Want to measure effectiveness of your LinkedIn updates? Use Analytics

Apr 2, 2021 | Blog

About Kuldeep Deshpande

I am a LinkedIn addict! It is my window to check what is happening in my areas of professional interest, my gadget to judge how people react to my thoughts.

Most of the active LinkedIn users are used to posting short updates using “share an update” feature of LinkedIn.  Number of views on your update tells you how many people may have seen your update. Effectively, number of views of your update is effectiveness of your update. However earlier this year, LinkedIn disabled the feature to view number of views on your updates.  If you post multiple updates in a day, at most you can know number of views only on 1 update. This was so disappointing.  I know many people have raised this issue with LinkedIn.

But there is a workaround!

We can use data from past LinkedIn updates on company page and predict views for your updates pretty accurately

If you post updates on company LinkedIn page, you can see number of views (LinkedIn calls it ‘impressions’), number of likes, number of comments etc. for all your updates. You can use this data to predict how your updates on personal LinkedIn profile will be received.

Data used for the predictions

The way LinkedIn updates work is like this – If I post an update and one of my contact (say Mr. ‘X’) likes the update, the update is put in the feed for all my contacts and X’s contacts. Thus more the number of likes to my update, the update is propagated to more people. So there must be a correlation between how many people like an update and how many people view the update.

I extracted the data of all updates from my company page. There were 30 updates that we had posted in last 2 months. For all updates, number of likes, number of comments, and number of impressions (views) was tabulated. LinkedIn defines ‘Interactions’ as the number of times people have liked, commented on, or shared each update. This data was used for coming up with prediction of number of views for an update based on number of interactions.

So here is how I did it

  • First step was to find Pearson’s coefficient between number of impressions and number of interactions. It came out to be 0.95 which signifies that there is significant correlation between the two. Value of correlation coefficient between impressions and likes was 0.93 and that between impressions and comments is 0.86.
  • Now that we found that there is significant correlation between impressions (i.e. views) and interactions (i.e. comments + likes + shares) next step was to model the relationship between the two using regression. I used excel for a building regression model.

No. of Interactions = No. of likes + No. of shares + No. of comments

  • Using the regression model, the relationship between Interactions and Impressions came out as follows:

(No. of Impressions) = 92.1 + 83.2 x (No. of interactions)

  • R-Square for this model came out to be 0.906. This indicates percentage of movement in number of impressions that can be expressed by movement in number of interactions. Thus a fairly significant number of impressions can be due to changes in number of interactions on the update.


During last few days, I have applied this equation to predict number of impressions on my updates on personal as well as company page and it has come out to be accurate with +/- 5% error.

What next

There are many way to improve this prediction:

  • The equation means that each of my contact who likes or comments on an update brings 83 additional views to the update. Now for someone else who has ‘rich’ contacts (i.e. contacts with large number of contact base), each like may bring significantly higher number of views. Thus average number of contacts of people who publish the update should be a factor to be considered in the analysis.
  • Time of the day when an update gets published is very important. If I publish an update at 3 AM, surely very few of my contacts will see it. It has to be a factor to be considered in analyzing effectiveness of updates.

For those who are interested in validating this model, I have built a simple spreadsheet to enter their own data and build prediction. Please contact me if you would like to get the spreadsheet.



Everything in LinkedIn can be explained statistically!

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