Martin Maisey

6 minute read

Lucy Kellaway’s recent Klout article and various discussions I’ve seen recently around endorsements on LinkedIn have got me thinking. The general consensus at the moment from sensible people seems to be that, at least in their current state, they’re pretty useless.

For Klout, the reason is clear: the whole thing is based around the idea that your social media activity somehow correlates with your real world influence. That’s blatantly not the case: many of the most genuinely influential people I know either don’t have social media accounts, or don’t touch them.

To illustrate the reverse side of the problem, I recently posted Facebook photos of my newborn baby. This, combined with a large fraction of my friends (well, the female ones, at least) saying ‘ah bless’ and hitting Like, appears to have netted me a Klout of 47 - above average - from my Facebook network alone. My LinkedIn network isn’t hooked up. 

While lovely from a personal perspective, the idea that people might use this as a hiring filter, which apparently seems to be the case, is frankly terrifying. It certainly does not equate to any notion of real world ‘clout’ that I recognise.

Additionally, the whole your-score-goes-down-if-you-haven’t-posted-in-a-few-days thing is self-evidently vapid.  Very few people posting with high frequency have anything meaningful to say on a regular basis. Real influence is built over decades and persists over many years, so any score this volatile must be wrong. Even in extreme cases like Rebekah Brook’s arrest, while her influence may have dropped fast from its height, I’d bet she could still wield a lot of it via a phone call to the right person.

The Klout score is an incredibly blunt metric for most useful purposes - it is, quite literally, one dimensional. By focusing on this number, Klout are either pushing a distasteful business model based on feeding people’s fears and self absorption, or don’t know what they’re talking about. I’ll be opting out.

The problem with LinkedIn endorsements (and the equivalent Klout +K scheme), is slightly more subtle. Making sure that the context for an endorsement is captured avoids the Klout score problem of condensing everything into one meaningless number.

However, the number of endorsements you have for a particular skill will still be a very poor indication of how good you are at it. This is because an endorsement as currently displayed in LinkedIn doesn’t provide any indication of the expertise of the endorser. Therefore gaming this metric becomes trivial - I just need to find people to ‘swap’ skills with.

But I suspect that - for LinkedIn, at least - this is only the first data collection stage in a sophisticated plan. My experience from 4 years at NetReveal, before moving to my current job, is that combining analysis of networks with good quality real world data is powerful. You can come up with very stable and reliable metrics, even in the face of deliberate attempts to frustrate this by cunning people who make a living working out how to game systems - e.g. fraudsters etc.

Building an expertise score for each person/skill combination is much more involved than just counting the endorsements. It is more analogous to Google’s original PageRank algorithm, and in particular its cousin TrustRank. Implementing this in large networks is technically hard, but the basic idea is that you ‘flow out’ reputation scores for each skill through the network of endorsements, starting at known experts. Endorsements for a particular skill from someone would be weighted by their own score. As a result, an endorsement wouldn’t count for much or anything unless the person providing it had been directly or indirectly endorsed by someone who is definitely an expert.

As they’re clearly collecting lots of endorsements very quickly, LinkedIn’s main challenge will be to identify good experts. It could probably be done manually for common skills in highly LinkedIn connected industries such as IT, although this wouldn’t scale for less connected industries and less common skills.

However, LinkedIn will have lots of ways of doing this fairly well on an automated basis given the incredibly rich datasets they have access to. In the IT space at least, they are rapidly approaching saturation. This means they know about many of the approaches recruiters make to potential candidates. They also know about virtually all job moves and whether they resulted in a person staying with a company for a while and forming a stable professional network. This is valuable data to mine.

Klout will have collected only a small fraction of real world connections. Their sparse coverage means the reputation network will be fractured into lots of disconnected ‘islands’, each of which will need at least one good expert per relevant skill. But they have few good real world signals from outside the echo chamber of the social networks to work with, so finding these will be nigh on impossible and their scores are very unlikely to be reliable.

Of course, there’s a lot more to it than just finding good experts. For starters, for it to cope with synonymous and related skills, you’re going to have to build a high quality ontology. If LinkedIn haven’t developed or bought one of these yet, I suspect this is something that they can obtain through statistical analysis of their datasets.

In summary - LinkedIn is, if they play their cards right, about to establish a high quality set of skill scores ranking a significant proportion of the world’s population. They may well not make this directly visible to users, but use it for - for example - refining recruiter search results. Once established as a key recruitment tool, being a participant in this system will not be optional, and it becomes a self reinforcing monopoly - similar to the one Google have managed to manufacture for themselves in advertising, but I’d argue more valuable.

One final thought. A really interesting (meta-)score you can generate for each person is how good they are at endorsing people who are already highly scored by others. You can then use this to further weight the reputation flow in the network and filter out people who are genuinely skilled, but endorse unreliably. I’d argue this is a very interesting metric in its own right. Particularly for senior people, the ability to accurately assess another’s skill level is possibly the most important skill of all.

I’d recommend people are highly selective about who they endorse and for what.

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