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The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.
A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!
Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.
The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.
A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!
Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.
The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.
A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!
Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.
LinkedIn must know nobody's using their analytics.
Let me explain.
Have you recently looked at your LinkedIn "Content performance"?
This is what you'll see
At first glance you might think: Hm, this looks good. It shows:
1. Evolution of impressions over time
2. Total impressions for the selected period
3. By hovering over the line, you see the data for that day and change compared to previous day - pretty cool
4. You can change the time period
5. You can change between impressions and engagements and even have a definition of each
6. Heck, you can even see the change for the selected period
Below the line chart you also see a list of your top performing posts, in descending order by number of impressions or engagements.
So what is wrong with this?
Glad you asked.
Try looking now at the line chart and your top posts on your own account.
Do you see what's missing?
That's right. You can't see your posts on the line chart.
This means that you don't know what posts generated each spike in impressions or engagements.
I think this is arguably the most important thing you should know from this analysis.
The only way to determine when you’ve posted is through a mental, eyeballing exercise and navigating between your list of posts and the line chart.
For older posts it’s impossible to pinpoint the post date on the line chart as LinkedIn doesn’t show their exact post date.
This is not all, though. There is much more that LinkedIn could help us visualize.
Instead of explaining what LinkedIn could have done, let’s follow along a visual redesign of the analytics page and gradually learn how to bring analytical value to the users.
In this exercise, we will respect the general design of the current LinkedIn Analytics page while also remembering that the page must be mobile friendly.
This will show that building rich, insightful charts does not and should not come at the expense of design.
So, without further ado, let’s start with the current line chart.
We’ve already mentioned a few strong points of the chart. I’d also like to add to that the line chart is a good choice for representing content performance, impressions in this case. That’s because we want to track impressions over time and line charts are probably the best way of abstracting and visualizing time.
So let’s tackle the above-mentioned problem, the fact that you can’t easily pinpoint your posts and activity on the line chart and understand the impact of your activity and content.
There’s a very easy way to fix this, actually. We just need to mark each post date on the line chart. We mark them ... well, with markers.
Easy enough, we now see when you’ve posted in your timeline. With this simple addition, we can start to understand some of the spikes. We still can’t say which post moved the line up, though.
This has an easy fix too. We can simply reuse the hover and tooltip features already implemented and show a few more details about the posts when hovering over the markers.
And that’s it. In these very simple 2 steps, we’ve managed to show the user what they couldn’t see before and visualize the impact of their posts.
Now, I can say, “well, my post from May 19, 2023 generated 1,961 impressions”.
I think it’s fair to assume this could literally take even a big company like LinkedIn with all of their complex decision and approval processes, less than a sprint to develop :). So they definitely could’ve done it. They just chose not to. Why? I have my theories, but my rant about this is further down.
Right now, let’s keep the momentum and see what else can we do to make this even better. And, as you’ll see, we can do much better.
The attentive reader might have already spotted a latent problem with our new design. How do you show the data when you have 2 or 3 or 10 posts in a day? We all know people who post more than enough times a day on LinkedIn, right?
Let’s look at an approach to handle multiple posts per day.
Firstly, we can change the details of the tooltip on marker hover to show only the number of posts in that day, showing a dot for each post up to 5 dots.
Here is where the mobile and desktop design can split.
On desktop, we can bring that “Top performing posts” section to the right of the line chart.
This section normally sits below the line chart and on a normal laptop monitor can hardly be looked at concomitantly with the line chart. And again, we don’t need to redesign the entire page for this.
Pro tip: It’s a good information design practice to show related data in the same viewport.
The user should not have to scroll, change tabs and thus lose context when looking at related data points.
Doing this allows us to do the following awesome thing that so few dashboards do.
Hovering over each marker will now show us the tooltip, but it will also dynamically filter the section on the right and thus, help you see all the details you need about your posts.
I would say, that only now, you have the complete picture of the performance of your posts. Now you can clearly see:
On mobile, I think there’s a case for leaving out these details since mobile should be easy to digest. If I would add these details on mobile, I would add them like so.
Clicking on the marker would open up a model with the same content we put on the right of the line chart on desktop.
For the remainder of the exercise, I’ll only work with the desktop view. It’s ok if advanced analytics are only available on desktop. Not everything can and should be consumed on any device.
We can even add an “advanced” section on the analytics page. We’ll use it later.
What else could we add to the design we have made so far to make it even better?
For starters, I would add more on the right side of each post. There is so much space wasted there.
I would add engagements and a simple ratio, of impressions to engagements (I/E). This is an easy calculation that can give you an idea of the impact of your posts. We’ll come back to impact.
See how much more useful the post cards become.
My top post by impressions has a ratio of 402,9 I/E. Or, in other words, it took 402,9 impressions to generate an engagement.
Another thing I would add to these post cards is a label that shows the rank of the post compared to all other posts.
This is useful when looking at specific dates and seeing the rank of each post on that day.
That’s it with the post cards. Nothing fancy and pretty easy to implement.
Now let's redesign the "top performing posts" section.
We don’t want to lose any information that was available before with the redesign.
For visualizing rankings, bar charts are usually a good choice - certainly better than showing individual cards ... and only 3 of them.
A bar chart where posts are sorted descending by impressions and where we show at least the top 20 posts would already be much more useful.
Let’s add bars for engagements too so we won’t have to make any clicks if we want to see engagements.
For clarity, let’s add some detail. We can show the first few words of each post, since we don’t have a better way of identifying each post. We can then add data labels next to each bar. Finally, I would add the post date to help place the posts, at least mentally on a calendar.
Of course, to really help vizualise these top performing posts in context, clicking and hovering on each of them should highlight the marker on the line chart and filter the section on the right.
Let’s come back to impact.
When someone looks at the LinkedIn analytics page, they might be wondering
“How are my posts doing?”
With the redesign, I think we can help them answer that question in an elegant way.
But we can do more for our users. And if we can do more, why not do more for them? Eh? ... LinkedIn?
So how can we help more?
How do you show the impact of the posts of the users?
This is a more loaded question and needs some definitions. How do you define impact?
In an ideal world, we would sit together with our users and learn from them what post impact is for them.
Let’s assume our users either don’t know or that we know better than them.
No, really, for the sake of the exercise let’s assume post impact is measured by a combination of:
1. Quantitative measures
2. Qualitative measures
Get the idea? Defining impact is an exercise and a lot of ingredients can go into it. Let’s go with the above definition and mix and let’s also assume we’re not smart enough to weigh all these ingredients to calculate a singular score, similar to the Morningstar rating score.
So how do we inform our users of this impact and help them visualize it?
Let’s take our redesigned analytics page and let’s build everything in the “advanced” analytics mode.
I will propose upgrading the same 3 sections to stay in the same design patterns.
Let's start with the bar chart.
This is a good place to include all our quantitative measures. A simple table is a good way to visualize them. By adding a sorting option for each column, we will be able to see the posts by the metrics we’re most interested in.
Let's continue with the right side.
We’ll keep the few post details, but expand the metrics to include the quantitative ones.
Let’s also expand the engagement section to include the number of engagements for each type.
Let’s also include the qualitative measures. This would be a perfect use case for an AI engine that selects the most relevant people that have engaged with the post. Let’s assume that the engine is built and we just show these people in the available space.
Lastly, let's upgrade the line chart.
By clinking on a marker you would see the each post on the right side but, this time, we’ll also show a different marker for each post and their separate area charts.
The markers and areas now represent the individual post impressions. Of course, when having multiple posts in a day, the areas can create clutter.
We can use the hover feature to help not only visualize the impact of each post, but also provide more details.
You can now see the evolution of any post and uncover more insights. For example, I found that, in general, a post posted on a Friday usually generates less impressions, but it generates impressions the following Monday.
This concludes our redesign exercise.
The experienced data analyst would talk about small multiples and comparing all the metrics over time using IBCS visuals, giving the user more filtering options and more.
Even after this redesign, more advanced things can still be done, I agree.
I didn’t want to turn the LinkedIn Analytics page into a dashboard, though. LinkedIn should remain friendly, easy to use and appeal to most users.
The question I have is why do these companies offer only the bare minimum analytics?
I’ve had this question for a few years now and the more I work in data analytics, the more I see businesses exporting data out of the platforms they’re using to create analytics because they can’t get anything premade that’s useful.
For a while I had a theory where I said “well, their developers are focused on functionality, rather than analytics. Creating useful, interesting charts is expensive and time consuming”.
Looking at LinkedIn analytics in detail, I am less convinced about this argument. We saw how easy it was to add more value.
My new theory is that the platforms we are using value simplicity over usefulness because they don’t believe data analytics bring significant value to them or their users.
You are given a simple line chart, but you can’t extract any meaningful information from it.
You are given your top 3 most performing posts, but out of context and with no means of analyzing the reasons that made them the top 3.
This is even more frustrating when these companies take all the data they can from you. I don’t want to jump on the “blame the big corporations for collecting all our data” train, but we should expect more in return for the data we give them.
My new theory is that the platforms we are using value simplicity over usefulness because they don’t believe data analytics bring significant value to them or their users.
You might say, “but hey, you can export your LinkedIn data and do your analytics yourself”.
Firstly, you can’t export all the data you need in order to build the visuals we created in this exercise. If you will download your data, you’ll see that LinkedIn doesn’t give you the impressions and engagements by post. It aggregates all posts.
Secondly, why would I need to do any work in order to see some useful stats about my LinkedIn presence and impact? I spend enough time providing LinkedIn all the data so they can use it for growing their business and revenue. I think we should get more value for the data we provide them.
I think that the majority of businesses miss out by not including analytics in their core business. I don’t think every SaaS should provide dashboards to their users, but I think analytics should be gradually and thoughtfully incorporated into the core product.
I plan to do this exercise for other platforms and give more examples how analytics can improve the SaaS products we use every day and generate value for both the business and their users.
And LinkedIn ... I’m just playing with you. And ... you’re welcome ;)
