What is Filtered?

By Marc Zao-Sanders

4 minute read

We write a lot. We usually publish an article a week on topics we know a little about, which we find interesting and which we think you enjoy too. I’ve published here and on LinkedIn about:

LXPs; understanding the actual workforce; the meaning, history and future of personalization; the utter indispensability of learning; AI for managers; how to visualise algorithms; the underlying kindness of recommendations; fake AI. We’ve also written on HBR about prioritizing skills, resisting algorithms and intelligent assistants.

Despite - or maybe partly because of - this, we still get asked, ‘but what is Filtered?’. It happened last week: “well I've heard about Filtered from your emails, but not quite sure what it is, maybe you can explain”.

It used to be hard to explain, because what we were doing (and continue to do) is new and new stuff doesn’t, by definition, belong to an existing market category which people know and understand (and love, hate or are indifferent to). Carving out our own category was both rewarding and difficult. 

So, we decided to have our cake and eat it. We have taken our novel approach, product, and purpose, and applied it to an existing category; we've built a Learning Experience Platform around our central innovation which we call "content intelligence". 

What is Content Intelligence? 

Content intelligence is our solution to what we see as the central challenge in corporate learning: content overload

Content overload refers to the detritus of learning materials that build up on every learning system. Many businesses aren't aware of how bad it's got. LinkedIn Learning has about 20,000 assets, Skillsoft 40,000, MindTools 3000, GetAbstract 8,000, and there are over a straight month’s worth of TedTalks available. Businesses constantly accumulate these libraries which keep on accumulating content. And the content mainly sits in unfiltered piles in the recesses of learning systems.  

The problem is that learners only have 24 minutes a week to spend on learning. When they try to find a bit of learning that's useful to them they're faced with more material than they could get through in a lifetime. Psychology suggests that they're likely to get frustrated at best and apathetic at worst - our brains are built to run from overload

L&D don't have much more luck making their way through this overload. Learning content is notoriously poorly tagged. Even if the classification is broadly correct, it lacks nuance. And 1000 articles about "leadership" are basically as difficult to navigate as 10,000 about learning as a whole - and the various definitions of leadership you'll get are as likely to cancel each other out as they are to teach something new. To accurately judge what a piece of content actually achieves, you have to read it. 

Here's where Content Intelligence comes in. It reads the content for you (in its own way) at 1000x the speed. All you have to do is plug in the skills you want an the algorithms pick out and rank the content according to how well if fits the skills you need (it can also work out the financial value of each of your content libraries but that's for another time). 

A question some learning leaders might ask: how do you get the skills right? It's an important one given that Content Intelligence's success relies on the accuracy of the skills. Filtered make sure that the skills are right using two agents: human expertise and data. 

Learning departments are famously understaffed. So, our LXP works as a semi-consultative model. Our experts have built countless skills frameworks for businesses and are well equipped to consult on the exact skills you need - right down to the nuance. 

The second prong is the data. We've developed a method of pulling together and standardising a range of internal and external data to aggregate the skills a business needs now, and will need in the future. 

Most LXPs start after this point - without sorting out skills or cutting the content. That's why they often become landfills for surplus content

The Smart LXP

For Filtered, Content Intelligence is the first step. After that necessary foundation, things are far better set to run smoothly. But we don't take any chances. We throw every weapon the LXP has in its arsenal to ensure that learning has an impact. Just in a smarter way. 

AI powered recommendations 

Once content has been curated for an organisation, it needs to be curated for individuals. Filtered does this through a skills signature. A learner’s skills signature is our best-estimate of which skills they can most valuably develop.

Mathematically, it’s just a set of numbers: {Personal Development, 0.8; Resilience, 0.9; Mindfulness, 0.84; …} for skills in a framework. It's live, which means that an initial self-assessment is supplemented as the algorithms spot the types of learning each user gravitates towards.

Each individual's signature is matched to content which has been tagged by Content Intelligence. Our neural network accommodates users' own activity into the skills signature as time goes on to improve recommendations. 

Smart playlists  

Learning playlists (or pathways) happen when learning content is strung together so that the learner improves incrementally. Each piece of content acts as a stepping stone to the next. 

Filtered LXP enhances these with technology.  While users can build playlists, our algorithms pick out pieces of content to supplement pathways. These can even be personalised to the individual. 


One of the main benefits of Learning Experience Platforms over older systems is their capacity to integrate. Filtered uses an open API which means that it can integrate with any learning system or platform. 

It can do more. We run put Filtered in inboxes and have the most comprehensive Microsoft Teams integration available. Both through email and MS Teams, we are able to run personalised engagement campaigns to keep learning going consistently. 

Data insights 

Learning departments are waking up to the value that data can give them. But, there's a big gap between data aspirations and getting practical insights. The Filtered team help clients understand what engagement and usage data mean for their learning provisions. And what they should do with the insights they get. 

How does it actually help?
Filtered helps learning professionals and end learners in a number of ways. Different clients get different benefits from it. Here are a few:

  • Learning happens more. There’s more of a reason to engage in elective learning like soft skills when a job-relevant recommendation has been made. Filtered has achieved 50% take-up amongst its audiences - compared to an industry average of 5% for elective learning.
  • Learning happens better. It’s not just more learning but better, more relevant learning as more of the right content is put to the people that need it. And it’s more engaging delivered in a conversation. Everyone likes that. Users launch learning from the top tray of recommendations 17% of the time. When they browse the rest it’s just 2%. In other words, recommended learning is 10x stickier.
  • Learning professionals and the workforce generally gain firsthand experience of AI. They can use this to further their careers and have a tangibly informed opinion beyond the headlines and hype.
  • Learning professionals get data insights to make better decisions. What content is working, what content can I drop, what type of content engages my people, what are my high-performers doing, where are people learning, when in the week and in the day are people learning and can I fit my learning strategy around that. Filtered enables a ‘data-driven learning design strategy’ 

That’s what Filtered is.

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