My Little LinkedIn Experiment

Launched in 2003, LinkedIn is now the world's largest professional network with 706 million members from 200 countries, and the #1 channel for B2B marketers.

Omnicore reported recently that LinkedIn has over 300 million active monthly users, and 80% of B2B come from LinkedIn (vs. 13% on Twitter & 7% on Facebook).

Like many other people who have a professional job (whatever that means), I also have a LinkedIn profile. My activity level ebbs and flows, but I've found it useful as a contact management tool and a way to stay connected with colleagues, clients and others.

Beyond that, I really haven't paid too much attention to LinkedIn, and not thought too much about it's usefulness - until recently that is, when I started posting daily links to Medium articles I was writing for a #30daychallenge.

Some posts seem to be getting more views than others, and I couldn't really figure out why.

I became curious about who amongst my approximately 1,500 followers, actually see my posts on LinkedIn.

On the last day of my #30daychallenge, I impulsively decided to run a little experiment to find out. (The things you get up to when you are in lockdown lol ??).

The purpose of this article is to keep my promise and share the results of my (hastily designed and tongue-in-cheek) experiment.

Experiment Design

1. Invite people who saw the LinkedIn Post to either like, comment, or PM me.
2. Track statistics - Views, Likes and Comments - over 10 days.

Results of the Experiment

There were 1,287 views, 25 likes (1.94%) and comments from 7 people excluding myself (0.54%).

The table and graph below summarises the number of views, likes and comments over the 10-day period.

(Note: Day 1 view is only for three hours between 8 PM to 11 PM. Statistics for the other days are all collected in the evening - give or take a couple of hours depending on how late I stayed up that night.)

Commentary/Analysis of Results

• I had assumed when I designed the experiments that people who "viewed" the post will click the post, and was surprised by how few people actually took part - 26 individual responses (like or comment) out of 1,287, or 2.02%
• Which lead me to think about what those views actually mean? When LinkedIn says that there were X views per post, does it actually mean that someone reads or even sees the post? If the answer is yes, then 1,261 people saw the post but take no action.
• I've made a key assumption, biased by my own personal preference - i.e. if one of my LinkedIn contacts posted something similar and I saw the post, I would help out and engage with the experiment. It's a no-brainer as it doesn't cost me anything other than clicking a button.
• Maybe people are just apathetic? Maybe the views don't mean anything? Of course, there is no way to really measure that - at least not as an end-user with no other data source apart from the statistics on my feed.
• Another thing that surprises me is the spread of the views across the 10 days - 50% of the views were in the first 2 days. After that it was a reducing trend, but with ad-hoc increases on some days. These appeared to be correlated to likes - i.e. more likes seems to cause a rebound in views.

What do LinkedIn views actually mean?

I did a bit more digging on what LinkedIn "views" actually mean:

• When you share an update, a "view" is counted when the update is loaded on the viewer's screen. Viewers do not necessarily need to click or read the update to count as a view, but rather have the update loaded on their Homepage. (https://www.dhirubhai.net/pulse/what-does-mean-when-someone-views-your-article-post-updated-johnston/)
• LinkedIn counts a post view every time a post is presented in someone’s home feed. That means the content might not have been read – it was just shown. (https://www.dhirubhai.net/pulse/linkedin-view-counts-explained-john-espirian/)
• In other words, your viewers could be thumb-scrolling through their feed and adding a view to the posts - even when they haven't read them.
• There's also differences in how LinkedIn counts "views" - for articles vs. posts vs. videos. (Refer to links in the first two bullet points).

Food for Thought

Below are some of the things that I'm reflecting on as a result of the experiment.

Attention economy

In 1997, Michael H. Goldhaber wrote that the global economy is shifting from a material-based economy to one based on the capacity of human attention. Many services online are offered for free. In the attention economy, attention is not only a resource but a currency: users pay for a service with their attention.

But with more and more content being produced and consumed online, I wonder if we're reaching saturation point, i.e. cognitive and information overload (even overwhelm).

Network effect

A network effect is the effect where an additional user of goods or services has on the value of that product to others - for example, online social networks such as LinkedIn, Twitter, Facebook increases in value as more users join.

As the number of people in a given person's network increases, does it mean that our network is less likely to see our status updates and posts?

In other words, as the number of users increases, is there a tipping point where the network effect starts to be negative - e.g. where more users make it less valuable due to "network congestion".

Algorithms

All of our social media and search engines are run by algorithms. These algorithms are opaque to the end-users and monetised - either by the platforms or by experts who specialise in them.

As we entrust more control to these algorithms, what's happening to our information flow?

Is it creating echo chambers that reinforce our biases, amplify differences and increase polarisation? What skillsets and capacities do we need to counter-act these effects?

Conclusion

I want to say a very special thank you to the 26 contacts who played along with this experiment. It has been instructive and valuable for me - and I hope you too got some value out of it.

It is ironic that even though most social media platforms started off to help people connect, I sometimes wonder if one of the unintended consequence is that it could be increasing disconnection.

As we are busily keeping up with all the news, updates, livestream and stories, are we also paying less attention to the things that matter.

This was brought home to me recently when I accepted a couple of requests from people that I haven't met - something that I don't normally do. But because we had a number of mutual contacts, I made an exception to connect and engage with them.

I am not sure if I'm bemused or offended - but it was only after I replied to their LinkedIn messages when I realised that I wasn't really responding to a "real" message or person, but some sort of automated response - like a chatbot.

Maybe this is the latest new thing - automate your pipeline, use saved searches to mine contacts, and then a funnelling system to help target your prospects. It feels so 1980s and leave a bad taste.

For me, this caused me to reflect on how I want to interact and has reinforced the need to go back to basics.

In my real life, I've a number of friends and colleagues who I know personally - the ones who'd pick up my phone calls or texts, who I'd trust and would turn to for advice, and vice versa.

I don't have many people on these list - maybe one dozen or so people, but these are also the people that I'd vouch for and drop everything to help out where I can.

These are the type of relationships where even when we have not spoken for years at a time, when we connect again, it is like I've just seen them yesterday.

Many of them are not on social media regularly, and I don't need them to like my posts to know that they are there for me.

Those relationships are not based on transactions, and they don't happen by accident. They are real and trusted connections that are built up over time.

Who cares how many LinkedIn connections we have, or how many views or likes our posts and articles get?

Just like in real life, it's the quality, not quantity, of our connections that matter.