BEHIND THE SCENES: The REAL LinkedIn Algorithm Secrets

Marketing School - Digital Marketing and Online Marketing Tips

Eric Siu and Neil Patel

Marketing, Careers, Business

4.6 • 1.4K Ratings

🗓️ 19 July 2023

⏱️ 7 minutes

🔗️ Recording | iTunes | RSS

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Summary

In Episode #2513, we shed light on some shocking information about the LinkedIn algorithm and its manual filtering from someone who works in its influencer program. You’ll hear about how they manually manipulate the reach of viral content, the types of accounts that get down-ranked and up-ranked, and why. We explain some of the criteria that affect this, the four levels they use to rank content, and the topics you should talk about if you want your posts to rank. We also discuss the lack of transparency about the algorithm and how LinkedIn differs from Twitter in this regard. Tune in to find out the truth about the LinkedIn algorithm! TIME-STAMPED SHOW NOTES: [00:00] Today’s topic: the secrets of the LinkedIn algorithm. [00:30] Shocking info on the manual filtering of LinkedIn from someone on the inside. [01:37] Typical account types that get down-ranked and up-ranked. [02:10] How the criteria changes per country and other factors that affect it. [02:31] Topics you should talk about depending on your racial profile. [03:17] The four levels that they use to rank content. [03:50] Thoughts on the incentives that drive this. [04:11] How our hosts would do things differently if they worked at LinkedIn. [04:47] A case study that Eric looked at and the value of playing by the rules. [05:16] Thoughts on the level of transparency when it comes to algorithms in LinkedIn versus Twitter. [06:33] That’s it for today! Don’t forget to rate, review, and subscribe! Go to https://www.marketingschool.io to learn more! Links Mentioned in Today’s Episode: LinkedIn Twitter Don’t forget to help us grow by subscribing and liking on YouTube! Leave Some Feedback: What should we talk about next? Please let us know in the comments below Did you enjoy this episode? If so, please leave a short review. Connect with Us: Single Grain << Eric’s ad agency NP Digital << Neil’s ad agency Twitter @neilpatel Twitter @ericosiu Learn more about your ad choices. Visit megaphone.fm/adchoices See omnystudio.com/listener for privacy information.

Transcript

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0:00.0	All right, so we're going to talk about the secrets behind the LinkedIn algorithm, but this one, this episode is a little different because this is from behind the scenes.
0:06.8	So when we're talking behind the scenes about doing this episode, Neil's like, we did this already, but I was like, Neil, my friend this morning told me there's actually a lot more that happens behind the scenes and this is my friend talking to a person that actually works at LinkedIn.
0:18.6	So Neil, do you want me to let her rip here?
0:21.0	Yeah, because I didn't talk to your friend who has a friend at LinkedIn, so.
0:24.4	Okay, I was like, hey, I was like, hey, do you want me to give you credit?
0:26.6	He's like, no, no, no, I'm going to get banned if you give him crap.
0:28.4	So I'm not going to give him credit.
0:29.8	So here's what he says.
0:30.6	So seeing how they're doing things is a trip right now. I talked to a person who runs their influencer program and she said it is totally rigged and I mean hand selected air quote. What they do is you get traction on stuff your account hits a list that they manually review
0:46.6	so let's for example let's say I had a picture and it goes viral that's 4,000 likes on it right?
0:51.3	They're like okay that triggers a manual review.
0:53.6	I didn't know that.
0:54.6	They have a criteria.
0:55.9	Race, gender, topic, age, school, etc.
0:59.0	And if you don't fit their current initiative,
1:01.0	they tag you and they downrank you and once they do it's almost
1:04.3	impossible to get out of their system.
1:06.0	You want to pause for a second.
1:07.2	I have more here.
1:08.2	Keep going, keep going.
1:09.2	Just right.
1:10.2	So she said, if your followers stopped growing significant in the last year it's because there are too many people going viral that don't fit their criteria and they basically downrank them all.
	...

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