Harvard Business Review
by Joshua Gans
October 13, 2011
The volume of information available freely to consumers is mind-boggling.
But even factoring in ease of access — no more sifting through card catalogs or microfiche in dark libraries — attention remains scarce. The task of sorting the useless to find the useful is a critical challenge.
Search was supposed to solve this problem and allow people to efficiently uncover the information they need.
Google’s algorithms have been structured to consider the information available and give us a ranking based on “quality” or “usefulness.”
Bing actually envisions itself as a “decision engine” rather than a search engine.
In each case, the goal is to resolve the information overload issue by identifying the sources most likely to have the answer you need.
Impressive though this is, how many times have you been frustrated at the first few links on a search page?
It’s easy to spend too much time plowing through a bunch of suggestions that turn out to be irrelevant (and possibly dangerous if you’re repairing small home appliances — take my word for it).
Searchers have no way of parsing the content itself.
What Google has done is narrow the set of possible pages that could contain the answer. But the results contain lots of other stuff as well.
“Content platforms” are emerging that are designed to solve precisely this problem.
A content platform is a standardized means of presenting information.
Take, for instance, Yelp.
If you want restaurant information, it gives you a list of possibilities with a ranking that can be sorted on distance or user reviews.
But if you want to delve deeper, you know what you will find: the same layout for every restaurant, showing you were it is, some pictures, contact details, and a review.
There’s a lot of content but it has been arranged in a standardized, easy-to-use format.
That makes it easy to understand what you are getting.
To be sure, both Google and Bing are moving toward standardized content formats for information on restaurants or movies or travel.
But the real challenge is organizing the vast wealth of information that is not part of common searches — for instance, when you want to find a reliable small-appliance repairperson.
Twitter and Facebook represent alternative content platforms, each of which focuses on their users’ scarce attention.
They make it easy to scroll through hyperlocal news. In Twitter’s case, the 140-character limit makes this particularly easy, freeing up consumers’ time.
At present, the mother of all content platforms is Wikipedia.
Indeed, while many focus on its free access and user-updated timeliness, my conjecture is that Wikipedia’s success is predominantly due to its standardized format.
To provide a format that allows distributed content provision, Wikipedia pages have a set of requirements and a certain layout.
Pages are sorted with headings and summary boxes so that users can very easily traverse any given page.
Traditional encyclopedias — even in digital form — are not laid out this way.
Print versions require reading large amounts of text. Online ones are dressed for education, allowing a student to examine an entire entry with various multimedia products.
Those are all very well but not suited to an age of scarce attention and a need for specific information.
Wikipedia pages feature prominently in search engine results.
In fact, sometimes it seems that Google and Bing are just search bars for Wikipedia. But that itself represents an efficiency.
Search algorithms are finding useful content that is standardized, making it more likely to be click-worthy.
There is clearly room for innovation. One wonders whether a super content platform is over the horizon that not only provides information but also allows it to be easily assessed or scanned for quality.
Originally published at https://hbr.org on October 13, 2011.
About the author:
Joshua Gans is the Jeffrey S. Skoll Chair in Technical Innovation and Entrepreneurship at the Rotman School of Management, University of Toronto, and the chief economist at the Creative Destruction Lab.
He is the author of The Disruption Dilemma (MIT Press, March 2016) and a co-author of Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business Review Press, April 2018).