HP Labs ponders grabbing attention in the age of social computing
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Beware social network denizens, you are being watched and your wallets measured.
The vast sea of information available on the internet is no doubt useful, but is it fruitful? HP Labs is hard at work figuring how to harvest the collective intelligence of groups arising from "web 2.0" and turn it into a profit.
Bernardo Huberman, a senior fellow and director of the Social Computing Lab at HP Labs, gave a room of journalists an update today on what the group has been doing.
One question pondered is how a web site can more effectively present its content.
The researchers used the popular web content aggregate digg.com to investigate how people look at web sites with an abundance of information that change extremely quickly.
After studying about 1,000 digg.com news stories, HP said the team was able to make a mathematical model to predict how long it takes for the popularity and novelty of an article to die off and disappear from the front page.
As people begin to pay attention to an article, it's exposure in the media will increase in turn. But as it becomes more popular, the novelty of the item fades, thus decreasing the amount of attention it gets.
Huberman said their model suggests that arranging a web site so that new and novel items are most prominently displayed is generally more effective at attracting clicks than prioritizing based on its popularity.
HP said it tried the process on its own web site to select which items are recommended to customers. Preliminary results, according to HP researchers, showed a 30 per cent increase in the attach rate of sales.
In a related study, Huberman said HP researchers found that user recommendations generally yield surprisingly unimpressive results.
As people become increasingly resistant to traditional forms of marketing, viral advertisements and recommendations are being used more often as an alternative strategy.
But the lab found that while the likelihood of someone buying a product does increase with the number of recommendations at first — it soon plateaus to a constant, but relatively low probability of purchase.
Huberman suggests this is because people often ignore recommendations of their friends in a social network. He said viral marketing may also be limited by the virtue that most people are actually only talking to small groups of people online.
"The scope and breadth of people we interact with aren't as big as we think they are," Huberman said.
Recommendation chains in online retail tend also to break down after one purchase or a relatively short amount of steps.
It seems despite the new emphasis placed on customer opinion and collaboration, the old adage stands true: don't sell the steak, sell the sizzle.
More information on HP's Social Computing Lab as well as its latest results can be found here. ®
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