30 Days and 6800 Views. What I learned from tracking my LinkedIn posts for a month.

Summary: LinkedIn has horrible data insights for premium members and does little to actually help its members increase their personal brand’s value within their network. Also, its probably not worth the effort to attempt to post every day, but garnering likes and comments can exponentially increases your post views.

Dwight’s LinkedIn Analytics Dashboard in PowerBI

Background

A little over a month ago, I decided to double down on my efforts to expand my exposure to the LinkedIn community. My effort focused on regularly sharing content I believed would be valuable for anyone trying to understand salesforce effectiveness (SFE), sales operations, and general process improvement best practices. What started as a simple attempt at posting every few days quickly turned into a mini obsession to post daily and track every possible data point I could get my hands on.

The Approach

There were several challenges with this. The first obstacle I ran across as finding fresh content. Within a few days, I realized that I tended to focus on too few sources for continuing education. This was a mistake. I was putting blinders on my own viewpoints and I realized that my network would get bored if I just posted links from the same three or four websites; nobody spends time on any social network hoping to see the same thing repeatedly. I spent the better part of six hours per week actively seeking out new SFE blogs to follow, new thought leaders to absorb from, and identifying new, creative sources to generate interesting posts.

The second obstacle was getting reliable data to track. I used Buffer to manage my posting activity, but I found that its own analytics were largely inconsistent with the data I could obtain directly from LinkedIn. Post view counts and comment/like counts never matched. Notice that I didn’t say their analytics were unreliable. You can’t get direct access to either Buffer’s or LinkedIn’s data, so its impossible to know whose data is correct. Which leads me to LinkedIn’s data analytics “efforts”.

LinkedIn has been touting how great its Activity analytics are, but to be candid, they suck. None of their analytics help me know how to increase my value to my network (or even easier, how to to expand my network). I can see details on the sources of my post’s views, comments and likes. Yet I can’t do anything with that summary view to figure out who is actually viewing my content. For example, it would be great to know who in my 2nd degree networks is looking at my posts so that I can decide if I want to reach out to them for a possible connection. LinkedIn doesn’t let me do this. Why am I paying for a premium subscription with them? This is a huge source of frustration.

None of this data is very helpful as you can’t click on anything to drill down.

My workflow was fairly straightforward: I would find content worth sharing, create a post in Buffer and add comment that would double as a pitch to read the content. After the post was uploaded by Buffer to LinkedIn, I would track the relevant data points in a data model in Excel.

The Excel Data Model

The data I chose to track grew as I learned more each day, but generally I was trying to capture the details first: content type, source, purpose, posting date, etc. The second set of data centered around daily views, likes, comments, profile views, new connections and how the view totals changed day to day.

I ran into a few issues with LinkedIn that hopefully they will fix soon. First, because I didn’t capture all of this information from day one (it changed as a I learned), it was impossible to go back and track a few things perfectly. LinkedIn doesn’t provide users with time stamps of profile views. This may not seem relevant, but it actually makes it a little difficult to track any view older than 24 hours. They also appear to only hold a 30 day cache of your post views. This is really irritating as it makes any long term trend analysis impossible.

I didn’t have many preconceived notions about potential outcomes of this, but trends in the data were obvious and will change my posting behavior going forward.

Before we get into the data, a note about one last tool in this workflow. The output of the Excel data model was used to create a dashboard in Microsoft PowerBI. PowerBI is an amazing tool and has easily caught up with Tableau in its feature set. Is it perfect? No, and neither is Tableau. The beauty is that its very easy to train on and use, and if you run Office 365, its free. Tableau’s licensing runs about $1,000 per seat, plus $10,000 per server.

Edit: Here’s a link to my PowerBI Report: https://goo.gl/gic7nT

When and What You Post Matters

The least surprising data (but still incredibly useful) was that not everyone reacts to all posts the same way. Reviewing the pie chart, it was pretty disappointing how much more favorable Harvard Business Review posts were compared to the other sources. Some of their web content is pretty good (the authors aren’t the same as the printed periodical), but its still a magazine. Just because it costs $20 per issue doesn’t mean you shouldn’t forget that its still advice from a magazine.

The second observation was that the data suggests not everyone checks LinkedIn every day. After 4+ weeks of posts across all seven week days, there were very obvious trends in activity. It will likely be different for everyone but Sunday/Monday and Wednesday/Thursday buckets tended to drive the highest amount of views, regardless of post content.

People Want Variety

Percent Change in Daily Post Views from Previous Day

This was mind blowing to me. As I watched the post view numbers each day, I noticed that the numbers didn’t appear to trend up or down. That is, after only a week or so, it was obvious that more posts did not equal more post views. I decided to track how the post views changed on a daily basis and then I used a waterfall chart to plot the outcome.

Looking the data in this way clearly calls out a trend in my audience’s behavior: With one exception, any popular post (my network isn’t entirely large, so “popular” is a bit of stretch) always resulted in the following day’s post being viewed by a smaller audience. Clearly, people want focus their time on fresh and new angles in their feeds. What’s further interesting is that the biggest posts usually resulted in the following two or three days’ posts seeing a smaller viewership.

This view coupled with the trends per weekday above should make it obvious that posting daily isn’t doing much for me.

Increasing Exposure

Some Simple Regression Analysis

The last observation I have from the last 30 days is this: Popular posts don’t drive Likes and Comments, Likes and Comments make posts popular.

Ironically, one of my posts early on in this exercise talked about regression analysis. And while PowerBi wouldn’t easily give me the R-Squared value for the top row of graphs, I know from a separate analysis that they are both well above 0.6 (two items can be considered correlated generally when their R-Squared value is greater than 0.3).

Correlation, however, doesn’t mean, causation. I had to watch the data more closely each day to draw my conclusions. As I got smarter about knowing there was a relationship between my post likes/comments and views, I would try to eyeball the growth patterns after a like/comment happened. What I found was that the more popular a post, the more likely that a large percentage of my viewership was outside my 1st degree network. This popularity then had to be a result of my direct connections triggering their other direct connections to look at the post.

The effect was definitely exponential. Three likes would drive 4.8x as many post views as zero likes, and five likes would drive 8.9x as a many views. The data around comments was largely the same. The biggest letdown though? That none of this activity increased my network size.

Tracking your personal brand’s relevance is tough. For one thing, my end game here wasn’t new employment but rather increased exposure. Did I achieve that? The data would suggest so. However, none of that exposure actually converted to new invitations to connect with anyone.

The other challenge with managing your personal brand isn’t too dissimilar from managing a corporate brand. You might hope that the drip campaign will result in near term success, but more than likely it will result in some benefit for which you won’t be able to identify the cause.

Takeaway

There are some thought leaders out there who seem to believe its their obligation to like or comment on every article in their feed, and to post content a few times a day. The data shows that’s a waste of time and might even be counterproductive.

The other takeaway is that LinkedIn has dropped the ball delivering their own value proposition. I pay them $30 per month for a narcissistic view of who‘s’ looked at my profile, but they do nothing to actually help me increase my network’s size or to improve the value I bring to that network. I don’t know if I can trust the post analytics they supply and there is no ability to drill down into the analytics they do provide. Useless.

The Secret Recipe

Short of being famous, I found that there is a loose formula for success.

One, stick with certain days of the week for your posts. Post content that’s from a popular source. Have a compelling pitch to convince your audience to actually read the material. Finally, encourage your network to participate.

That last piece is probably most critical. I have some friends with ten to twenty times as many connections. The most popular posts tended to have their digital fingerprints on them.

You can expect my content to take on a different mix beginning this week. My intent is to deliver more original content (like this, but shorter) and to trust the data that fewer posts per week will still keep me relevant.