Back in February we reported Google Trends data showing search volumes declining for numerous common categories. In the past four years, searches for movie reviews, financial services, florists, digital cameras, cancer treatment and sexy lingerie are all down about 50 percent. Now Alex Campbell, a strategy consultant at DTDigital / OgilvyInteractive in Melbourne, posits that consumers are migrating to social media instead of search engines to find what they need online. He lists three factors:
1. “Social networks have dramatically expanded my network of contacts” who “share similar interests,” Alex says, so he trusts their recommendations more than those from Google.
2. SEO experts have killed their own game. “The SEO industry has transformed from ‘help Google index my site better’ to ‘how can I beat Google’s relevance algorithms to show people results they don’t want’.” The natural result is many common searches turn up companies that are not the most relevant, but have gamed their way to the top of the listings.
3. “The flow of information has changed,” Alex suggests. “In times past, I was always seeking out information through Google search. Now the vast majority of the information I am interested in comes to me, rather than me having to go out and find it.” As people become skilled in setting up human networks among experts with ideas they like, they build their own information feeds that often supplant Google.
The movie review test
Here’s a test. Want to know if “District 9” is a good movie? Here’s what Google returns; and here is what Search.Twitter.com gives you. Which results provide a better feel for the film?
Alex is not predicting the death of Google, and neither are we. Search engine marketing, especially paid search, remains a powerful tool for marketers to share wares with the customers searching for them. We believe, though, that Google requires ever more careful management of paid search campaigns due to rising competition and diminished consumer interest. With only 24 hours in a day, it is obvious that Google will reset at a lower search volume … because people seeking information via human networks spend less time asking computer algorithms for help.
We spoke today with a gentleman working on a business plan for a very clever, and potentially lucrative, business. Research is required to tune the concept, and especially to predict which types of customers will be most interested. So we suggested that instead of focus groups or quantitative studies, he instead stage a small web campaign, insert a snippet of Quantcast code into the banners and landing pages, and use it to match inbound visitors to the vast data sets of user behavior online — which would pinpoint the exact demographics of the audience who likes his offer.
You know. Open the door and observe who walks through it.
Data is dangerous because it lulls us into false security — we often want to predict what will happen based on erroneous theories, and then fail to see the reality transpiring before our eyes. Wired notes this week the meltdown on Wall Street was tied to a single math formula that allowed investors a shortcut to assess hugely complex risks (um, big mistake). Our Twitter colleague Max Zeledon points out Nassim Taleb’s thesis that major events are really unpredictable; humans in hindsight try to make sense of the disorder in the universe by linking data points into logic flows, when the reality is Black Swans — things that shouldn’t exist — often just pop up. You can bend your mind thinking about the paths of fate; see Schrödinger’s cat and then ask which of yourselves is going in to work tomorrow morning.
Sometimes data can predict events, if screened carefully; Google does this beautifully with its little known Flu Trends site, collecting user Google searches for flu remedies to predict outbreaks in the United States two weeks before the Centers for Disease Control. Tel Aviv University professors are sorting Gnutella music searches by the location of consumers to predict when small bands will spike into bell curves of popularity. And in our favorite example, a simple chart comparing home prices vs. rent over the past 28 years indicates clearly that the U.S. housing market was due for a massive headache in 2009.
So are we humans vain to try to see the future, based on the data at hand? Or does randomness really make forecasting impossible? We ordered Taleb’s Black Swan tonight to learn more. Until then, we’ll keep trying.