How I created an Analytical tool for LinkedIn using Google Sheets
By: Chaitanya Arora
Tags: Marketing, LinkedIn, Growth Hacking, Analytics, Google Sheets

How I created an Analytical tool for LinkedIn using Google Sheets Chaitanya Arora Follow May 11 · 4 min read

My Motivation!

So I started using LinkedIn a few days back and ever since I was fascinated by the knowledge-rich content people regularly post on this platform. Inspired by others, I started sharing my insights and knowledge through some posts that I thought others might benefit from. Being from an analytical background, I looked at the numbers, i.e. the amount of reach and engagement my posts got and soon I felt the need for an analytical tool. I searched a lot online but could not find any good solutions, most of them were paid and only for LinkedIn Pages. Thus I came to the task of building a basic tool to track views, likes, and comments on my posts!

So what came to my mind?

Being already accustomed to the world of scraping and web development, my first go to was to build a python script to scrape the required data and then deliver it to a web application to analyze it and show me the insights. But all this required a decent amount of time and knowledge to make a proper working application. As this was meant to be for personal use I chose to simplify my work and do something so that I can visualize the data easily and create insights without much coding.

The best tool that came up to my mind was something we all have been using from school that is Microsoft Excel or nowadays Google Sheets. My idea was simple, get the number of views, likes, and comments from my posts on a regular interval of time and then insert it into a new row. Once I have enough data, I can plot graphs between various columns and deduce inferences from it thus helping me to get a better understanding of my audience.

So what did I do?

To get the data into my Spreadsheet I considered exploiting the power of Google Apps Script’s integration with Sheets to interact with the outside world and append data onto new rows. Using a script, I got the numbers from LinkedIn and appended them to come up with the above sheet. Now applying my knowledge, I could use these numbers to make inferences and plot graphs to get a better understanding of my audience, based upon the data I plotted a timeline of views, likes, and comments on my post vs time.

Here, in this next graph, I can see that more than 50% of my views came in the first 5 hours between 10 am and 3 pm where my post acquired the initial boost to spread through the network. Also, I can see that the effect of the comments lasted for about an hour and brought in some more views.

But the most important inference I could get from the chart is that most of my viewers are active during the day around 1:00 PM and continue to use LinkedIn. This trend is unusual from what the internet suggests but as we all know the Global Pandemic we are facing right now could lead to this kind of behavior as most of my connections are young people who are still in college and tend to be free around this time.

So whats next?

I plan to use my script and gather more data until I can fully understand my audience as making inferences from just 1 post is kind of wrong as data is insufficient. Therefore, I believe if I keep analyzing my posts, one day I will be able to understand what my audience wants and can create the right content for them, thus making the best use of this platform for all of us.

So if you have any queries or suggestions, send me a connection request, mention in the comments, or drop a message. If you are interested in knowing how I fetched my data or about the script I used, do let me know!

Connect with me on LinkedIn for more awesome posts and updates.

Thanks.