image_pdfimage_print

Explaining the power of the Facebook social graph using containers and social networks

image_pdfimage_print

I had a great time at Lean Startup Machine London this weekend, learning about using lean startup ideas and practice from a social networking perspective to build a business. It helped that I’d already been to hear Eric Ries talk, thanks to a tip off from Andy at Crocodile Clips (currently looking for investment himself I believe, and I picked up a good contact for him at the event). And also because I’ve been helping Barnaby with his Name That Place concept, thinking about how to get proof of concept and wondering about what the best way to take that forward (btw he’s not in the office today at Regus, but moving lodgings to a house boat near Vauxhall:-)

So while I promised myself a lazy day today I wanted to quickly note down two things. I still have to prepare for a talk at Cass next week on using MVP to help corporates build successful online communities, and I still have ot find a job/drive revenue before my severance from eBay runs out in X number of weeks. So time is short and comes with a cost attached, and before I pop into town to watch Mr Spacey in ‘Margin Call’ here’s a couple of quick creative thoughts.

Containers – in a container (paper page) – in a container (photo) – in a container (blog post) – etc

Mapping containers to networksPhoto by Stuart Glendinning Hall

I like to try and simplify things where possible as that way you can get difficult things done more easily right? So in thinking about what works as a social business I came up with the idea of matching up ‘containers’ – that is simply a tool for mapping how a social concept might work. The example above is an attempt to show across 3 degrees of separation how in rough and ready terms a business like Airbnb  works best.

In trying to find somewhere to stay you are first going to see if any of your ‘friends’ live in the city you are visiting (the idea behind Airbnb is providing cheap places for people to stay in other people’s homes). But the chances they have a room in that city are ‘unlikely’ as your friendship network is relatively small. So you turn to ‘friends of friends,’ and they are ‘likely’ as they are my the virtue of wider geo-distribution going to have a possible place to stay. But maybe the night you want to stay they are busy? So the next container along, which for the sake of 3rd degree of separation symmetry I’ve called ‘friends of friends of friends’ is very likely to provide the room you want, and for the time/date you want. (It’s a nice fact that the average user on Facebook is connected to everyone else by 3.74 degrees of separation, so you can see why Facebook based commerce using the social graph is so potentially powerful).

As a side note I really liked the pivot by lean startup participants ‘You never know’ led by ‘Easy Ed’ (alliteration really helps remember ppl’s first names:-) who started with the idea of an app where you could get matched up with single people in your immediate social network, but found that people didn’t want to do that for themselves. But then on pivoting realised that ‘smug married’ people would happily introduce single people to other single people. Neat change of the social networking dynamic, from ‘doing it for yourself’ as a single person not working due to fear of rejection for example to someone with a networking ‘doing it for you’. So maybe that’s why blind dates work, so long as someone you know sets it up for you!

Superbowl Sunday: data crunchers vs grandmothers

While I was talking to Javi he happened to mention one of LSM London teams ‘hstream’ had a real time Twitter analytics idea. I got excited at the idea of tracking sentiment around Patriots vs Giants and even had a look at the odds at Betfair. I also tried Twitter manually, so to speak, and found and favorited one tweet which from a gambler’s perspective seemed to stand out. It turned out to be right, the 94-year-old grandmother backed the Giants, the winners of the Superbowl XLVI. Wonder what the results of hstream’s real time data analytics were?

PS: Post-Sony I now know this Giants case to be an example of #thinslicing on yes:-)

94-year-old grandmother predicts Giants to winPhoto by Stuart Glendinning Hall