The threat to search isn’t social. It isn’t social because search and social involve two different use cases; they are two different ways of using the Internet.
Search is used by people who want to find something and know enough about it to be able to type something into the search box. They’re focused on a task and have a specific intent. They aren’t simply using a search engine because they enjoy it.
Social is used by people for all sorts of things, but when it’s to find something they’re typically simply asking friends. Or browsing along trying to figure out what to type into a search engine later. Or they’ve already made a decision and are specifically following a brand or other information source.
Search and social co-exist for the most part. Yes, they influence each other, but neither is a true threat to the other type of use case. The ability to search for things doesn’t negate our need to be social. The ability to be social doesn’t negate our need to do things. Well, except for when all day is wasted reading Twitter and poking friends on Facebook.
A process of how people behave online might look like this: First, a person talks with their friend about the thing they’re looking for. Then, the person investigates the stuff their friends talked about and also does their own research by using search. After generating a group of possibilities, the person narrows it down.
In the above process social activity occurs before and after search activity. While search activity shares that middle section with social, it isn’t replaced or threatened.
This is no different with online behavior than it ever has been. Just because people walked into a real estate office and got to search through a big book of listings doesn’t mean that they never talked about it with their friends later. Offline or online, social activity is a part of who we are.
Another process of how people behave online might look like this: First, a person searches for something he knows he wants. Then the person narrows down the choices to a reasonable number. Finally, the person discusses this reasonable number of choices with friends or other people he trusts.
In this example, same as with offline behavior, search and the thoughts of others are also both used. Again, nothing new here. It’s not like people weren’t "social" before Facebook.
Both of these use cases show normal, common human behavior: First, generate a number of possibilities. Then, narrow it down. We like to see "all the possibilities" and that’s why we look for things and use search.
But at the same time, holding all of those possibilities in our heads is overwhelming. This is why we narrow it down. Our friends are great at helping us narrow down the universe into meaningful chunks. Maybe too good sometimes.
Search engines, with their focus on showing the most results, haven’t been all that great at narrowing things down. It’s a difficult, complicated task to read the mind of someone based on the few clues they type into the search box.
There is so much intuition in the inferences made about what someone was really meaning to find when they type "Omaha real estate" into a search engine.
Mind and machine
There are two basic patterns at play in this business of finding things online: a pattern of generation, gathering, discovery, and a pattern of winnowing, culling and disregarding.
There are two mechanisms in use in this business of finding things online. There’s the machine, with all of its algorithms, data and historical behavior-watching. Then there’s the mind, with all of its bias, relationships and historical experience.
The mind and the machine both play significant roles in the gathering and culling of possibilities. They can amplify each other. Sometimes, they replace each other, but often with limited success.
The mechanisms have tendencies for being better at one of the two patterns. The machine excels at gathering and discovery. The mind excels at culling and disregarding.
Digital social tools train the mind to behave in a fashion that increases discovery. Share this. Tweet this. Like this. And if you do, the machine’s ability to generate will increase.
But, so far, attempts at applying the social mind to the results of the machine have not been useful. We don’t necessarily want to see the influence of our friends in all of our searches. We search because we’re seeking something outside of our experience, for something we don’t really know.
The machine, however, could certainly learn how to cull results more effectively. And perhaps it is.
Million Short search engine
There’s an experimental search engine that threatens Google search in a way that social does not. It’s called Million Short.
The premise of Million Short is that it shows the search results minus "the top million results." We’ve all done searches where the results are filled with irrelevant and not-useful garbage. In the real estate space this is especially true — overoptimized, misleading, not helpful stuff.
Million Short strips out the garbage and disregards it. Or at least that’s what it says it’s doing. It doesn’t actually take out listing results one to 1 million. But it does strip out a lot. The end result is a pile of blue links that are more relevant.
My first time using Million Short left me with the same feeling I had when I first used Google. Back then I had been an avid AltaVista user. I had experience with library science so boolean search was the bomb. I couldn’t imagine anything more awesome for searching the web. I used Google and all that changed. The results were just great.
Million Short feels the same way to me. The results are just plain good. Less poking around to Page 2 or 3 before finding something worth clicking.
I’m not saying this will replace Google or anything. I’m just presenting an example of a machine getting extremely good at culling the results of search to end up with more meaningful results.
It’s even kind enough to list what sort of sites it’s stripping from its results. You can add them back in if you like. On a search for "Omaha real estate" here are some of the sites it stripped from the results:
Are those sites yours? Is your site like these? Is the machine wrong? Are the remaining results more meaningful?