Research & Insights

By Lukas Biewald, September 22, 2009

TechCrunch 50 – Business Card Analysis

One of my favorite scenes in American Psycho is the business card scene. A business card’s importance cannot be understated as Christian Bale’s character decides to kill a man who had a better business card. In order to avoid a similar fate, I thought it would be nice to know what kind of reactions people might have to my business card.

With this in mind, we demoed CrowdFlower in the TechCrunch50 DemoPit last week on Tuesday with a live task in which we scanned images of a person’s business card and asked crowdsourced workers from the Amazon Mechanical Turk channel to write five kind words about the person based on what they saw. Here are some examples of what some workers said:


Worker id Kind Word 1 Kind Word 2 Kind Word 3 Kind Word 4 Kind Word 5
37928 Intuitive Smart Organized Connected Gentlemen
38341 computer-savvy articulate intelligent hard-working ambitious
37928 Organized Experienced Accredited Professional Thorough
43713 Smart Efficient Hard-working No-nonsense Leader
42272 respected technical consulant Manager senior kindly person high powered
42344 technical organized professional respectable senior
41905 professional connected a business card so awesome it screwed with the scanner! international traveler
1148 Long term Life Accessible For the long haul Reality

It feels great to be complimented by strangers, and seeing the positive reaction people had to the kind words said about them reminded me of this awesome short film.


For tasks like these, where the responses are subjective, it is generally hard to control for quality. Workers can input anything, making it difficult to tell whether they are actually doing the task or scamming you. This is why we also asked workers to input the business card holder’s name. The name was usually clear, so we could quickly tell as judgments were being made whether the workers were actually completing the task.

We found that workers were generally inputting the correct names, and knowing that they are doing this part of the task correctly, we can for the most part infer that workers are actually doing the other part of the task well and not scamming us. And as we can see in the above examples most words were indeed kind.

Of the 306 total judgments for 61 business cards, only 29 were of bad quality (a single judgment asked for 5 words and the name). A judgment was considered bad when workers were not inputting kind words describing the person, repeating things like “NA”, “this is good”, “this is bad”, “…”, “No image”. But 19 of those 29 bad quality judgments were due to the business card being scanned poorly looking like the ones here and here.

If we had wanted to hear kind words even when images were of poor quality, a quick task improvement to increase our “kind word” quality might be to clearly specify what workers should do in these cases. Because we didn’t specify to do this when the image was particularly poor, workers were more likely to finish the task as quickly as possible by giving us non-useful data (non-kind words). But overall the results and words used were certainly interesting (I particularly liked worker 41905 who said “a business card so awesome it screwed with the scanner!” as kind words).