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.
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 –
So I went looking for answers by visualizing data using Gigasheet as part of my website traffic analysis.
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.”
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.
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.Allow me to share the different visual formats I generated.
Column graph –
Pie chart –
Bar graph –
Inside Gigasheet, you can hover over the different lines to make sense of the data like this –
Judging by the data, the top five traffic sources were A, B, C, E, and F.
You can also perform the same operation for two different column groups; let’s say we do it here for users and new users.
Column Graph –
Bar Graph –
You can also apply the 2019 filter prior to grouping the data to visualize this data on a yearly basis. Or you can group data by first the year, then months, and finally by source to gain an understanding of the data on a monthly level.
Let me show you how.
To perform this operation, I applied three layers of grouping –
Now let me visualize all the data under 2019 -> 11.
Column graph –
Pie chart –
Bar graph -
Judging by the data, the top five traffic sources in November 2019 were A, B, E, H, and I.
Now, you can easily compare this with data from a different month – like December 2019 for an MoM comparison or November 2020 YoY comparison.
The next question I had in mind was – what the top five months that drove the highest revenue were in 2020
Remember, here the data from 2019 is useless to us. We want to find out what the five biggest sources of revenue were for the eCommerce store in 2020. So, here’s what I did to find out the answer.
First, I applied this filter to get my hands only on 2020 data.
Next, I grouped data by the column groups “Source/Medium.”
The total revenue for all the sources was automatically calculated by Gigasheet. If not, you can calculate the sum manually. Next, I visualized the data –
Column graph –
Pie chart -
Bar graph -
Doesn’t that put you on the edge of your seat?
When trying to explore a big data set of site traffic, visualizations really help tell the story. You just need the right tool to enable charts and graphs on such a big data set! With our platform's outstanding data visualization capabilities, you can visualize all sorts of data inside Gigasheet.
And you know what the best part is? Gigasheet is free to use. Absolutely free!
Oh, and did we tell you, you can validate millions of emails within a few clicks with Gigasheet’s email verification functionality? All you need to do is upload a spreadsheet with CSV files, and you’re good to go. Learn more about the email verification functionality here.
And if you want to try out Gigasheet and unlock its full potential, sign up today!