Crowdsourcing and the Task Unification Tool
Crowdsourcing has a crowd of critics. Crowdsourcing is the notion of distributed problem-solving where problems are broadcast to large groups of solvers in the form of an open call for solutions. The belief is that the “wisdom of the crowd” yields superior results over what individuals can do. The use of the term has spread to just about any activity that involves groups of people tackling an issue.
The critics have a point. Crowdsourcing seems to be an old story retold a new way. The idea of collaborating with others is not new. The idea of reaching out to thousands to gain insights about a problem is not new either. Here are two examples held out as crowdsourcing best practices that make the point. A Catholic church in Germany launched an online open idea competition. On the competition platform, young people are encouraged to submit their ideas about what they would like to change at the Catholic Church.
That is not crowdsourcing. That is market research.
Here is another. CreateMyTattoo connects customers with a community of 700 tattoo artists who compete to design the perfect custom tattoo. Customers see several variations of their tattoo idea and provide feedback to the artists during the contest. The site guarantees at least ten unique custom tattoo designs or your money back!
That is not crowdsourcing. That is competitive bidding.
Here is a better example that starts to move in the right direction. DHL, a courier company, is testing a way to use city residents to deliver packages along the route as they go about their daily travel. The programs is called “bring.BUDDY,” and it hopes to reduce road congestion and DHL’s carbon emissions. Participants use a smartphone app to specify their travel. An alert is sent to them of any package that needs to be delivered along their route. In return, the participants receive points which they can redeem at local stores.
This is novel. But DHL could go further with the concept. What else do people know or do (explicitly or tacitly) that DHL could use to improve operations, reduce cost, or increase revenue? For example, what if DHL had a way to know what delivery routes are optimal based on information fed to it by customers (through cellular technology)? What if the crowd could identify open parking spots, report packages that need picked up, or spot activities that might demand the use of courier delivery?
There is a better way to leverage the crowd. Rather than “source” the crowd for their explicit ideas, we “cache” their tacit, day-to-day routines to detect patterns and insights. In “crowdcaching,” people don’t know that they are contributing their small, incremental movements and decisions to a larger pool. It is like digital ethnography. We collect large samples of tiny decisions to “bootstrap” our insights and decisions.