Want Your LinkedIn Post To Reach Thousands of People? Use This Trick
Data Science can predict what kinds of posts can go viral based on the nature of posts that got viral in the previous days and months.
Today we have so many tools that makes it possible for people like me who don’t have a thorough knowledge in Data Science to still leverage data, analyse it and gain insights.
I used Tableau to visualise a large dataset containing 34000 viral LinkedIn posts. Before this experiment, I had not much experience in Tableau. But this and this YouTube videos helped me easily master Tableau in a span of 2 months.
So let’s begin!
What makes a post go viral? Is it more of an emotional thing than a logical one? Or is there a pattern behind virality? Most people tend to become uncomfortable when it is revealed that our thoughts and emotions can be predicted. In fact, we have a term for that — Sentiment Analysis.
So the pattern is not in the viral posts — it is in the emotions of the people. Once you can gauge what kind of emotion prevails in a particular social networking platform, you can leverage it to make custom posts that are more likely to go viral. And the best way to do that is to analyse a large set of posts to see which of them struck a chord with the audience. I did just that!
Using Tableau To Analyse 34000 Viral LinkedIn Posts
I used Tableau to visualise a large dataset containing 34000 viral LinkedIn posts. These posts are made by influencers like Simon Sinek, Richard Branson and the likes.
Step 1 — Downloading The Dataset From Kaggle
I downloaded the dataset from Kaggle. You too can do so by clicking here. It’s amazing that this valuable dataset is freely available.
Step 2 —
I created a Tableau Online trial account to visualise the dataset. Look, you can’t figure out anything useful just by looking at the CSV file. There is too much information there. You have to use B.I tools like Tableau or Power BI.
So I created a workbook on Tableau and uploaded the CSV file containing the LinkedIn data
Step 3 —
So now I have all the raw data on the left panel. I can drag and drop relevant data to see granular information and hidden insights.
What I wanted to do first is to exclude the data of people who are too famous — like Richard Branson. Why? Because the average person likes and engages with their posts after getting awed by their persona. So there is a bias that works here. Their reputation precedes their posts. To get a correct picture, I had to remove such people from the list.
So what I did was I dragged two raw data values — ‘Name’ and ‘Reaction’ — to the Columns and Rows field respectively -
Tableau presented this data visualisation after I dragged the values -
I excluded Richard Branson, Simon Sinek, Kevin O’Leary, Ian Bremmer and Vani Kola. These people are too famous in their respective fields and thus people have bias as far as their posts are concerned.
Step 4
What I wanted now was to see exactly what kind of content they posted that got them so many reactions (reactions = likes on LinkedIn).
I just dragged the raw data titled ‘Content’ from the left panel. (Also I removed the ‘Name’ value from the columns section as that would have created information that I didn’t need. I just wanted to see the nature of the viral posts.)
There was too much data.
To filter the data, I clicked on the ‘sort in descending order’ button. So now the first bar chart was displayed in the high to low order.
I wanted to further filter the visualisation to exclude posts that got less than 2000 reactions. I did that by dragging the ‘reactions’ raw data into the filter box and added a range of minimum — 2000, maximum — 12000.
SO HERE IS THE GOLD MINE. I HAVE IN FRONT OF ME — THE MOST VIRAL LINKEDIN POSTS.
Now Let’s Examine The Nature of Top 10 Posts —
The post that got the highest number of reactions was —
The post that got the second highest number of reactions-
The post that got the third highest number of reactions was
The post that got the fourth highest amount of reactions was -
The post that got the fifth highest amount of reactions was-
The post that got the sixth highest amount of reactions was-
The post that got the seventh highest amount of reactions was-
The post that got the eighth highest amount of reactions was-
The post that got the ninth highest number of reactions was-
The post that got the eleventh highest number of reactions was- (Number 10 was some kind of an image post. Hence excluded.)
If any of you want the full data, I can happily send you the full Tableau dashboard file. Just mail me.
Insights From The Visualisation
So what exactly do we learn from this visualisation?
- 9 out of 10 top LinkedIn posts contain- I, Me, My or You. This indicates that subjective posts work best on LinkedIn. So don’t shy away from giving your personal opinion on LinkedIn.
- Very often we think that LinkedIn is not the place to talk about our personal lives. But these viral posts indicate that posts on personal affairs are quite popular on LinkedIn. So share what’s going on in your life.
- Examine carefully. You will see that many of these posts talk about personal achievements. We often think that talking about personal achievements is obscene. But as long as you don’t present these personal achievements in an ‘in your face’ kind of way, people will love to hear about them. They get motivated hearing about these achievements.
- Soft rants — rants that are harmless are also popular on LinkedIn.
- Examine the top 10 posts. Do you find any jargon? No! Keep your LinkedIn posts jargon-free.
- Most of the top 10 posts that I presented preach something. But the advices are not directly pointed to the readers. So theses posts are insightful, but not preachy in a negative sense.
If I you figure out any other pattern, please tell me.
Once again, thanks to those YouTube channels that made it so easy for me to handle Tableau.
