Matchify
Skills & Tools
About
Matchify is a web platform designed to use generative AI to help make the process of online shopping easier and less overwhelming. Matchify supports consumers in being more decisive and boosts buyer confidence when making online purchases, particularly in categories with a large amount of product offerings.
Duration
Spring 2023 - 14 weeks
My Contributions
During the framing process, I brainstormed problem space ideas and helped flush out all the areas of the Lean UX Canvas, along with creating a practical persona. In the research phase, I interviewed prospective users, which yielded rich, qualitative data and helped analyze this user data through empathy mapping and affinity diagramming. In the design phase, I crafted low-fidelity wireframes, which were later refined based on user feedback. I helped implement the design into a prototype, which was then tested with users through a series of usability tests to identify any lingering pain points within the design and inform platform improvements.
Team
Grace M.
Ethan B.
Jenna L.
Melika Z.
Aditya E.
Overview
We began this project by brainstorming problem spaces we'd like to explore throughout a semester-long project. After considering a few different options, we landed on exploring online shopping and the difficulties associated with the process. According to a 2021 research paper exploring the factors that limit online shopping behavior among consumers from multiple generations, several factors restrain consumers from buying products online, including a fear of fraudulent bank transactions, being more comfortable with in-person shopping, lack of experience, lack of confidence, insufficient product information, and a lack of trust.
Problem
What needs fixing?
Consumers can be indecisive when shopping online and can have a hard time purchasing products that meet their needs without feeling overwhelmed, leading to slower purchases, abandoned shopping carts, and more returns. They struggle to consolidate all the knowledge and information they need to feel confident in their purchases and understand the nuances amongst many different options. Consumers are having “analysis paralysis” while online shopping, so we set out to design a product that helps consumers feel more secure in their decisions while shopping online.
Product Opportunity
What's the value?
For our team, it was all about understanding users' experiences with online shopping platforms and identifying opportunities to increase purchase satisfaction and confidence among consumers when purchasing items online. By better understanding user pain points and exploring opportunities to improve the online shopping experience, we knew there was an opportunity to design something that minimizes the amount of time and external research needed to make a purchase, leading to speedier purchases and fewer returns.
Challenge
How might we...
Help consumers be more decisive and satisfied when making online purchases, particularly when shopping for an investment product or an item in a category with a large amount of product offerings?
Solution
What we made
A web application designed to utilize generative AI to make the process of shopping online easier and less overwhelming – think less tabs open and fewer abandoned shopping carts!
Matchify supports consumers in being more decisive and boosts buyer confidence when making online purchases by providing personalized recommendations to meet a user’s budget, preferred features, and more.
Design spotlight
Our Lean UX Approach
Lean UX?
Lean UX is an agile user experience workflow that values speed and learning over perfection, measurements over opinions, confidence over certainty, and outcomes over output. It’s all about trimming down the typical UX process and doing just enough to create a Minimum Viable Product (MVP) that will facilitate learning and support quick iterations.
Framing
To kick off the framing for this project, we began by using a framing methodology called the Lean UX Canvas and developed practical personas, while also taking time to discuss our assumptions and facilitate a user-centric conversation about the challenge.
Lean UX Canvas
To first get an understanding of the underlying problem space surrounding online shopping, our team completed a Lean UX Canvas to identify the business problem, potential business outcomes, users, user benefits, and potential solution ideas. By working as a team to come up with different solution ideas, rather than hone in on one particular solution, we were able to explore many possible approaches to solving the problem at hand.
Practical Personas
Our practical personas were crafted prior to user interviews to help us keep our focus user-centric from early on in the process and explore what we thought we knew about users in the problem space. By crafting personas early on, we felt more confident about the types of participants we planned to recruit for user interviews, which would later be used to inform changes to our personas and refine our understanding. We crafted two persona profiles in order to foster a shared understanding of target users, including what we thought we knew and what we didn’t know. Below we share their background, goals, motivations, pain points, implications, and some context.
Research
While we believed consumers were experiencing difficulties while online shopping, we still wanted to validate if it would be worthwhile to explore the problem space in more depth. To do this, we dove into our initial primary research process, which utilized user interviews, empathy mapping, and story mapping, allowing us to collect rich, qualitative data and develop a deeper understanding about users’ experiences shopping online.
User Interviews
Research Method
To develop a deeper understanding of when and why consumers sometimes have a hard time deciding what to buy while online shopping, how they decide if they should buy an item, and when and why they return products, we conducted eight semi-structured individual interviews with users aged 22-46. We were intentional in recruiting at least 2-3 interview participants that aligned with each of our practical personas in order to validate and refine our understanding of target users.
Goal
During interviews, we wanted to get to know participants, obtain an understanding of their backgrounds, ask them about their prior experiences with online shopping, and inquire about their pain points. We wanted to develop a sense of what aspects of their current online shopping processes worked well and what could be improved, while also better understanding how consumers build confidence and satisfaction when making purchases online.
Empathy Mapping and Affinity Diagramming
Analysis Method
Using the data collected during user interviews, we categorized the information into empathy maps, which you can find a sample of below, to explore what users were saying, what their online shopping processes looked like, how they felt while shopping online, and what they think about the problem space.
Once our empathy maps were put together, the nuggets of information were pulled forward into an affinity diagram, where we could look for common themes and derive insights. Key findings included:
Story Mapping
Synthesis method
Based on data collected during interviews, we also created a story map – a collaborative approach to condense into one artifact our understanding of the flow experienced by users as they move through their online shopping processes.
Working through our story map, we were also able to identify our MVP, helping our team align on the design aspects that would need to be prioritized and tested. Below is a list of the primary features that we planned to prioritize, as well as a sample of other important, but non-critical, feature ideas we could consider for a phase two iteration.
Refining Practical Personas
Based on the information we collected during interviews, we also refined our practical persona set by adding a third persona dedicated to the users struggling to shop for products in categories with an abundance of options, as we learned this is a primary source of users feeling overwhelmed while online shopping.
Design
With the problem to address at the front of our minds and our initial user research collected, we began the process of ideating on solutions as a team. To meet the constraints of the course, we decided to pursue one solution consisting of a web application designed to utilize generative AI to make the process of shopping online easier and less overwhelming – think less tabs open and fewer abandoned shopping carts – and support consumers in being more decisive and boost buyer confidence when making online purchases by providing personalized recommendations to meet a user’s budget, preferred features, and more.
Design Principles
Based on the problem space and our key research findings, our team came up with four primary design principles to help guide the design process.
Wireframing
With our research and design principles informing our design decisions, we were able to generate a series of wireframes as a starting point for the experience that would be offered by our solution. Each team member took time to develop solution mockups on their own, followed by a collaborative session where we came together as a team to discuss the options and choose the most promising and feasible option that would support our target users. Our early design approaches focused on implementing a personalized quiz feature to guide users through the process of sharing their preferences, offering a highly personalized experience.
Wireframes
Design Critique Feedback
In order to collect an initial round of feedback about the bones of our solution and evaluate our wireframes, our team participated in a design critique session with a group of fellow student designers.
Through receiving feedback from other designers, we hoped to learn, from an external perspective, how well the application would support users in meeting their goals and uncover any oversights in our design, such as points where user expectations might be violated or next steps might be ambiguous. We received quite a bit of meaningful feedback, some of which included:
- Concerns about a personalized quiz to establish user preferences taking too long, especially for users that just wanted to explore the application (this one was a bit of a bummer, since it was a feature we focused on, but we knew it was best to hear about this concern early on, rather than later)
- Need for clarification about how and where artificial intelligence was designed to be used
- The onboarding flow was missing an option to sync social media accounts, which other designers suggested would be really valuable for informing AI output, helping system recommendations be more customized and personalized for individual users
- Some screens were too content-heavy and could be overwhelming for users
- Faceted search options would be expected by users, since they are an element users would be familiar with and likely desire in our solution
- Lots of questions about the type of information that would be prioritized and displayed to users throughout the comparison process
Implement
Flow
Based on the feedback collected during the design critique session, our team got to work refining the design of our application and creating a prototype.
Evaluate
Usability Testing
With the first iteration of our prototype ready, we set out to collect another round of feedback, but this time from potential users. We conducted a series of six remote usability testing sessions with a diverse group of participants who aligned with Matchify’s target user groups and the practical personas we crafted.
Goals
From the usability tests, our team aimed to learn about three different research objectives:
Task
As part of the usability test, participants were presented with the following online shopping task to be completed using the Matchify prototype:
You are shopping for a women’s jacket. Use Matchify to input your preferences and review personalized product recommendations before choosing a jacket to purchase. Your budget is $200.
Following the completion of the task, participants completed a brief, post-task interview and shared their feedback.
Results
4 of the 6 participants were able to move through the task without assistance, but this left some room for improvement, which is where feedback from the participants came in handy.
As a result of the usability tests, we collected meaningful insights from participants, including:
- Participants liked how they could have product information across different options all in one place through personalized recommendations and were excited by the possibility of doing their online shopping “under one roof”
- The ability to compare products in-app and filter to refine options based on personal preferences was a valuable feature
- There was some confusion about products that end up in the “wishlist” bucket and what happens to products that are saved for a later, possible purchase
- When moving through the process of elimination, users wanted to be able to refer back to the compatibility score breakdown they saw while reviewing recommended options, as opposed to simply seeing the percentage
- Some participants found the most challenging part of using the application to be understanding exactly what the product was meant for and how to get started
Revisions
All feedback collected during the usability testing sessions were pulled forward into another affinity diagram, so we could evaluate common insights across participants and refine our solution to address the most glaring issues:
Future Steps
Based on the results of the usability tests, future work calls for an additional round of usability testing to assess if the changes we made to the design of our application resonate with users. Additionally, some other design challenges could also be tackled:
Mobile Platform
Many people spend time browsing and shopping online using a mobile phone, but with the focus on our MVP being on creating a web application, we didn’t prioritize the design of the mobile platform during our initial design iterations. However, without a mobile-based platform, we may be missing out on a segment of users within the market. How might we best design a mobile version of Matchify, so users can access the platform on a mobile device and shop for their perfect match at their fingertips?
Gamifying the Experience
Since users are not purchasing products directly using our application and are instead externally moved to the retailer’s product page when making their actual purchase, it would be hard for the Matchify team to know when users successfully make a purchase and when it’s time to remind them to review their purchases or inform us that an item didn’t actually end up being their “perfect match”. One way to encourage users to share their purchasing history is through gamifying the Matchify experience. As a future step, how might we gamify the Matchify experience to encourage users to share the details of their purchasing experiences after utilizing Matchify? Perhaps we could allow users to earn badges for reporting purchases or incentivize them to share purchasing history in exchange for discounts. If we had more time with this project, I think this is something that would be exciting to explore!
Reflection
How was working on this design challenge?
Working on this project with a group of teammates while all being remote proved to be a challenge, given different work schedules and course commitments. We did the majority of our work in Miro, Mural, and Figma, which were absolutely crucial to support the remote nature of the project. These tools enabled us to work individually on our own, while still being able to interact with each other and use assets from a small-scale component library we crafted. Overall, the time and effort we put into making this project successful led to a rewarding outcome and enabled us to turn challenges into opportunities. We were able to work through a challenge we were all personally familiar with and leverage each other’s working styles throughout the process. This project experience taught me a lot about my own work and teaming styles and showed me the importance of working with a team to conquer a problem!
Looking back, what would i have done differently?
Looking back, one thing I think our team would have done differently, had we had more time, was create a better procedure for our usability sessions with participants. While the usability sessions were informative, I think we could have set aside more time to prompt participants to complete an additional 1-2 tasks. With the sessions being remote and participants only asked to perform one task, I think we could have collected more feedback from users if they had a chance to move through the experience more than once. This would have required a more robust prototype and more time for implementation, but this is something that could be kept in mind if future usability sessions were to be conducted.
What did I learn from working on this project?
I learned how important it is to be able to abandon a concept early on and pivot when necessary. Thankfully, the professor overseeing this project, along with the Lean UX process we utilized, fostered an environment where failure was encouraged and learning was demanded. But we’re human and we were attached to our early design iterations, including the personalized quiz feature, but once we were able to find a way to pivot, we were able to come up with solution features that would ultimately better support who it’s all about – our users!