Heading into a major site redesign, with a product team split among several divergent and firmly held opinions, I decided it was time to break out a card sort.
Card sorting is an old, and often overlooked, UX method for organizing information. In its simplest form, you hand a test subject a stack of index cards representing the site’s content and ask her to sort them into piles and name each pile. This is what’s known as an “open card sort” — the naming of the piles is left up to the test subject. This is a great way to discover mental models of an information space, natural groupings of content, and labeling. Once you have derived a set of generally accepted categories and labels, you can move on to a closed card sort. In a closed card sort, you will provide a set of category names and ask test subjects to place the content cards into the categories where they seem to fit best. This is a good way to test your nascent navigation scheme.
These tests are pretty straightforward. The difficulty comes when you go to collate the data. Measuring the results of a card sort comes down to similarity scores. Similarity scores measure the number of times different test subjects place the same card in the same pile. If all your test subjects sorted two cards into the same pile, then the two items represented by the cards would have 100 percent similarity. If half the users placed two cards together and half placed them in separate piles, those two items would have a 50 percent similarity score. Multiply that by 40 or 50 cards and a dozen or so test subjects, and you’re looking at a long, costly process of collation and counting.
That’s why I was very happy to find Optimal Sort, an online card sorting tool with great analysis components. Part of the Wellington-based Optimal Workshop, Optimal Sort allows you to create cards representing your content and then invite test subjects to sort and categorize the content by following a link to your survey. The real value comes with the analysis tools. Optimal Sort provides a clutch of analysis tools, the most useful of which are the Similarity Matrix (shown below) and the Dendograms. Both quickly highlight the obvious content clusters and suggest generally understood labeling. For me, those two features were worth the $110 monthly fee.
So far, we’ve had over 100 responses to our survey and some very clear patterns are emerging. It seems that when the humble old card sort is merged with some smart data analysis and presentation, we’ve got a great new UX tool.
An elegant new visualization of internet languages, tools and traffic was recently released by the Chrome team, Hyperakt and Vizzuality. It shows the converging streams of languages and viewers from 1990 to the present in The Evolution of the Web. Equally impressive is the growth of traffic from one petabyte a month in late 1995, to over 27 petabytes in 2011.
Netflix’s recent move to shift subscribers from DVDs by mail to streaming over the net holds some valuable lessons for the newspaper business, argues Ken Doctor, the author of Newsonomics. By making streaming roughly half the cost of DVDs by mail, Netflix is moving their customers to where the company needs them to be, Doctor writes in The Newsonomics of Netflix and the Digital Shift.
“Imagine 2020,” Doctor writes, “and the always-out-there-question: Will we still have print newspapers? Well, maybe, but imagine how much they’ll cost — $3 for a local daily? — and consumers will compare that to the ‘cheap’ tablet pricing, and decide, just as they doing now are with Netflix, which product to take and which to let go.”
Of course, Netflix doesn’t have to contend with the huge revenue gap between print advertising and digital advertising as newspapers do. All that is still TBD, Doctor writes, but Netflix may point the way.
The May issue of The Atlantic has funny-scary piece about a social engineering contest on Twitter. Titled Are You Following a Bot?, the brief article outlines a recently concluded experiment by the Web Ecology Project wherein socialbots (programs) were let loose on the Twitter network to try to win friends and influence people.
Turns out, they did pretty well. According to the Web Ecology post on the contest, “In under a week, Team C’s bot was able to generate close to 200 responses from the target network, with conversations ranging from a few back and forth tweets to an actual set of lengthy interchanges between the bot and the targets.”
Think of the labor that can be saved if you outsource all those boring tweets about what you ate for lunch and the cute thing your cat did today to a bot! Free from the chains of Twitter, regular people will have scads more time for walking around outside, or engaging in F2F conversations with other actual people. And, if socialbots can pass the Turing Test, marketers have gained a powerful new spamming tool.
Apparently, the applications of the tech may be a bit more sinister than that. As The Atlantic story noted, “A week after [the Web Ecology Project’s] experiment ended, Anonymous, a notorious hacker group, penetrated the e-mail accounts of the cyber-security firm HBGary Federal and revealed a solicitation of bids by the United States Air Force in June 2010 for ‘Persona Management Software’—a program that would enable the government to create multiple fake identities that trawl social-networking sites to collect data on real people and then use that data to gain credibility and to circulate propaganda.”
Steven Johnson has a fascinating article in the November issue of Wired titled, “Invisible City: What a Hundred Million Calls to 311 Reveal About New York.” For those of you who don’t live in Gotham, or one of the 300 or so other American cities that have instituted similar programs, 311 is simply the call center for New York City. It takes about 50,000 calls a day and returns information in 180 different languages. New Yorkers call in with complaints about rats, potholes, sewers and noise (noise is by far the biggest complaint category from 9pm to 3 am) and questions about parking rules, school closures and recycling.
Besides offering a safe way for cranky New Yorkers to let off steam, the 311 service has become an important data-gathering tool for the city. As Johnson points out in his article, “A data-driven approach to urban life makes sense, because cities are in many respects problems of information management.” Read more about Steven Johnson on his blog.
By categorizing incoming calls and tagging them with time and location data, city administrators can identify patterns and pinpoint problems. For example, the first hot days of May or June will bring a spike in questions about the city’s chlorofluorocarbon recycling program as New Yorkers look to dump their old air conditioners and buy new units. Similarly, a cluster of sanitary complaints about a specific restaurant will prompt a visit from the city’s health department.
So far the Bloomberg administration hasn’t made too much of the 311 data-pile public, but the potential for reuse of this type of info is huge. Imagine the suffering that could be averted if 311 data were merged with a public service like The Bedbug Registry.
Groups like Open311 are looking to standardize the system and open all the data to the public: “Open311 refers to a standardized technology for location-based collaborative issue-tracking.” That’s a pretty cool idea — bug tracking software for your hometown.