This project was developed for an Italian bank which targeting Gen Z as their clients. From the briefing, they want to encourage a balanced and healthy lifestyle for the user, initiate people to share resources, experiences, and knowledge to support growth, and contribute to a more just society. There are other requirements like prefer to use of AR/VR/Machine Learning Technology, and the delivery should be a mobile application.
Financial services should empower Gen Z in re-defining their style through incentivizing sustainable re-use and exchange.
Retrend is a service which allows Gen Z users to discover and trade vintage clothing through the local community system, and thereby promote a sustainable lifestyle. Retrend adjusts the user’s contribution to the environment into easy-to-understand “Sustainable Impact”, and encourages users to share the impacts on social platforms.
Takeaways from desk research
Due to the radical change of the environment in which Gen Z was born, their behavior and consumption habits are significantly different compared with the previous generation. McKinsey summarized these differences in a detailed report. In short, there are 3 aspects.
The aspect of consumption is particularly interesting.
- Consumption re-signified: From possession to access: For Gen Z, consumption means having access to products or services, not necessarily owning them.
- Singularity: Consumption as an expression of individual identity: The core of Gen Z is the idea of manifesting individual identity. Consumption, therefore, becomes a means of self-expression.
- Consumption anchored on ethics: 70% of survey respondents say they try to purchase products from companies they consider ethical. Consumers increasingly expect brands to “take a stand.”
Consumers are willing to pay more for sustainable products.
More than 1/3 of consumers are willing to pay 25% more for sustainable products, but Gen Z is even more willing to pay an extra 50-100% than other age groups’ consumers.
Did you know?
Extending the life of clothing by an extra nine months of active use would reduce carbon, waste, and water footprints by around 20-30% each and cut resource costs by 20% (£5 billion). (Data from WRAP)
Generation Z people will be a dominant group in the fashion second-hand market in the future.
Takeaways from Interview
From the results of desk research, I was inspired by the market of vintage clothing and second-hand clothing and decide to start with them as an entry point. So I conducted field research in a vintage clothing store. The interview aims to investigate the user’s consideration and motivations for buying second-hand vintage clothes. Finally, I synthesized the results into an empathy map.
Guilia is a student who lives in Milan. She has been studied fashion for about 2 years from school. She buys second-hand clothing not because it’s cheap but because of the history stories hidden behind the brand. She is highly receptive to social networking, online shopping. In addition, she is a staunch environmentalist and insists on boycotting fashionable fast-moving consumer goods.
Empower Gen Z in re-defining their style through incentivizing sustainable re-use and exchange.
I conclude four Key Insights from the interview results: Motivation, Discovery, Perceive, Transparent. And wrote the following four need statement:
- Motivation: Giulia needs a way to buy eco-friendly clothes to satisfy her ethical needs.
- Discovery: Giulia needs a way to find clothes by her preference to manifesting her individual identity.
- Perceive: Giulia needs a way to perceive the environmental impact to satisfy her ethical needs.
- Transparent: Giulia needs a way to verify the authenticity of clothes to feel safe.
To be Journey Map
I used the following customer journey map to visualize the future service state. By creating this map, I shape the concept more specifically, from high-level stages/tasks to each step’s detailed needs.
After the journey map, I decide to use user flow to organize the pages need to design for the prototype. The user flow here also represents a minimum viable product; I also conclude the design priority and test plan inside the same flow.
- In Test 1, I want to test the process of share sustainable impact, which is the most important feature of the solution. Test 1 can also be considered to validate the solution in “Motivation” and “Perceive” mentioned before.
- In Test 2, I want to test the process of the seller scan and publish her object. Test 1 can also be considered to validate the solution in “Perceive” and “Transparent” mentioned before.
- In Test 3, I want to test the buyer scan/browse process to buy an object. Test 1 can also be considered to validate the solution in “Discovery,” “Perceive,” and “Transparent” mentioned before.
Low Fidelity Prototype & Test
I used Adobe XD to design the low fidelity prototype at a structural level and keep testing during the design process. After introducing the project background, I assigned tasks for the user to complete and ask for their feedback.
- In Test 1, the user said they couldn’t understand the sustainable impact’s meaning. The impact just numbers, and users don’t understand what the number’s meaning.
- In Test 2, the user pointed out that he was uncertain about the fabric’s composition’s prediction accuracy. He hopes to know the accuracy of the scanned results and wish the page layout cleaner.
- In Test 3, The user wants not only the recommendations but also why these recommendations. So they can gain knowledge about styles and object-related information.
High Fidelity Prototype
The high-fidelity prototype was created in adobe xd too. You can try it here. Here are some improvements according to the test results from the wireframe.
- In Test 1, the improvement is to make sustainable impact easier to understand, I used analogy to explain it. Like CO2 saving-You get 2 cars off the road!
- In test 2, the improvement is added accuracy prediction and used AR to scan results directly on the scanned object, which is more immersive and engaging.
- In Test 3, the improvement is used labels to indicate the WHY of recommendations. Users can click the labels and discover more similar items.
The scan function sound utopia, are you sure it is feasible?
The answer is yes. I did a feasibility study to find technologies that may support the scan, prediction feature. DeepFashion is a large-scale clothes dataset with comprehensive annotations. Base on the dataset, a machine learning model was developed, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks. The accuracy of FashionNet, in category & attribute prediction, can achieve 82.58% and 45.52% respectively. With the expansion of the Deepfashion dataset, the prediction accuracy will be improved for sure.