logo
episode-header-image
Sep 2024
1h 5m

Upping Your Odds of BEATING the LinkedIn...

One Knight in Product
About this episode

Ivana Todorovic is the co-founder of AuthoredUp, the "Ultimate LinkedIn Content Creation & Analytics Tool", and wants to help YOU get better at standing out from the crowd and beating the LinkedIn algorithm. We spoke about all things LinkedIn, including the dangers of "engagement pods", whether it matters where you put your links in the post, how to engage with larger accounts, the power of secondary comments, and much, much more. We also spoke about her startup journey, the pros and cons of being reliant on a larger platform, and why she's so happy she bootstrapped rather than seeking VC funding. Check the episode out now!

A message from this episode's sponsor - Leadfeeder

This episode is sponsored by Leadfeeder. No more not knowing who’s coming to your website, convert more leads and get a free trial at Leadfeeder.com: Check out Leadfeeder here.

Episode highlights:

 

1. There's no "Quick Fix" for your LinkedIn profile

Beware snake oil salespeople who claim to be making millions off of their LinkedIn content and are trying to sell you frameworks to be just like them. There's no cookie-cutter approach, the algorithm is changing all the time, and the majority of these people are basically lying about the results you will get and laughing their way to the bank.

2. It's Important to Soft Sell on LinkedIn

Direct sales pitches underperform compared to content that offers value with a subtle call to action. Posts with a soft sell, focusing on the audience’s needs and delivering value without the CTA, perform better. You can't just keep selling things or trying to get people to click links... LinkedIn hates you leaving the platform and they will de-boost your posts.

3. The Pros and Cons of "Link in Comments"

Posts with external links often get down-boosted because LinkedIn wants to keep users on the platform. Adding links in the comments or at the very end of the post is a better strategy, though even this approach reduces post impressions.

4. LinkedIn doesn't want your posts to go viral

However it might look, LinkedIn explicitly prioritises real conversations and interactions rather than people mindlessly sharing clickbait. Concentrate on having real conversations, replying to comments, and replying to the comments on comments. This will boost your own impressions.

5. LinkedIn Blue and Gold Badges are Statistically Meaningless

There's no statistically significant impact on having either of these badges. The badges are just there to make you feel special and keep you coming back to LinkedIn so that they can keep advertising to you. People with blue badges don't obviously have better content than those without, and people with gold badges are just being rewarded for feeding the AI-training hamster wheel.

6. Beware Engagement Pods

Engagement Pods are private groups of people who share their posts with each other so they can game engagement and try to defeat the dreaded algorithm. However, these are super-easy to detect and they show up quickly even to external analysis. There are better ways to win at LinkedIn than paying exorbitant fees to snake oil salespeople.

Contact Ivana

You can catch up with Ivana on LinkedIn or check out AuthoredUp.

Related episodes you should like:
Up next
Nov 10
CPO Stories: Sean O'Neill - Syncron
On this episode, I speak to Sean O'Neill. Sean is the Chief Product & Technology Officer at Syncron, and an executive product leader with a storied career spanning companies like Amazon, Tesco, and GfK. We bond over our shared history at GfK, speak about how Amazon has influenced ... Show More
50m 35s
Oct 22
CPO Stories: Georgie Smallwood - Moonpig
On this episode, I speak to Georgie Smallwood. Georgie is Chief Product, Technology & Data Officer at Moonpig - the UK's best-known online gifting and greeting card platform. Georgie has built a global career leading product and technology teams at companies like N26, Xero, and T ... Show More
53m 6s
Sep 23
Nesrine Changuel - We Should All Prioritise Product Delight! (with Nesrine Changuel, Product Coach & Author of “Product Delight“)
On this episode, I speak to my friend Nesrine Changuel, product coach and author of the new book "Product Delight". Nesrine started her career at Bell Labs as a research engineer before moving into product management at Microsoft, Spotify, and Google, where she even held the titl ... Show More
1h 6m
Recommended Episodes
Aug 2024
ChatGPT has a language problem — but science can fix it
AIs built on Large Language Models have wowed by producing particularly fluent text. However, their ability to do this is limited in many languages. As the data and resources used to train a model in a specific language drops, so does the performance of the model, meaning that fo ... Show More
36m 50s
Jul 2025
Are World Models the Key to AGI?
A groundbreaking Harvard study trained AI on 10 million solar systems and found it perfectly predicted orbits but completely failed to understand gravity, raising questions about whether LLMs can develop true world models. While companies pour billions into scaling, Meta's Yann L ... Show More
21m 28s
May 2023
TinyML: Bringing machine learning to the edge
When we think about machine learning today we often think in terms of immense scale — large language models that require huge amounts of computational power, for example. But one of the most interesting innovations in machine learning right now is actually happening on a really s ... Show More
45m 45s
Aug 2023
Cuttlefish Model Tuning
<p>Hongyi Wang, a Senior Researcher at the Machine Learning Department at Carnegie Mellon University, joins us. His research is in the intersection of systems and machine learning. He discussed his research paper, Cuttlefish: Low-Rank Model Training without All the Tuning, on tod ... Show More
27m 8s
Feb 2017
MLG 002 Difference Between Artificial Intelligence, Machine Learning, Data Science
<div> <div> <p>Artificial intelligence is the automation of tasks that require human intelligence, encompassing fields like natural language processing, perception, planning, and robotics, with machine learning emerging as the primary method to recognize patterns in data and make ... Show More
1h 5m
Jun 2025
Alexandr Wang: Building Scale AI, Transforming Work with Agents & Competing With China
Alexandr Wang started Scale AI to help machine learning teams label data faster.It started as a simple API for human labor, but behind the scenes, he was tackling a much bigger problem: how to turn messy, real-world data into something AI could learn from. Today, that early idea ... Show More
1h 1m
May 2025
MLG 035 Large Language Models 2
At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup ... Show More
45m 25s
Sep 2024
Large Language Model (LLM) Risks and Mitigation Strategies
<p>As machine learning algorithms continue to evolve, Large Language Models (LLMs) like GPT-4 are gaining popularity. While these models hold great promise in revolutionizing various functions and industries—ranging from content generation and customer service to research and dev ... Show More
28m 58s
Feb 2017
MLG 001 Introduction
<p>Show notes: <a href= "https://ocdevel.com/mlg/1?utm_source=podcast&utm_medium=mlg&utm_campaign=mlg1" target="_blank" rel="noopener">ocdevel.com/mlg/1</a>. MLG teaches the fundamentals of machine learning and artificial intelligence. It covers intuition, models, math, languages ... Show More
8m 11s