- Elaine Tran
- Michelle Hoang
- Steven Derhammer
- Vishwas Shetty
We decided to employ multiple research methods to triangulate the biggest issue with the self checkout kiosks at stores that a user faces.
This method allowed for the discrete observation of users interacting with self-checkout systems in a typical environment and in standard use case scenarios. We collected data covering user gender, age range, payment method, number of items, whether assistance was needed, and any breakdowns in the process. Users were most commonly seen to have had issues with bagging their items as well as scale related issues.
A survey was generated that allowed for a large quantitative response from self-checkout users regarding their experiences. The survey generated about 148 responses, it was determined that that 60% of participants experience issues with bagging during the self checkout process.
After assimilating the results from the research, we triangulated that bagging is one of the biggest issues faced by users.
Next we created personas to identify the primary, secondary and negative users for our solution.
How can we make self-checkout more efficient for users by reducing hindrances to the bagging process?
We then went on to sketch possible solutions for the bagging issue. We spent close to an hour sketching out the most wacky and out of the box solutions. We convened to discuss our sketches and narrowed it down based on feasibility, convenience, and other metrics.
One of the things we realized early on, was that our idea of an automated bagging system was far fetched and came to a conclusion that we will not delve into the technology side of this. We wanted to concentrate on the the look and feel of the prototype and assume magic would take care of the technology.
Keeping this in mind, we went on to build our very first low fidelity prototype and tested the concept with our peers in class. We were the only team in class to build a physical artifact, while rest of the teams were building apps or websites. We did get some raised eyebrows, but the professor encouraged us to continue exploring and dig deeper.
Based on the feedback received during the usability testing of the prototype, we decide to make the following changes
- Making the auto bagging feature clearer to the user
- Auto bagging feature indication.
- Increased auto bagger item insertion port.
- Green, yellow, and red lights to provide users with feedback during auto bagging.
- Removed the directional arrows originally.
- Installed a window to visually observe the process.
- Changed label to “place item here”.
- Physical changes to the auto-bagging design
- Increased volume of auto-bagger storage area.
- Added shock dampening material.
- Improved ramp design
- Split ramps to provide increased surface area and reliability for the auto bagger.
- Increased the declination angle to reduce friction and expedite item transport.
- Safety guides installed on the edges of the ramps and around the bagging area.
- Encouraging auto bagging by increasing distance between item scanner and bagging area.
- Changes to payment methods
- Payment page added to on-screen interaction point rather than unit frame.