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  • Ankit Vora

Website Traffic Data Analysis Visualization

Welcome to the 21st century, where we’re all drowning in an ocean full of data and can’t possibly keep up. Like seriously – have you seen the size of those CSV files companies generate?

We’re generating terabytes of data and have very little time to make sense of it. And let’s be real; the human brain isn’t cut out for reading and memorizing CSV files with millions of rows and columns. That’s a job for some super-advanced algorithm, not us mere mortals.

Simply storing your data in spreadsheets isn’t just enough. It’s great that you’re generating data from different sources like website, customer surveys, social media, and your marketing campaigns. But let’s be honest – without a way to make sense of this data, you’ll probably stick it all in a folder and forget about it.

So – what’s the best way to make sense of the data we’re generating? Don’t worry; I have the answer to your DATA problem.

Gigasheet is a free online CSV viewer that can handle files too big for Excel or Google sheets. Gigasheet is also a data visualization tool, which is the key to unlocking the full potential of your data. With a tool like ours, you can not only explore big CSV files using filters and groups, but you can also visualize your data in the form of column graphs, pie charts, bars, histograms, and many other formats, thereby giving you the ability to identify trends and patterns, gain valuable insights and make informed business decisions.

Website Traffic Data Analysis

Visualizing Data from a Website Traffic Data Analysis Dataset

Don’t worry! This isn’t another one of those all-talk and no-action, all-theoretical blog posts that you’ll find on the internet. If you’re reading this post, I want to make sure that you learn how to smartly visualize your data and make meaning out of it.

So, here’s what I did –

I fetched a web analytics dataset from Kaggle comprising an eCommerce store's 2019 and 2020 web analytics data. While there’s no dataset description available on Kaggle, it seems that the eCommerce store was attracting traffic from different sources throughout the span of two years. The data is organized by month and year. This dataset includes metrics like the number of new users, number of sessions, bounce rate, page views, and average session time. Also, the data includes conversion rate, transactions, revenue, and quantity sold.

You can find the dataset on Kaggle here.

After going through this dataset, the marketer inside me started looking for answers.

I wanted to find out –

  • What the five biggest sources of traffic were.

  • What the five sources that drove the most traffic in November 2019 were.

  • What the five biggest sources of revenue in 2020 were.

So I went looking for answers by visualizing data using Gigasheet as part of my website traffic analysis.

Site Traffic Analysis

1. What were the five biggest sources of traffic?

If you got the chance to look at the dataset, you might have noticed that the eCommerce store is attracting traffic from different sources. We have hands on information like what traffic sources helped attract how many users every month in 2019 and 2020. We also have access to other similar metrics, but more on that later.

I wanted to determine the five biggest traffic sources for the eCommerce store. To find out, first, I grouped by the column group “Source/Medium.”

Website traffic analysis using Group by Source/Medium

Results -

Results of grouped website data

Next, I calculated the sum of the column group “Users” to gain insights into the total number of users different sources helped attract to the store.

Sumarizing the of Number of Users as part of site traffic analysis

While I can individually sort the column group “Users” in decreasing order, I wanted to visualize it to paint the complete picture in front of my eyes.

Gigasheet Data Visualization for website traffic analysis

Allow me to share the different visual formats I generated.

Column graph –