Which companies are interested in Pain? The useful analytics below LinkedIn likes & comments.

I believe LinkedIn is a great source for keeping up to date with content which can be tailored to be most relevant to you. Over the past year or so I have increased the posts that I have been making on LinkedIn and wanted to share some insights with regard to the analytics these can provide.

Posting consistent content such as articles, blogs, videos etc creates a valuable source of analytics often hidden from all but the individual content provider. Each has post has a visible analytic of:

• Likes

• Shares

• Comments

These give everyone a top level overview as to how the post was received. Underneath this is also a general tracker of how many people actually interacted with your post – an overall figure which gives an idea of how far your post reached into your linkedin community.

Perhaps the most interesting data lie underneath this, where LinkedIn gives and additional layer of analytics showing:

• Company

• Position

• Location

Of the person interacting with your post.

If one takes the time to collect this data it can start to generate an interesting analytical profile for areas and companies taking interest in the content posted.

Here I share three parts of analytics from recent posts that I have made.

Takeaways based on these examples.

Locations interested in Pain:

• Boston

• London

• New York

Companies interested in the topics from these examples.

Consistent interest (all topics)

• Grunenthal

• GSK

• Pfizer

Specific interest on one topic:

• AZ

• Jansson

• Biogen

Here I have shared just three of my latest posts. When one is posting regularly a dense database can be built. One can cross check via subjects and strategically post content to validate the analytics generated.

For most of us it can be immensely useful to know which company is interested in our area of research in general as well as knowing which company has specific interests in certain subjects.

It is likely underappreciated that a highly valuable analytic data set can be collated by spending time to create posts and then taking the time to collate the analytics associated with them.