Self-Directed Project

Self-Directed Project

Self-Directed Project

CoffeeSpot

CoffeeSpot

CoffeeSpot

The Problem

The Problem

The Problem

Due to the uncertainty that arises with working in a new local space, whether that be due to the unknown quality of Wifi, or access to plug sockets, or the fear that you will be kicked out for sitting on your laptop too long without ordering a second coffee - users often find themselves returning to the safety of familiar café chains to avoid this uncertainty, despite wanting to support local businesses.

The Solution

The Solution

The Solution

Create an app that facilitates a symbiotic relationship between long-stay laptop users and local cafés in the Norwich area by eliminating the sense of the unknown.

The Method

The Method

The Method

  1. Research and survey the target audience, discover their key frustrations

  2. Talk to local cafés, establish their stance on long-stay laptop users and gather data on their facilities

  3. Organise and problem-solve insights

  4. Analyse competitors, what do they do that tackles these frustrations? What are they missing?

  5. Generate visual ideas based on insights, analyse user interfaces, and research best practices

  6. Organise information by creating a proposed site map and user flow

  7. User test wireframes, and continue to test throughout the design process, to ensure a user-centred design

Research:

Research:

Who is the target audience?

This app is mainly aimed at long-stay laptop users. Through my research I discovered this group is predominantly made up of students and remote/hybrid workers, so I focused my research on these groups.

Surveying the target audience

I put out an extensive survey to the target audience and compiled these results, pulling out important insights and potential pain points that my app can target solving. I received both qualitative and quantitative data through this process, having users rank the importance of different amenities as well as asking a range of open-ended questions on their thoughts and feelings associated with working in public spaces.

Extracting most common responses and outliers from survey results

Surveying local cafés

I reached out to local cafés to gain insights on their perspective of long-stay laptop users, receiving a wide variety of responses. I also visited a range of the responding cafés in person to take note of their facilities,I could have fabricated the information on café amenities, but going in person proved to be invaluable to my project as it raised unexpected issues such as: How to quantify table sizes? How many seats does a large sofa count as? What if the amount of seating is variable? What if the Wifi quality reading is ‘Strong’, but then the experience of using it is bad?

Insights : How are users feeling?

"I don't know how long is okay for me to work in a café, I get anxious about overstaying my welcome. If I knew it was okay to work in a local business, I would 100% choose that over a chain"

"I don't know how long is okay for me to work in a café, I get anxious about overstaying my welcome. If I knew it was okay to work in a local business, I would 100% choose that over a chain"

"I don't know what the space will be like until I get there, or whether they'll have good wifi or plug sockets for my laptop, so it's easier to go somewhere familiar"

"I don't know what the space will be like until I get there, or whether they'll have good wifi or plug sockets for my laptop, so it's easier to go somewhere familiar"

Creating Personas

Considering my collated data, and the paint points discovered, I used this to form three main personas. I find creating personas to be a useful tool in summarising research insights and to refer back to throughout my design process to ensure I meet the needs of the user.

Personas

Problem Solving:

Problem Solving:

Opportunity-Solution Tree

Taking the pain points I had found through my research, I then thought of ways I could target solving each of these in my app by first creating an opportunity-solution tree. Opportunity-solution trees allow me to tackle each of the key points, ideating solutions for each. Through this process I generated ideas for potential features my app should include to solve each of the key user pain points in their journey to finding a café that meets their needs.

Opportunity-Solution Tree

User Story Map & MVP

I considered the minimum viable product (MVP) for my application, turning my opportunities into tangible product functionality. I built a user story map to plan the features for my app, using the first row as the MVP and expanding downwards on further info and features I could include.

MVP - What to Include?

User Story Map

Design Process:

Design Process:

Initial Sketches

Based on the insights gained through my research, I began to generate ideas for my final application. I usually begin the design process physically with sketches and post-its, before transitioning to user flows and site maps, which become the basis for my wireframes.

User Flow & Site Map

Wireframes: Low Fidelity

Created in Figma

Testing & Iterating:

Testing and Iterating:

Testing and Iterating:

Low Fidelity Testing Insights

I tested at a low fidelity level as I am creating a completely new product and want ensure that users are always at the forefront of my designs. I compiled my testing insights to consider moving forward.

High Fidelity -> Interactive Prototype

Testing and Iterating

I conducted moderated user tests to identify any pain points in my prototype and then iterate my design. I set tasks and observed users experience with the app, asking questions throughout about their experience, the images below show my user test plan and then some of the pain points I discovered through my testing.

Insights : How are users feeling?

Insights : How are users feeling?

"I would genuinely use this all the time. I really like how it's got so much info, sometimes when you go you're unsure what they have, it's so useful to know"

Final Interactive Prototype

Sofia Durnford

Portfolio