Matt Biddulph (one of the Dopplr founders) is to blame. At least I think he’s the one that started the “Silicon Roundabout” name off.  What did Larry Page say when someone told him Google were buying space at London’s Silicon Roundabout? Probably, “what’s a roundabout?”. Americans are so cut-and-thrust they don’t have roundabouts, roundabouts imply too much collaboration between drivers.  At least Brighton’s Silicon Beach makes sense, in that sand is made of silicon (only, there’s no sand on Brighton beach.) Anyway, the organisers of Silicon Milkroundabout can’t be blamed for perpetuating a silly name, or making it sillier by punning it with the idea of the university milk round.

In case you haven’t come across it Silicon Milkroundabout is a job fair – startups (and mature companies) have stalls. On Saturday product managers went round the stalls and tried to find jobs, on Sunday developers did the same, and in much greater numbers. Having tried to hire developers, I can say that anything that makes finding them easier is a good thing.

I’ve never quite known what my job title ought to be, but it seems like I’m mostly in the product manager camp. So it was nice to meet a bunch of people who do the same thing and chat about our shared experiences. But I’m also a bit of a dev, so I went on Sunday too.

Aside from trying to find some work, it was an opportunity to see what kind of companies are growing. Distilling customer tastes from big data was definitely the standout theme. There were companies that mined data on previous purchases to discover what products you might like, others that looked at your results in personality test, and others that looked across you social graph. The objective was either to serve better targeted adverts, or to customise a website to highlight the products that a particular user is most likely to buy.

I’m slightly inclined to question a fundamental assumption in all this: that I have fixed propensity to purchase any given product, and that propensity can be discovered by looking at my behaviour, or my friend’s behaviour.

I have quite a vivid memory of going to a party where guy with a massive beard and a comforting northern accent was playing music. Everything he put on was different, unknown to me and really good. Everything he put on I asked what it was, and he told me some interesting things about the song and its context. He worked in a record shop, and if I was a customer I think I might have bought about 50% of the stuff he was playing.

Instead I got home and YouTubed most of it. It turned out that, listening off my laptop on my own, I liked much less of it – Yacht was the only band that really stuck with me. Even then, by playing perhaps 6 records he found one that was of genuine interest. He had a good conversion rate.  Obviously this is anecdotal, but there are two things that might be interesting:

  1. I felt “active” in the discovery process. I was at the right party speaking to the right guy to find these things out. I had exclusivity, if someone asked how I found out about Yacht I had a story to tell. Not a great story, but there was a real connection behind it.  I would have dismissed exactly the same results if they’d appeared to me as automatically generated recommendations in a  UI. In fact, I would probably have said they were stupid, because there was no personal investment in the selection process.
  2. No amount of looking through my previous purchases would have shown artists similar to Yacht. No amount of looking through my social graph would have shown that my friends liked Yacht. That was what made them a great discovery, I could say to my friends – “Hey, I found a cool thing”, and be reasonably sure I had new information.

Often I want website suggestion algorithms to fail, confirming what I like to think of as my unique and distinctive tastes.

Liking Yacht isn’t a deep-seated feature of my brain that could be discovered if you had enough data about me. It was something that happened when I guy that thought was cool, but not too cool, told me about them. Meeting him in that context made it better.

I hate Amazon Books suggestions. Even if they could perfectly predict what I would have bought, as soon as I see the suggestions I change my mind. My reading is a deep part of my individuality; if it can be predicated by an Amazon algorithm then I feel obliged to switch it up a bit. Conversely, I’d be more than happy to have a film recommended to me: film taste isn’t something that’s particularly important to me.

I love the Hype Machine, a site that finds music from blogs. It understands that my musical taste is not going to be formed by a suggestion engine, but by what other identifiable humans have said. Each track is presented with a snippet from the blog it appeared in. I have to search for it – I’m an active participant. I discover the music, rather than it being suggested to me.

Obviously, suggestions systems do work, enough for Netflix to invest in a million dollar prize for anyone who could improve their algorithm by 10%.  Small increases in conversion rate are worth a lot of money – I just wonder if they would work better if they took a deeper account of the social factor than crawling my Facebook friends. Or if you can generate the “meeting a guy at a party” moment on a website.

Turns out I’m not that into Yacht that much anymore. I don’t want to be identified with the kind of people who “like” them on YouTube.



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