Social media advertising is big business nowadays, and of the two top sites, Twitter and Facebook, one is doing it right, while one is doing it very, very, wrong.
Twitter recently announced "interest targeting", a new form of online advertising that'll allow businesses engaged with the micro-blogging site to direct adverts towards those who'll be most interested in whatever products they're trying to sell.
There's a more detailed explanation of it here, but it can be understood simply if I use myself as an example. As a member of the Computing team, I regularly tweet articles from the site (as I'll no doubt do with this one). As there's a technology theme, promoted tweets for me – usually on the basis of a hashtag – include fancy things such as the new iPhone 5, or perhaps the latest Android tablet.
I also play a lot of video games, unsurprising given my previous life as a games journalist, so I tweet a lot of thoughts about what I've been playing, meaning the evermore lavish publicity campaigns for the latest game will be fed directly to my feed. While it's unlikely that I'll immediately take in every advert and rush out and buy the latest device or video game – the prices are what stop me the most – at least businesses are getting my attention with adverts that are relevant to me. It's a business model that Twitter has managed to successfully apply.
Facebook, on the other hand, doesn't seem to "get" advertising, despite increasingly pushing product more directly into users' faces. The newest development is for overly large, intrusive ads to appear on your news feed, simply because one of your friends – or that person you only met once but who still added you as a friend – likes a product.
I'm not too bothered whether this friend likes that particular brand of beer, or if that friend farmed a particular number of pigs in one of the many farming games. I'm certainly not going to go out and buy that beer for an evening of that game because they're there.
By eliminating high entry costs for big data analysis, you can convert more raw data into valuable business insight.
A discussion of the "risk perception gap", its implications and how it can be closed