One product that I’ve used every day over the past few years is LinkedIn. I used to prefer using LinkedIn over other social media platforms like Instagram due to the rich and high-quality professional content and limited spam. I used to post a lot of content as well, especially during the pandemic but I stopped posting as content overload increased over time.
I used LinkedIn as a way to improve my writing as well as to learn from other professionals on a completely new perspective on topics that I write on. The content overload has negatively impacted my user experience.
Few examples of the recent content and feature clutter problem I’ve observed on LinkedIn:
- New AI tools and features have been coming out every day which is exciting to me as a tech enthusiast but the number of people who post the same type of content repeatedly on LinkedIn defeats the purpose for someone like me who wants to gain valuable insights and relevant updates.
- Contribute Expertise is a really great feature on LinkedIn in my opinion to learn things and experiment in my day-to-day work but the number of plagiarized answers has put me in a position to not use the feature at all. The positive intent of the feature has been overshadowed by the content practices that stray from LinkedIn’s mission.
- I am still trying to understand the need for games/puzzles on LinkedIn. It reminds me of good old Farmville days on Facebook although I don’t see the relevance on LinkedIn.
Let’s take a step back and look at the mission and vision of LinkedIn before diving deep into the pain points and potential solutions.
The mission of LinkedIn is simple: connect the world’s professionals to make them more productive and successful.
LinkedIn’s vision is to create economic opportunity for every member of the global workforce.
Now that I have provided the context and background, the following seem to be the top pain points I’ve noticed on LinkedIn today. Please note that these are based on personal experiences and the reality might be far-fetched due to the lack of quantitative and qualitative research here.
- Data Clutter – Content Overload, Low Quality, and Irrelevant Content: The rapid pace of content generation on LinkedIn has led to the repetition of low-quality or irrelevant content. Users are bombarded with ads, notifications, posts, and updates from connections, and relevant industry insights get lost in the noise.
- Algorithmic Challenges: LinkedIn prioritizes engagement-driven posts that favor sensational or “viral” posts over genuinely insightful ones that might not align with the user’s professional goals.
- Feature Overload: LinkedIn’s variety of features—such as Games, Contribute Expertise, LinkedIn News, Groups, Articles, Events, and Media—adds to the platform’s clutter. This is a genuine problem not just on LinkedIn but for any company. Even if I start a competing product that has a simple user experience today, it might evolve into another LinkedIn in probably another 10 years due to limited growth potential after a saturation point.
- Limited Customization: The LinkedIn feed currently has limited flexibility in filtering the relevant content that interests the user. There is no tailor-made content that meets relevance to the users’ needs.
- User Experience Impact: All the above pain points in summary reduce LinkedIn’s effectiveness as both a professional networking and knowledge-sharing tool.
Addressing these problems effectively will lead to better user experience and help LinkedIn to be more in alignment with its mission and vision. With the recent advancements in AI capabilities, LinkedIn can leverage some of the following tools for a more personalized and clutter-free user experience.
- Generative AI for Content Summarization
- AI for Content Quality Scoring
- Enhanced AI Search and Recommendations
- Conversational AI for In-Depth Engagement
My goal here is not to overuse the term ‘AI’ but to suggest practical AI applications to address these issues. Combining the above tools can help LinkedIn generate a cohesive user experience aimed at reducing content clutter and more user-centric experience.
LinkedIn already has implemented some AI tools for its features like AI-Powered Feed Summarization and Generative AI for Content Summarization. Please check the linkfor more details.
There are some other potential ideas that I could think of to reduce the clutter problem on both content and features.
Customizable Notification Controls: The current notification center is too extensive and hard to use. Analyzing user behavior to suggest relevant notification preferences can help improve the user experience.
Personalized Feed Filters: Advanced filter options allow users to adjust their feeds based on relevant topics, industries, and connection levels, all powered by AI for a more dynamic feed. Tags do help alleviate this issue to a certain extent but it’s not foolproof.
LinkedIn Games: Since LinkedIn hasn’t really been a platform that I really want to play games on, I think it can tune the current games feature in order to align with its mission. They can gamify so that users can learn or update their skills in various topics. Taking the Duolingo approach for learning product management skills, software development skills, marketing, etc. would be really nice to explore.
Similar to the LinkedIn top Voice badge, gamification can add more badges would really encourage users to create more genuine content or learn new skills that could benefit a lot of users. I personally have a lot of ideas that I would love to see on the topic. One of my side projects has been on similar lines which I could speak for pages in a different blog post.
AI for Content Quality Scoring is another feature that I highly value. Similar to how ChatGPT content can be upvoted or downvoted, LinkedIn could add this feature for users other than the author of the post to upvote or downvote and this data doesn’t need to be shared with the author. But LinkedIn could use it to recommend or bring this content up the order.
This doesn’t mean this has to be the only way to determine the content quality but can be used as an additional parameter that can be considered for their algorithmic challenges.
In summary, there are still a lot more ideas that I could think of like optimizing or changing the Contribute Expertise feature and Article Feature. Job-related features are a different beast altogether to tackle, but overall, I feel LinkedIn has done a pretty good job so far, and I hope they continue improving the overload problem on both the content and feature set by leveraging AI.
References:
LinkedIn AI Features
Prompt for DALL.E Image Generation – Generate an image for LinkedIn decluttering
