When Ted Mann founded SnipSnap, the popular mobile couponing application, he didn’t anticipate the challenge his team would encounter converting printed coupons into a digital, mobile-ready offers using automated solutions, like optical character recognition. In a recent post on Fast Company’s blog, Co.LABs, Mann discusses how SnipSnap addressed the challenge by integrating with Mechanical Turk to develop a scalable data extraction workflow able to process more than 200,000 coupons per day.
Here are some of the kernels of wisdom Mann had to offer for new Requesters based on his own experience implementing Mechanical Turk:
- Implement effective quality control workflows. Mann specifically recommends using tools like Known Answers to audit Workers in real-time and Plurality to measure consensus between Workers.
- Streamline Workers’ user experience when designing your task to make it as simple and speedy as possible. For instance, consider adding auto-completes to fields, making it easy to tab between fields, and minimizing scrolling.
- Maintain consistent HIT pricing, if possible. Use caution when modifying HIT prices. Changing prices on HITs (especially reducing the price of HITs) can negatively impact your relationship with Workers.
- Spend an hour doing your own tasks. This will give you a sense of whether or not you’re tasks are appropriately priced and help identify opportunities to streamline your Worker experience.
By the way, in case this use case sounds familiar to you, we recently featured a guest blog post from the team at Upside Commerce – another mobile app developer using Mechanical Turk to extract data from print coupons for mobile delivery. You can read more about Upside’s story here.