence.html. By being consistent it has got a good quality audience

The above metrics then implied that my blog had started to get a better attention from Google – since searches were more often throwing up my blog. Yet, some of the keywords were not leafing audience who liked the site. This implied either that the content was confusing google or it was not optimized for the right kind of keywords. So, now, I have to read through the blogs, check the title and URLs and optimize the blog so that it starts to gain a better performance.

Conclusion

The pattern analysis for two time-period for just a personal blog threw up a huge list of questions and some startling results. Imagine how it could unlock the areas for improvement if this had been a commercial grade high-visibility web site!

Just before I sign off – I found that there were 9 visits from a same person in Tech Mahindra reading all the blogs regarding matrimonial.. hmm. i wonder who this was? And – one person from Millington, NJ searching for my name on google had landed and remained for almost 10 seconds on my blog! Any clues who these people were?? 🙂

Glossary:

Referring Sites: The web site where a link to the current blog was placed. Users clicked on this hyper link and arrived at my blog

Organic Search: Process of entering a search keyword and arriving at a site by clicking on one of the search results. This is used to compare against the paid search.

Reference site for analytics

Life Unraveled – http://rakshay.blogspot.com

Web Analytics results(excel sheet) – http://rakshay.googlepages.com/webanalytics.xls

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Author: akshayrangananth

CDN Specialist, Web performance evangelist, and SEO tinkerer.

One thought on “Tutorial: Analyzing trends through Web Analytics”

  1. hello!
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