Improving the self-checkout counters at NTUC FairPrice
Through the Design Thinking process, we discovered unique insights from the data collected and came up with recommendations to improve the overall experience of a user at NTUC FairPrice’s self-checkout counter.
CONTEXT
Here are some key considerations for this project.
Target Group — We chose working adults aged between 25–39 as they’re more tech-savvy and digitally fluent.
Outreach — Due to the Covid-19 measures forcing us to minimise contact with other people, we only managed to reach out to those in our close circles.
DESIGN THINKING
- Empathize — We used observation and interview methods to understand how a user feels about their experience using the self-checkout at NTUC FairPrice.
- Define — To identify the problem and what a user needs, we noted the pain points in their journey.
- Ideate — We brainstormed for possible ideas that could improve the user experience.
- Prototype — We made a low-fidelity digital prototype with limited functionality but clickable areas which features the suggested improvements. We’re focusing on the user experience part and not the user interface at this stage.
- Testing — We tested the prototype on 5 users to get their feedback and pinpoint areas of revisions.
1. EMPATHY
In order to make observations about how users experience the interface, all 5 members of the group tested the checkout experience and made notes on what we felt, said, thought and did.
Each of the researchers observed and interviewed 1 other user and consolidated our results into in an empathy map.
Consolidated Empathy Map
Key Comments
We categorised the responses of the users in 2 different ways.
1. One was a chart categorising each response by key insights we identified across what participants did, thought, felt and said. There were 4 key insights we found.
2. Secondly, a more in-depth look at each participants’ responses was collated in the Consolidated Empathy Map.
Key Insights
From the 4 key insights we gained from our observations, we were able to discern certain pain points:
- Feels pressure
- Payment Issues
- Unsure of scanning process, bulk scanning
- Prefers quick and efficient process
2. DEFINE
Based on our observations and data gathered from the “Empathize” stage, we used 3 methods to analyse the similar themes the users faced. Namely, they are the Aggregated Empathy Map, Persona and Customer Journey Map.
Aggregated Empathy Map
An aggregated empathy map represent a user segment rather than a single user. They are made by integrating many separate empathy maps from individuals who have comparable tendencies and can be combined together into a single segment. The aggregated empathy map combines themes from throughout the user group and can serve as a starting point for developing personas.
Persona
Personas are fictional users that represent the target group of the project along with their needs, experiences, behaviours and goals. We identified similar behavioural patterns and presented a scenario in which most of our users would use the self-checkout counters at NTUC FairPrice. We then created a persona named “Darren”. This would help us as UX Researchers to identify with the user we’re designing for.
Customer Journey Map
Based on data gathered from the Empathize stage, we presented that information through a customer journey map. This map tracks the persona’s process as he completes his task which in this case is purchasing groceries through the self-checkout counter. His actions and emotions from the journey are tracked visually on a timeline. This is really helpful in highlighting the pain points experienced by the user at specific parts of the journey. From this, we can focus on those parts and propose ways to rectify the problems.
Through the use of these 3 methods, we:
- Discovered how a user interacts with the self-checkout at NTUC FairPrice and the pain points they faced through the process.
- Identified gaps in the product through the pain points which we translated to ideas for improvement.
3. IDEATE
After defining the problems faced by the users, we can now proceed to find solutions for them. At this stage, we can finally address the needs of the users. We brainstormed and came up with ideas through the methods listed below.
Generating HMWs
In this step, we framed our questions into How-Might-We (HMW) statements to set ourselves up for generation of ideas in the brainstorming stage. We aimed to keep the question broad enough to inspire potential solutions and avoided narrow questions to allow us to dive deeper. Our group came up with a few generic HMWs and the rest fell into three categories — Scanning, Experience and Accuracy, which we felt were too specific. Ultimately, we decided upon “How might we design a streamlined system that speeds up the checkout process in an intuitive way?” to be used in the Brainstorming stage.
Brainstorming
Building upon our chosen HMW statement “How might we design a streamlined system that speeds up the checkout process in an intuitive way?”, we churned out a large number of ideas as means to produce as many options as possible. We went wild on our ideas, not limiting them to their feasibility, as the abundance of options would provide us with more to build on and test in the coming steps.
SCAMPER
In order to generate more ideas, our group used SCAMPER — a lateral ideation technique. We created a lotus pattern with a chosen idea from the brainstorming stage as a central concept, then expanded outwards with solutions that — Substituted, Combined, Adapted, Modify/Maximise/Minify, Put to other uses, Eliminated, Reversed or Re-arranged the central idea. This SCAMPER technique was used to build upon existing ideas to come up with more innovative options.
Worst Idea Possible and Four Categories Method
We incorporated the Four Categories Method starting with novel ideas and pulling them back into relevance. To come up with ‘Long Shot’ ideas, we used the ‘Worst Idea Possible’ method. This ‘inverted’ search process would help us turn our impossible ideas into a possible ones, as it helps us to creatively examine the ideas and gain insights towards a greater solution.
After consolidating some wild ideas, we moved on into the convergent state — evaluating, comparing, rankings and clustering to pull together three main ideas to act on. We then worked backwards on these ideas to create feasible alternatives that we used in the Prototyping stage. We decided on 3 ideas that focused on 3 different features, making the checkout process more efficient, reduces errors and improves privacy — addressing the insights that we identified in the Empathy stage previously.
4. PROTOTYPE
As a group, we collectively picked one idea each from the “most relevant” and “long shot” categories to be developed into prototypes.
Here is the idea that we picked:
- Membership login that brings up an item directory of all NTUC’s available items (with frequent purchases prioritised at the top) for the user to select. Payment details and discount codes are also saved & automatically selected.
Although we initially started out by sketching out paper prototypes, we decided to collate our ideas on Figma for easier collaboration & testing without the need to meet face-to-face.
We kept our prototype low-fidelity, with minimal functions and limited design detail. This enabled us to clarify our ideas with each other and rapidly iterate solutions without getting caught up over design details. By creating connections between hotspots on the pages, we came up with the following prototype for the first round of user testing.
5. TEST
With the first iteration of our prototype, we conducted a round of user testing to collect unbiased feedback and identity areas for improvement.
Collectively, we managed to conduct 5 user tests. This is an overview of our test scenario, tasks, and user profiles.
Scenario
- You are shopping for the week’s groceries and you have a few menus that’s rotated every week
- You want to check out in the shortest time possible
- You already have a FairPrice account with a few grocery lists saved
Tasks
- Sign in to your account
- Purchase items from two grocery lists
- Add a few items from frequent purchases
- Scan one additional item before checking out
- Choose your preferred payment method
A challenge that we encountered while testing with Figma was that users were expecting a fully functioning product, and understandably distracted by the discrepancy of details in the prototype. For instance, many testers were confused when the hypothetical items in the cart page did not match what they selected during the test.
Upon reflecting, we concluded that for future low-fidelity prototypes it would be better to use more rudimentary prototypes such as on paper or powerpoint, and only proceed to Figma & other user testing interfaces for high fidelity iterations.
After consolidating tester feedback, we sorted it into positive & negative feedback and smaller sub-categories.
Using the rose, bud and thorn technique, we concluded that while users liked the frequent purchases feature, they found that the overall system was confusing and added redundant steps. We also identified the saved grocery lists feature as an area for potential exploration in a different platform (e.g. online checkout instead of in-store).
NEXT STEPS
Collectively, we found that the design thinking process should not stop at “Test”. We must repeatedly “ideate, prototype and test” so that improvements are made until there is a viable new product at the end.
Based on the conclusions from the rose/bud/thorn exercise, here are some proposed revisions to the prototype:
- Keep the featured products section & figure out how to integrate it into the current checkout system
- Rethink the saved grocery lists section, perhaps re-purpose it as an app or online only feature with the accompanying FairPrice app
- Instead of adding extra features (& by extensions steps), we can think about how to simplify the checkout process instead
- Reduce the number of buttons & improve visual hierarchy on each page to minimise confusion
For the subsequent user tests, we will also aim to test users from an older demographic (e.g. 40–50) with the same goals as our persona. As our first round of user tests were conducted with younger users, a different age demographic could provide different insights and highlight other limitations of our prototype missed by younger testers. This will help us to design and cater to a wider range of users.
CONCLUSION
We tried to solve the problem by adding more features, however it ended up making the system more complicated.
Moving forward, as we go back to the ideation step, we will keep in mind that our goal is to streamline the process.
Putting into practice the design thinking process helped us to see things in a different way, and also understand the techniques taught in class better. Although the various methodologies appear straightforward, we found that they were difficult to execute well.
In short, we now know what we don’t know.
