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In this episode of So What? Trust Insights weekly livestream, you’ll learn how the LinkedIn algorithm works. You’ll discover how to create content that gets seen by the right people and how to avoid common mistakes that can hurt your reach. You’ll also learn how to use generative AI to evaluate and optimize your LinkedIn content for the LinkedIn algorithm. Finally, you’ll learn some practical tips for getting more engagement on your LinkedIn posts.
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In this episode you’ll learn:
- The latest updates to the LinkedIn algorithm
- What your best practices are for visibility with LinkedIn’s algorithm
- What doesn’t matter to LinkedIn’s algorithm
Transcript:
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher Penn – 00:34
Welcome to “So What,” the Trust Insights live stream. I feel like we need to update the tagline because analytics and insights are still relevant, but we cover a lot more. Like today, we’re going to be talking about the LinkedIn algorithm. So how are things, John?
John Wall – 00:52
Good, good. I’m excited to dig into this. You’ve been covering LinkedIn for years, but it’s always interesting to see what’s going on as far as what types of posts work. We had over in “Analytics for Marketers” today, talking about what people think about LinkedIn and where things are, and people’s opinions are all over the place. So yeah, I’m excited to see what you’ve dug into.
Christopher Penn – 01:13
So here’s how this works. If you are not familiar, Trust Insights has been publishing the unofficial guide to the LinkedIn algorithm for marketers. We’ve been publishing this for several years, and the way we create this guide is pretty straightforward. LinkedIn is one of the few social networks where their engineers publish what they’re building and how they’re building things on their engineering blog, on Archive.org, and a few other places. Just the other day, they were talking about designing for scale, and they have this whole blog post that talks through the queuing system for message queuing in the backend architecture. This is pretty cool stuff.
Christopher Penn – 02:04
What this means for a lot of us who are in marketing is that they’re telling you how LinkedIn works—how the newsfeed algorithm works. The challenge is distinguishing between Kafka and RabbitMQ for message queuing doesn’t translate into easy-to-understand practices for the average marketer.
John Wall – 02:29
So yeah, I’ve not played with Kafka.
Christopher Penn – 02:31
Recently, Apache Kafka is a pretty cool piece of software, but that’s another show entirely. What you can do, and what we do, is we grab this content from LinkedIn and put it into language model tools like ChatGPT, Google Gemini, Anthropic, Claude, etc. We’ll grab 70 to 80 different papers, posts, and technical diagrams that they have and put it into a language model and say, “Now, based on all this information, summarize it and tell us how the LinkedIn feed actually works.” What’s great about this is it allows you to understand the technical language in terms of how it impacts our work as marketers, as opposed to—I mean, some of the details are extremely relevant, but they’re buried under the technobabble, under the jargon.
John Wall – 03:30
So how much raw material do you run through this when you do this update?
Christopher Penn – 03:35
Our current update is about 30 different sources. So these are the blog posts and the papers. It’s about 120,000 words. It’s in the download. You can actually go to the original post if you want to check out anything. It’s not a secret.
John Wall – 03:55
Very cool.
Christopher Penn – 03:56
So what we do then is take that analysis and structure it into a guide. This guide is available on the Trust Insights website. If you go into the insight section—I probably should have put up a short URL, but we’ll do that after the show—we say, “Okay, with all these things, how does it impact our work? What do we do?” So I figured today on the show, we’d walk through the way the LinkedIn system works and then what we should be doing as marketers for it. Now, I’m going to start off by saying there is no such thing as the LinkedIn algorithm. The algorithm doesn’t exist because there are 12 to 15 different pieces of technology interacting with the content that you post, making decisions, and showing it.
Christopher Penn – 04:48
So the idea that there is this one mythical master algorithm that’s making decisions is not true; it’s an ensemble of different systems working together. That means, in turn, as a marketer, if you see someone hawking the “secret algorithm,” no.
John Wall – 05:08
More snake oil.
Christopher Penn – 05:10
You’re not going to fool 14 different systems, some of which are explicitly designed to look for garbage. What they do is take into account a bunch of different factors that we may not necessarily think about as marketers and assemble them. The system can broadly be broken down into five major stages. The first stage is what I would call the annotation stage, content creation. So you write a post on LinkedIn. You write your post, you hit submit. What happens? First stage: feature extraction. LinkedIn software looks at the different features. What is the format? What are the words that are used in it? What are the images? What’s the video? Is there audio? And then they map it to the knowledge graph.
Christopher Penn – 05:56
The LinkedIn knowledge graph is essentially a vectorized embedded graph of probabilities of what connects to other things. So in this first stage, LinkedIn wants to understand what relationships your new post on LinkedIn has to everything else that exists on LinkedIn. That extraction creates a system—I would almost call it an index card’s worth of probabilities—that’s going into the ranking system. No ranking has occurred yet. It’s just trying to digest your content. What you need to know here then is—and this is spelled out in the first section of the guide—you want to put in things that the feature extraction can understand. Things like hashtags—they are taken into account—mentions of individuals and organizations. Your own profile as the author has to be complete and robust. Things like when it’s being posted do matter.
Christopher Penn – 07:00
The format of the content tends to matter for generating good features. That’s part one. It’s like one of those Rube Goldberg machines. The second stage is first pass ranking of these different candidates. The big one that we need to know about is called Follow Feed. Follow Feed is a ranking system that LinkedIn uses that tries to determine what is likely to resonate. Follow Feed looks at a few different things. Who are you connected to? Your first-degree connections are important. And then, second, are those first-degree connections likely to interact with your content? LinkedIn is going to be looking at your content, your profile, the words and phrases and expressions and jobs and titles, and all that stuff that’s in your profile, and then doing the exact same thing for your first-degree connections.
Christopher Penn – 08:00
Who are those folks? What is in their profiles? What do they write about? What are they commenting on? All the words, phrases, and characteristics. The Follow Feed first pass ranker essentially tries to build a set of guesses as to who in your immediate network would find this content potentially useful. It basically comes up with a scoring list and a list of names at this point.
John Wall – 08:27
So it is critically important who your first-degree followers are then? Because that will determine where stuff shows up on that first pass.
Christopher Penn – 08:36
Exactly. So if you are marketing to a specific type of audience, you should be connected with them on LinkedIn. If John, if you were a buyer of mine, I would want to be connected with you. If you are not a buyer, I might not want that connection because it might not be relevant. So the folks who have a gazillion connections are potentially not reaching their audience because they’re maybe connecting with more irrelevant people than relevant people. When these embeddings are made, they’re made on probabilities, they’re made on guesses like who’s going to be—who should be seen—who should even see this content. The other thing is, it matters what the content is. So if I put up a post on podcasting, and John, you have a lot of podcasting content in your own profile and in your own feed,
Christopher Penn – 09:36
The probability of you seeing that particular post is pretty high. If I put up a post about pogo sticks—Katie’s favorite example—and you have nothing at all in your profile about pogo sticks, even though you’re in my first-degree network, you’re probably going to be less likely to engage with that. So the first pass ranker may say, “Maybe John’s not a good candidate for this particular piece of content.” So once your post passes first pass ranking, it goes into second pass ranking. This is where a lot of very interesting systems refine the guesses to decide who to serve it to. There’s a few different things here. There’s things like dwell time, a multi-objective optimization ranking. This is where the rubber meets the road. First, the dwell time. Dwell time is measured in two ways.
Christopher Penn – 10:34
LinkedIn’s backend systems try to guess how long a user is going to spend with a piece of content. So if you’re on your phone and you’re swiping through LinkedIn, and you see a post, and you and I are connected and you see a post, and you just scroll past it, you’ve effectively given it a dwell time of zero. LinkedIn monitors not only engagement with, but just how long you view, any particular piece of content. That goes into one of the two dwell time predictions. The P skip model is the short dwell time one saying, “What is the probability that John’s just going to skip this post?” And then the longer one is, “What is the probability that John’s going to spend some time reading this post and maybe interact with it?”
Christopher Penn – 11:17
Those are two parts of the second pass ranker. Part of what you’ve got to do as a marketer is figure out how to hook someone’s attention, get them to stop scrolling.
John Wall – 11:29
That’s interesting. So both of those characteristics are tracked. One is if there’s zero dwell time, you get the zero, but if there’s stuff where you stay twice as long, that increases the probability that it’s considered quality stuff.
Christopher Penn – 11:43
That’s exactly right. The longer you spend on a piece of content, that particular factoid is tracked and increases the likelihood that it’s going to be shown to other people. You want people to stop, which means you’ve got to have stuff that slows them down. Images in your posts tend to—particularly if they’re attention-getting—a good opening hook tends to help. Seeing other people’s engagements tends to help. Then probably I would call it the not-so-secret carousels. Multi-image or multi-things that you have to swipe through left and right or click through, they slow people down considerably. You may want to do some testing with carousels to see if, like on Instagram, if that’s enough to get people to stop and at least passively use the little swiper.
Christopher Penn – 12:39
There are two other things they look for. One, what is the likelihood that the audience is going to engage? Liking, commenting, sharing are the three major mechanisms that an audience can engage with. LinkedIn looks not only at the raw numbers of those, but they look at the number of those that happen within the first hour of a post because it’s looking for how quickly it should be shown to other people. If you can get some early engagement quickly from relevant people in that first hour, your posts tend to do better.
John Wall – 13:16
So would you say that, you shouldn’t be automating stuff or posting something at 11 at night? Because you’ll never get any first hour engagement.
Christopher Penn – 13:25
It depends on who your audience is and where they’re located. Certainly, for me, having a mostly American audience, posting at 3 a.m. is probably not a good strategy. The other one that’s very interesting is that there’s what they have called upstream metrics. They published a paper about this about five years ago. Upstream metrics are the probability that you, as the creator, will also engage. That means that when someone comments on your post, you come back right away and have a conversation and interact with them. Their algorithm is trying to figure out what will get you, as the creator, to come back and what will incentivize you to create more higher quality content for LinkedIn.
Christopher Penn – 14:20
So again, if you want things to do well, you probably should block time on your calendar to spend time on LinkedIn interacting with people who are on your post. Always going back and, at the very least, liking other engagements on there, but having real conversations. LinkedIn wants to see that.
John Wall – 14:40
Yeah, that makes sense because they want the comments section to light up.
Christopher Penn – 14:45
Exactly. They want people spending time swiping and scrolling and seeing all the ads. That’s the second pass ranker. It goes through all these stages. Then there is a third stage which is less about ranking, more about cleaning things up. This looks for content diversity. Is the content being shared sufficiently different from other content that it’s worth showing? Is it a duplicate? What is the level of quality? What is the likelihood that it’s going to take off? This stage is all about whether the content is novel. You don’t want to see 12 job postings in a row. You don’t want to see 12 text-only posts in a row.
Christopher Penn – 15:38
The reranking stage looks at freshness and then looks at the quality of the content. There are two different systems there. One is generally, is the content good? Two, is the content potentially harmful? If you fall afoul of trust and safety, your content’s gone. You will get seen by no one.
John Wall – 16:01
That’s really interesting that there’s—yeah, this is a whole separate thing. First, there’s the scan to see if the content is worthwhile, but now there are a bunch of other gates for the qualitative ranking of that content. That’s interesting.
Christopher Penn – 16:16
Exactly. One of the interesting things is that this is about the content itself, but not about the interactions. The interactions themselves don’t seem to be monitored in the same way. Trust and safety will pick up blatant abuses, but all those completely crappy AI comment bots seem to be escaping LinkedIn’s notice.
John Wall – 16:40
And that’s a whole separate rat hole. I don’t know if you want to touch on that now, or is it just slop city?
Christopher Penn – 16:47
It is. Part of the reason why LinkedIn has a hard time dealing with that is because a lot of those services are browser extensions. It’s not something plugging into an API that you could block easily. It’s AI taking control of someone’s browser. When you look at the placeholders and templates that these tools use, they’re all exceptionally similar: “Great insights on marketing, John,” and the little rocket ship emoji.
John Wall – 17:16
Right? That’s what I was thinking. I’ve seen all of the LinkedIn-suggested ones and wondered why they can’t control those duplicates. I totally forgot that those are extensions and plugins. That’s a whole different level of security headache for LinkedIn.
Christopher Penn – 17:34
Yeah, because they can’t police the browser. There’s no way for them to figure out that you, the human, are not just this idiot. This section here is all about passing the reranking system. Is your content any good? Obviously important, but what’s interesting is whether it’s content that people will want to engage with. Something that’s a really long, thoughtful piece that no one does anything with other than hit the like button will not do as well as saying something stupid and then having the comments section light up.
John Wall – 18:17
Yeah, we keep coming up against this kind of optimizing for stupid, you know, idiocracy strategy, but I guess there’s no way around that.
Christopher Penn – 18:28
Not with the way the systems are balanced right now. That’s one of the big challenges. It is optimized for engagement. What gets people to engage? Anything that provokes a strong emotional response, as opposed to something that makes you think. You’ll do well on the dwell time, but you won’t get the engagement. There’s a very thoughtful piece—”Oh, that’s interesting. I’ve got to go away for an hour and think about that”—that piece of content will not do well because someone went away for an hour to think about it, to come back and think. Whereas if you’re constantly lying and instigating stress, you’re going to light it up.
John Wall – 18:59
Christopher Penn – 19:09
Exactly. Or even just silly stuff like having a poll: “Pineapple on pizza?” People will engage with that.
John Wall – 19:17
Yeah. That’s at least one bright side. It doesn’t need to be complete garbage because compelling polls and provocative questions can have some quality.
Christopher Penn – 19:27
Exactly. That is what happens at the reranking section. You have all these things: remove duplicates, filter quality. Then the last part is the technical piece where it gets marked up, where the system tracks it, and then it gets deployed. The video gets compressed properly. For the average marketer, within the guidebook itself, there is a checklist with what to do, section by section, for your content. One of the things this checklist is designed to do is help you, as the marketer, figure out what you need to do to create good content on LinkedIn. Here’s the hack: you can use this as a prompt in Generative AI.
Christopher Penn – 20:15
You could take this and say, “I want to evaluate my content for LinkedIn. Here’s my content, evaluate it and then tell me how well it aligns with this checklist.” A lot of the stuff that we produce now at Trust Insights, we want to be not only usable for humans, but also usable for machines. For example, if I were to take this checklist here and put it in a text editor with a prompt, say, “Your goal is to accept content from the user for a LinkedIn post and evaluate it against this set of guidelines. Determine how well, per item, the user content answers the key questions. Provide step-by-step, item-by-item analysis, and then a summary.”
Christopher Penn – 21:35
We’ll put in this straight from our LinkedIn guide. Just to make sure it doesn’t get completely lost, we’ll just put this at the end as well. Copy this over. Let’s go into Google’s Gemini. I’m going to use Gemini, but you can use ChatGPT, you can use whatever you want. Create a new thing, call this “LinkedIn Post Evaluator.” Paste that in, hit save, and now we can start chatting with it. We’ve taken this section from the LinkedIn guide and turned it into some content. Let’s see if I’ve got a good slightly trashy post that I can put in here. No, that’s not even written yet. Here we are. I’ll paste this in and I’ll say, “Evaluate this LinkedIn post per the system instructions.”
Christopher Penn – 22:44
“Okay, so I didn’t define the target. Encouraging, thoughtful stuff. Headline often post currently lacks a headline. The introduction is ineffective. The text format is suitable for content. It’s well structured, could benefit from some knowledge graph alignment, lacks hashtags.” This is basically saying you didn’t do such a hot job with this post. But that’s the purpose of this section of the LinkedIn guide—not just for you to read as a human, but to use it with AI.
John Wall – 23:15
Right. And that’s up on the site now, so you can grab that.
Christopher Penn – 23:24
One of the other things that LinkedIn mentioned that I thought was very interesting was their control systems. The ranking systems, particularly the ranking system for the second pass ranking, have model weights—essentially the statistics for how it makes decisions—that are updated daily. The LinkedIn knowledge graph and the second pass ranker, its underlying model, updates daily. Which means that if you find a LinkedIn hack that works today, there’s a good chance it isn’t going to work tomorrow because the system will have recomputed its weights overnight.
John Wall – 24:07
It’s constantly moving targets.
Christopher Penn – 24:11
Exactly. That’s what is going on with LinkedIn as to how the system works, from the moment you hit “post” until it appears. It goes through all these five major stages. For optimizing for LinkedIn, it really comes down to adhering to the general best practices of the system, trying to optimize your content for the way it works. But more than anything, it’s about who you’re connected to and who you are interacting with. One of the strategies that is known to be effective, because of the way the knowledge graph works, is for you to spend 15, 20, 30, 45, 60 minutes a day commenting on other content on LinkedIn with language that is relevant to what you want to share.
Christopher Penn – 25:07
So if you are writing about generative AI, you should be commenting about generative AI on other people’s generative AI posts using comments that invoke those terms and topics so that you are interacting with other people’s knowledge graphs. When you next publish your next generative AI piece, the embeddings will align better, they’ll match up better, and you’ll have a stronger probability of being seen. You will see this very quickly if you were to go out and comment, for example, on a post that Katie made. You will see Katie’s posts more yourself in the next couple of days on LinkedIn until that stops. But she will also start to see more of you as well.
John Wall – 25:59
So the more active you are, the wider you’re casting.
Christopher Penn – 26:03
Exactly. If you are in business development and doing some account-based marketing, and there’s a list of 10 CEOs you’re trying to get the attention of, guess what you better be doing.
John Wall – 26:20
Lovingly examining their LinkedIn feeds every day.
Christopher Penn – 26:25
Exactly. Commenting, engaging, so that ideally they engage back with those comments and then ultimately they see your stuff. So the next time you put up your pitch, they will see that. We have a couple of comments in the chat. Hazeldine is saying now we’ll see “pineapple on pizza” polls for the rest of the year. Well, only 25 days left.
John Wall – 26:52
That’s a plug for “Analytics for Marketers,” too. Those are instant fighting words over there. If you want to come, check out the AFRM Slack community.
Christopher Penn – 26:59
Exactly. Greg is saying it’s important that content be a certain length, like the character count. Yes. Generally speaking, this is something that Hano and Ashley Foss talk about. Big comments—comments that are a couple of paragraphs long, that actually require some thinking—tend to do very well for surfacing a piece of content in someone else’s feed, and for having your stuff be detectable. Which makes sense. If I just say on the LinkedIn post, “Haha, good job, John,” there isn’t much for a language model to embed and match there. If I say, “Hey John, did you think about using generative AI to better align sales copy with someone’s LinkedIn profile?” there are just more words, more embeddings.
Christopher Penn – 27:54
Think about the word cloud of your content and how well it overlaps the word cloud of all the words in that person’s profile. “Haha, good job,” and a rocket ship emoji, there’s not a lot of overlap there.
John Wall – 28:08
Not delivering for you.
Christopher Penn – 28:10
Exactly. So that’s our current understanding of the LinkedIn feed. I always pay attention to two things. I check this blog regularly to make sure that when new stuff comes out, we incorporate it into the guide. The other place you should be looking at on a regular basis is Cornell University’s archive. This is the place to find new academic papers. You will see tons of them. You’re looking for papers published by Microsoft employees specifically about LinkedIn. You can do it there or in Google Scholar. Google Scholar is another great place to look for those papers written about LinkedIn by Microsoft employees because, chances are, they’re talking about things that they’re patenting or presenting.
Christopher Penn – 29:06
A great conference for this sort of thing is NeurIPS (Neural Information Processing). It’s one of the headline conferences for extremely technical AI. All the big tech companies, like Microsoft, which owns LinkedIn, present there because they’re trying to poach talent. When you look at the NeurIPS papers that are shared, there’s a lot of competition, like, “Hey, check out the cool stuff we’re doing at LinkedIn. You should want to work here.” Which benefits us as marketers.
John Wall – 29:44
Give me the inside look.
Christopher Penn – 29:47
Exactly. That’s what’s in the current version. There’s also a section here about the individual technologies being used, so if you’re highly technical, you can enjoy that as well. But that’s what’s new on LinkedIn. So, John, what are you currently doing on LinkedIn? What might you do differently?
John Wall – 30:17
The fact that you could take that entire copy to build a prompt and just run stuff through it before you post and say, “Hey, give me some pointers on how to better meet this scoring criteria” can definitely give you an edge. Otherwise it’s just the normal spray and pray. I also have a question about myths on the street: one, having no outgoing links in the body of the post itself; two, times of day to post.
John Wall – 30:56
People have said, you don’t want to be doing ten posts a day. It should be one quality post a day. Are either of those legit?
Christopher Penn – 31:10
We do know time of day matters because that’s implicit as to your audience. If your audience interacts with everything that you post as often as you want—if you put up a post and 100 people like and comment in the first hour and you can maintain that hour by hour, go for it. Most accounts probably aren’t going to have that kind of pull, and you’re better off with fewer posts where you’re more guaranteed engagement. The same is true of links. There’s no evidence in the technical documentation that says having a URL will ruin your visibility. There’s no evidence to the contrary either.
Christopher Penn – 31:59
People have said that having a link can diminish visibility. I’d guess, based on what we understand about the system, that having a link in the post means someone clicks off of it. What does that do to your dwell time?
John Wall – 32:14
Goes straight to zero.
Christopher Penn – 32:16
Right. People click out and they’re gone. If you put the link in the comments, people don’t click away, they stay with the post longer. It may not be a direct correlation; it may be a dwell time issue.
John Wall – 32:34
That’s a really interesting point. I hadn’t thought of it because I know for a fact there’s no point in posting stuff that links direct to YouTube. It never gets any views, regardless. But yeah, that makes sense now with dwell time being zero.
Christopher Penn – 32:53
The other thing that people have done, to varying degrees of success, is have pods. This was first popularized on Instagram and does work on LinkedIn, where you have a group of 10, 15, or 20 people who all engage on any posts. I’ve been part of these in the past, where someone will post a link, and everyone agrees that each person gets one link per day, and every member of the community goes and engages with everyone in there, which can get overwhelming if you have, like, 20 people. However, it is somewhat effective because engagement metrics do matter. However, the people who are in the pod need to be relevant to you.
Christopher Penn – 33:42
Otherwise, if you are in the pod and you’re marketing B2B marketing services and everyone in your pod is into knitting…
John Wall – 33:51
That’s really interesting. I hadn’t thought about that because pods like this have been around for every social network. But the fact that they analyze who those people are, their backgrounds, and if the content is relevant to them, that creates another level that can basically disqualify all that stuff and make it harder to even reach your initial audience.
Christopher Penn – 34:13
Think about what else happens with the pod. You go into your Slack group, your Discord channel, your group text. You post the link, everyone goes and says, “Yeah,” clicks it, clicks “like,” says, “Good job, John,” puts the rocket ship emoji and then leaves. Where did your dwell time go?
John Wall – 34:30
Two seconds or less on every one of those.
Christopher Penn – 34:34
Exactly. The algorithm having so many features makes it harder to mess with. It’s not impossible, but it’s harder.
John Wall – 34:46
So is it more along the lines of these needing to be real marketing campaigns? Like, “We’re going to do this on this day, go like it, give us some solid feedback, and you should be promoting it to your audience,” not to arbitrary groups of people? That seems like the right way to go—the painful organic path—but it does seem like the way that would get results.
Christopher Penn – 35:08
The painful organic path is probably your best bet for long-term success. There’s any number of gimmicks that can work in the very short term, but they have a very short shelf life. Creating stuff that people actually want, that’s targeted towards your buyers, is the sustainable long-term path because you’re going to make that stuff anyway. Even if it gets two views on LinkedIn, that’s stuff that you can repurpose for your HubSpot instance, for your email newsletter, et cetera. It may do better in those places. It’s not trying to hack the algorithm, it’s just providing stuff that people want.
John Wall – 35:44
Make great stuff.
Christopher Penn – 35:46
Exactly. Which is really hard for a lot of marketers.
John Wall – 35:49
Indeed.
Christopher Penn – 35:52
So with that, anything you’re going to do differently now?
John Wall – 36:00
Run it against the prompt and get feedback. I definitely need to give more thought to posting times because I usually have LinkedIn on my list for crack of dawn or the end of the day. I need to be dropping stuff at 10 or 11 in the morning to get the lunch group and the West Coast. I need to rework the calendar and think about when that actually happens, instead of when it’s convenient for me.
Christopher Penn – 36:33
Exactly. Anywhere from 11 to 1 Eastern time gets you the 11 a.m. or noon lunch crowd on the East, the 9 a.m. crowd on the West Coast, end of day in the UK, the early morning crowd in Asia. Wherever your audience is based. One easy way to figure that out, if you’ve been sharing content on LinkedIn that links back to your website, is to go into Google Analytics and look at your time of day traffic from LinkedIn.
Christopher Penn – 37:15
Day of week, time of day, make it into a heat map and see when your stuff is getting traffic. Then maybe you do a test: post every six hours for a week and see when you get traffic.
John Wall – 37:36
That would be great. Then the next week, you go back and look at the heat map and see what your schedule should be.
Christopher Penn – 37:42
Exactly. The other way is to look more broadly at what time of day the audience is typically active. Software like Agora Pulse will do that, with audience activity map built right into the platform.
John Wall – 38:00
That’s a complete advantage for getting action, as opposed to zero likes.
Christopher Penn – 38:09
Exactly. John says most marketers want quick hit rewards. The thinking is usually short-term. By the time things are important, those marketers have moved on. It reminds me of the old adage that if you’re seeing it at a conference session, it…
John Wall – 38:24
No longer works. There are always lottery winners. You’ve got to be the first or second person. But luck is not evenly distributed. If you’re early, you get all the luck, and everybody else trying to catch up gets the bum tips.
Christopher Penn – 38:42
Exactly. That’s one of the basics of any social network: whenever a new feature is announced—”Oh, we have voice chat now”—you need to use it aggressively because they typically overweight new features for a while. Meta is well known for that. We have a question from LinkedIn: “Anything we can say about lead generation and targeting in relation to the LinkedIn algorithm?” Lead generation itself—I’ve certainly noticed in our data, and I’ve heard from folks in “Analytics for Marketers,” that company pages and profiles get nothing.
Christopher Penn – 39:25
We’ve been posting the same update across our personal accounts and the company account, and without fail, there’s no engagement on the company account. This is across different sized personal accounts. I have a fairly large account, and we have some folks who have less large accounts, and even the smaller personal accounts do well with engagement. The company stuff, crickets. You post because you checked a box in your software, but nobody is home on company stuff.
John Wall – 40:04
That’s a really interesting point. Is it just the classic Facebook thing where they want you to turn on the ads and basically nobody gets any traffic if you’re a company page?
Christopher Penn – 40:18
That’s exactly it. Pay up.
John Wall – 40:23
Shakedown.
Christopher Penn – 40:25
Exactly. It’s always the same shakedown. The workaround is getting your individual marketers to share stuff and create non-promotional content the rest of the time.
John Wall – 40:40
Stuff that isn’t spam. If they just start parroting stuff and spamming, they get put into a different batch.
Christopher Penn – 40:53
Exactly. Back in the old days, the recommendation was a 5-to-1 ratio. For every one promotional piece of content you put up, you need five pieces that are non-promotional and still valuable. It’s sort of like an inverse tentpole strategy. Most of your stuff has to be non-promotional, and once in a while, you throw out a “Hey, pay up.”
John Wall – 41:19
You earn the right to throw a pitch in.
Christopher Penn – 41:23
Exactly. Content strategy. Any final parting thoughts, John?
John Wall – 41:29
Just get down to work on LinkedIn. Is it still your best odds and best option if you’re in business?
Christopher Penn – 41:41
It depends on your business and audience. If you have a specific audience demographic, they may be on TikTok. It’s generally accepted that if you’re in B2B, LinkedIn is probably the best place to be. But it depends on the industry. If you are in B2B tech, LinkedIn is the place to be. If you are in, for example, industrial ball bearing B2B, your audience isn’t on social media because they’re at the golf course. The number one thing you can do is a poll or survey in your email newsletter. “Where do you go online, if anywhere, when you want to talk about work?”
Christopher Penn – 42:28
Not golf or politics, but work. Then you have your list: LinkedIn, Facebook, Twitter. Run that survey quarterly.
John Wall – 42:46
That sounds like good advice.
Christopher Penn – 42:49
That’s it for this week. Next week we should be back to normal. Then one more live stream and we’re off until the new year. Two more shows this year. Thanks for tuning in. Be sure to subscribe to our show. For more resources, check out the Trust Insights podcast at TrustInsights.ai, the TI podcast, and our weekly email newsletter. Got questions? Join our free Analytics for Marketers Slack group at trustinsights.ai/analyticsformarketers. See you next time.
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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.
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