In 2005, Artie Rogers of NCSoft in Austin, TX, makers of Guild Wars^TM and City of Heroes^TM, wrote an article describing how the three thirties are vital to the adoption of a massively multi-player online game (MMOG). Much of what he described is applicable to optimizers in the Trada Marketplace, or any crowd-sourced environment for that matter for encouraging strong adoption.

So, what are the three thirties and what do they mean?

The first 30 minutes

This is probably the most important time—first impressions matter with games and software environments.  It’s important that the user is easily able to establish herself in the system, get acquainted with the interface and get started quickly.  The sign up process should be fairly quick in order to utilize the eagerness of a new member.  If at all possible tutorials should be provided; controlling and operating in the system should be simple and customizable.

This phase is no less important in a crowd-sourced professional environment.  The user should have few barriers to starting up.  However, unlike games, in a crowd-sourced environment there must be reasonable measures to ensure that only qualified applicants are granted access to the marketplace.

Considering Trada, what if you are great at coming up with relevant keywords, but lacking in your ability to write ad copy?  What if you are great at writing ads but couldn’t come up with good keywords if your life depended on it?  What if you really know the outdoor retail products and have no interest in real estate?

This ability to select an area of interest, focus, and competency, like selecting a guild or trade in a role-playing game, allows people to differentiate themselves, to not feel as though they are treated in a generic manner.  At Trada, we support the ability of an optimizer to define herself through the campaigns she may join, and in the future intend to allow copywriters to write ads that keyword wizards can use for a fee or a cut of the earnings.

No less important is defining the rules of the game—the world, if you will.  What matters?  What are the goals?  Could a player or member of the crowd describe what the environment needs for them to “win” after 30 minutes of use?  Knowing what is required of the participant is vitally important to establishing a clear sense of purpose.

Beyond learning the rules, there’s another level of learning that’s needed to function in the environment– what are the commands and tools to manipulate the system?  This step may require pre-existing knowledge or vocabulary, such as a spell-checking task in Mechanical Turk^TM or knowing how to utilize an ad group in Trada; but based on that knowledge, how does a user go about creating an ad group, how do they manage it?

In my opinion, we have not done very well in the first 30 minutes at Trada, historically.  Our sign-up process has too many delays for that hook needed to take advantage of initial enthusiasm.  Tutorials and clear definition of goals have been lacking—they need to be “in your face” or at least easily available.  Our navigation has improved over the last year, but still has a long way to go.

We are hoping that with our new release focused on crowd mechanics, we’ll be able to address goal definition and newbie (and eventually advanced) tutorials.  Knowing which actions to take and how to take them will also be clearer in the new interface.

I’ll discuss the first thirty hours and first thirty days in my next blog post.

Trada’s CEO Niel Robertson (@nielr1) was interviewed by David Bratvold about the Crowdsortium on The Daily Crowdsource.

The Crowdsortium is is a group of crowdsourcing industry practitioners that have self-organized to advance the crowdsourcing industry through best practices, education, data collection and public dialog. Check out the membership page for more information.

While attending the SES Conference in San Fransisco and the SMX East Conference in New York, we decided to bring along a gumball machine and Crowdsource our favorite problem: how many gumballs in the gumball machine? (Fun fact: There is a story behind why Trada has gumballs in its logo.)

Now that we’ve got some data, here are our results.

We had 215 participants in San Fransisco and 288 participants in New York. After a first pass at the data, we realized there were a number of “village idiots” (people with guesses that were significantly off). To put things in perspective, this accounted for about 0.9% of San Francisco guesses and 1.7% for New York. There were roughly twice as many erroneous guesses in New York than San Fransisco. You may extrapolate from this as you see fit.

The next logical step was to remove these clowns and proceed with a high-level summary analysis of the data.

The crowd’s average guess in San Fran was 1780 – versus an actual count of 1629 gumballs.

In  New York, the average guess was 2131 against an actual count of 2116 gumballs.

So in an absolute count, New York significantly outperformed San Fran by being off by only 15 gumballs.

Because we are overachievers, we created histograms (with erroneous guesses removed) to better understand the spread and distribution. Surprisingly, the distributions were very similar in shape and both were skewed correctly.

What we really wanted to see was: how well did the Crowd perform? First off, you’ll notice that in both cases one lucky individual guessed closer than the Crowd. However, at Trada we like to look at the Crowd’s performance in relation to every individual. When we measure the proximity to the actual gumball count the Crowd is closer than 92% of the participants in San Fran and 98.7% of the participants in New York.

The Crowd is performing, on average, at the 95th percentile. That’s impressive.

Another characteristic of the Crowd is the ability to negate individuals’ biases. In both New York and San Fran the majority of individuals underestimated the number of gumballs, 67% and 61% respectively. So how was the Crowd able to overcome this bias? If you take a look at the high-tech crayon-drawn infographic*, you will see that above the actual number, the guesses have a broader distribution. These outliers pull up the Crowd’s average.

So what’s the takeaway here?

The Crowd beats the vast majority of the individuals comprising it, and the collective result is more powerful than that of most of the individuals involved.

This is our belief at Trada, and this data just gives us a little pat on the back.

***Created by  Michael Lavrisha (@vrish88) during his arts and crafts time.