Using Google Analytics to Discover our Tops and Flops of 2020

Welcome to our first post of 2021! As promised in the previous post to be more regular in posting the various topics in this blog, we are kick starting the year with analytics to discover our top 5 and bottom 5 posts (credit the tops and flops inspiration from one of my colleagues who loves to use that in her weekly review at work), to better understand the content which interests you, the reader!

From the cover slide, we can somewhat see that the traffic has been rather cyclical, perhaps we can expand more on that trend in future but today, let us take a look at the top 5 and bottom 5 posts in the past 6 months. While we are currently 1200 users strong, you might also be interested in looking at our previous Google Analytics Analysis of our first 500 users.

When dealing with analytics, as usual, we want to ask questions which we want to answer. Through the behaviour overview, and full report of Google Analytics we want to know what our best and worst performing posts were.

What are our Top 5?

We were able to discover our top 5 posts (In terms of viewership, from the highest to lowest):
1. Nanyang Business School Business Analytics Module Selection Guide
2. 3 Reasons Why I picked a Specialisation in Business Analytics at Nanyang Business School
3. University Internship Hunting Guide (Tips from NTU NBS Graduate with 3 MNC Internship Experiences)
4. General and Unrestricted Electives Guide – From NBS Business (Business Analytics) Graduate
5. Which Major to Pick? Business Analytics vs Marketing (Ex-NBS Student)

These 5 posts contribute to a total of 37% of all our page views, even though they made up about 25% of all content.

What are our Flop 5?

We also managed to pick out our flop 5 posts (From the lowest to highest in viewership):
1. COVID-19 Pandemic: Should I Start Work or Go Back to School?
2. Business Model Template: Photo Studio
3. 6 things to do for 2 Hours in Stuttgart, Germany
4. Integrating Analytics and Management: Where and How to Start?
5. Key Takeaways from my In-office turned Work-from-Home Internship

These 5 posts contribute to 4.2% of all our page views, much less than the 25% of all our posts in 2020.

Additional Remark: The clear bottom fodders were the newer posts of Christmas Text Analytics and Hair Salon Business Model which we would exclude from the analysis as they have yet to pick up, but I urge you to take a read as they are really interesting posts!

Making sense of the insights

Our Age Demographics for readership shows that 60% are youths, and a good 40% are non-youth readers.

From the top 5 posts, there is a clear indication that many students visit us and rely on the information posted here for advice on their curriculum needs. We are really humbled to be able to create impact for the student audience as we always try to pay it forward after learning from the knowledge of seniors and we urge you to pay it forward in future too!

We also noticed that it was an interesting trend that 40% of our users are a non-youth audience, and we are heartened that we are able to communicate analytics and innovation to an audience that we initially did not imagine to create impact for. Do let us know which content you love in the comments below!

For the flop 5 posts, one of the central themes which surround these posts is for instance, it being no longer specific to analytics, which we relaunched the blog on (yes we used to include lifestyle posts and travel.), or the very slight reference to the epidemic which shall not be named since this is risk of lowering the search engine score of this post (we instantly apply these insights!!). We hope to continue bringing new content and will continue to generate more content which caters to your hunger for learning about analytics, innovation and management!

Additional note: We initially wanted to add in a text analytics, but we realised that there isn’t enough posts to do that on this post without getting just words that are repeated non-stop. If you liked the text analytics, you could look at our ranked 6th post, What I learned from Text Mining 400 Spam Comments on my Blog using R, to see what spam users like to write in our comments section.

If you liked our post, do bookmark this site, or follow us on our LinkedIn page as we look forward to creating new content for you every week. Wishing you a Happy 2021!!!

Image Credits: Original Image created by Tan Wei Xiang


What I learned from Text Mining 400 Spam Comments on my Blog using R

Hey everyone! Welcome back to another amazing analytics post this week. If you are a frequent visitor of my blog, and somehow made a genuine comment here, you would have noticed your comment never appears. If you saw the screenshot above, out of a total of 999 comments, I have marked 400 as spam.

I was reading through some really interesting comments on my blog and I was thinking, why not try doing some text analytics to see what are spammers most interested in talking about on my blog.

Some simple explanation, text mining is a common way to do sentiment analysis on long lines of text which many market researchers do not want to look through. By going through specific text found in the whole data, researchers want to find out what the general public is talking about. In this instance, I want to find out what spam comments are generally being posted to my blog.

A bit of Data Cleaning: The very manual and boring part…

I started off by copying 400 comments and saving it inside a txt file. As my professor always said, data analytics is about 80% data cleaning and 20% analysis. I would change the 20% analysis to 19% and add 1% in terms of insights, which is what the business world truly values.

My First Round of Analysis

After a whole massive cleaning exercise here are the first set of results, represented in a word cloud of my top 30 most popular words in the spam.

The most popular keyword is http… Which means people are spamming websites.

The most popular keyword in the list of comments is http, which many websites start with (https was also likely in the list with the s being removed and recoded as http.) The second most popular keyword is urlqhttp which is probably also a website.

In 400 posts, there were close to 8000 times http has appeared.

In 400 posts, there were close to 8000 instances a web address has appeared, which means on average, spammers were posting 20 links to my blog. (They are probably trying to create backlinks to their website to improve their search engine rankings, which also will damage my website search engine ranking if it has too many backlinks out.) Thankfully these comments did not see the light of day.

Site and blog were the next highest which would make sense to come out 1.5 times per comment. Things like: This is an amazing blog/site, before adding in other things.

These links all appeared 582 times, which should be more or less safe to assume they are posted by the same poster.

These websites were also the most frequent in the comments, in the same frequency, it is likely that a bot has been created by a poster to consistently post the same thing over and over again. (Or perhaps he is that free and did it manually.) It was nice to know that spammers on my blog is interested in reviews, trips and books, linking things, and some German place which consists of Freiheit, which means political freedom (Yes, I learned German for 4 years before.). I did not open the links as I was worried of any potential spyware.

Okay that is enough analysis for today. If you are interested, do drop by for round 2! If the viewership is high enough, I’ll likely run another analysis on more comments in future.

If you liked the analysis, you may like this analysis too!

Otherwise you might want to know how to put analytics and management together!