Context

I made this case study during my UX nano degree journey with Udacity, and it’s one of my favorites.

I feel like there’s a lack of in-depth explanations of design thinking process, and it’s often undervalued, even though understanding these steps should be a standard practice because they play a critical role in problem-solving.

This is precisely why I want to dive deep into the details in this case study, giving a clear understanding of the design thinking process. However, don’t worry about it, I’ll serve up a concise and fun read! 😃🚀

Now, one of my fundamental design thinking rules is: Not every problem in the universe screams “App needed!” it’s like assuming every meal should be a fancy dinner when sometimes, a good sandwich does the trick.

Our job isn’t solely about creating apps or websites for every situation. It’s primarily about solving problems, big or small. Sometimes, all that’s required is a cool feature, a physical product, a service, or even a minor adjustment to the system. So, always bear in mind, our focus is on problem-solving, not making an app!

Where it all begins

You know that feeling when you and your friends, ready and in a hurry to hang out, can’t decide where to go? Picture this: a group of friends, tons of options, loads of preferences, and limited time, or suddenly you bump into an old friend, and you both have to quickly figure out where to go?

Trust me; I’ve lost count of the times I’ve been stuck, spending too much time searching through endless options, while considering things like what we’ll do, the desired atmosphere, ensuring it’s not too far and also convenient for everyone when hanging out.

Here’s the catch: there isn’t one tool that works really well for this. So, I end up using different apps — like Facebook for opinions, Instagram for more pictures and videos, and Google Maps for directions.

Did you also know that our mental well-being can be significantly influenced by the number of decisions we make each day? So, it’s worth exploring a solution that allows you to kick back and relax while your hangout decisions are effortlessly handled.

Understanding the users

It’s always better to start a project with a well-structured research plan. It acts as a roadmap, helping maintain focus, avoid distractions, and define the areas of exploration. Without a plan, it’s easy to feel overwhelmed.

The research plan has three main aspects:

1. Research objectives: which are the specific goals we aim to achieve, usually aimed towards understanding the problem area.
   I was interested in exploring potential opportunities for a digital solution that makes it easy for users to quickly get outing place recommendations.
2. Identifying our target audience: like who we want to learn from, which means choosing the groups of people to interview or observe for deeper insights. In my case, I focused on adult residents who frequently go out and responsible for making decisions about where to hang out.
3. Method Selection: There’s no one-size-fits-all method, as it depends on various factors, including the project stage. I chose two methods: semi-structured interviews and surveys.

Semi-structured interviews

I started with a semi-structured interviews because they are best for initial exploration, offering flexibility for deeper insights, making them ideal in my case.

Research goal

• The goal is to learn about how adult residents choose where to hang out, including what approach or tools they may use, as well as their preferences and aims.
• Identify any problems, unmet needs and potential opportunities for a new product.
• Utilize these insights to take the right path towards simplifying the process by reducing the time and effort needed.

Research questions
Based on my research objectives, I prepared research questions to stay focused on my objectives and gather relevant information efficiently.

• [Needs]
  – What is necessary for choosing the right place to hang out?
  – What are the current pain points for users?
• [Behaviors]
  – How do locals currently choose a place to hang out?
  – What existing platforms/digital tools do they use?
  – How do they use it to their advantage?
• [Idea validation]
  – Do users really need a product that recommends the best places to hang out?
  – How do users feel about the existing solutions/ways to get recommendations?

Conducting interviews

1. Recruiting participants who meet the criteria.
2. Preparing an interview script to follow as a guide based on research questions.
3. Conducting 30-minute in-person interviews to collect qualitative data

Survey

In the previous study, I observed that there isn’t a single tool that handles the problem efficiently, users end up using multiple tools, which is time-consuming.

While combining all the necessary features is a great opportunity, I need more data to understand the needs and preferences of a larger population of adult residents and validate the main idea as well.

Research goal

• Identifying what users care about the most.
• Having a clear understanding of all the desirable features in order to come up with an effective solution.

Research questions

• How can this product help the entire decision-making process?
• What are the users’ top priorities?

Using surveys

1. Create a survey based on the goals and research questions.
2. Test survey questions with 5 users before sending to the rest
3. Send surveys to people whose answers in the screener meet the recruiting criteria.

Affinity map

From interviews and surveys, I was able to identify themes and opportunities using affinity mapping. This technique involves grouping observations and words from interviews into themes, providing deeper insights and preventing confirmation bias.

You could also explore affinity mapping further, in this Nielsen Norman Group article, for more understanding.

Extracting key insights

Adult residents, in fact, struggle to pick the right place to hang out because there are so many options; additionally, in order to find the ideal place, they need to use multiple platforms, which takes a long time.

Key findings:

1. Too many outing places options overwhelm users.
   The new product could display the best recommendation, with additional suggestions available upon user request.
2. Seeing images and videos of the place has one of the most positive impacts on decision-making process.
   The new product could include images and videos for each place.
3. Unknown price range is the most common issue that users face when deciding where to hang out.
   The new product could make sure to include the price range of each place, as well as emphasizing offers.
4. All adult residents participants use multiple tools to find places to hang out, as they rarely find everything they’re looking for on a single platform, and don’t rely on a single digital tool for their decisions.

Recommendations overview:

Simplifying the process of finding the best place recommendation by focusing on highlighting the user’s top priorities, such as including pictures, videos, and price range, and by providing filtering options

I believe there’s a great opportunity to create a product focused on local hangouts. Surprisingly, many existing products are all about travel, when, in reality, most of us spend way more time hanging out locally than traveling!

Framing the problem

Adults have trouble picking a good place to hang out because there are so many options, and they have to use many different apps to find the right one.

Writing how might we questions (HMW):

Based on the problem statement and insights gathered earlier, I focused on breaking down the main challenge into specific areas to consider potential solutions for each in the next step.

• How can we make it easier for adults to choose a place to hang out when they’re overwhelmed by all the choices?
• How might we encourage users to trust and rely on our product for their hangout decisions?
• How might we minimize the need for users to rely on multiple tools for hangout suggestions?
• How might we ensure that users have access to the necessary information about potential hangout places?
• How might we help users decide?
• How might we let them know the prices and any special deals at these places?
• How might we bring everything together in one solution, so they don’t have to use lots of different apps?
• How might we personalize recommendations based on users’ preferences and past choices?

Shifting from problems to actionable solutions

Building on themes and opportunities, I expanded into feature ideation using creative prompts like ‘how might we’ questions I just made.

I begin the process by generating plenty of ideas, focusing on quantity rather than quality at first, saving criticism for when I have a lot of ideas to work with.

Evaluating ideas and prioritizing features

I used a value vs. complexity quadrant to prioritize features. It’s a strategic approach that ensures high user value while managing development complexity, keeping the product efficient and user-centered.

Here are the selected features I decided to include:

1. Save for later: users can save places with a like button, making it easier to access their favorite spots quickly.
2. Ready place ratings: since users care a lot about ratings, instead of starting complex rating systems from scratch, we import ratings from Google, and this could make the idea more applicable.
3. Advanced filtering options: users can filter places by moods, activities, etc., solving the main user problem and there are ready-to-use filter libraries that will simplify the process.
4. Recommendation section based on recent views: It’s easy to build and many users appreciate how time-saving it is.
5. Push notifications for special offers: from a marketing and business perspective, it could be advantageous for both users and place owners, while also working as a prompt for users to use the app.
6. Sharing places: this is a common and essential feature because users will undoubtedly need to share a place with the people they are hanging out with at some point.
7. Attach videos: attaching at least one video is relatively easy and was a feature requested by users during interviews.
8. Multiple Images: users find it crucial to see images from various angles, and it’s highly applicable and easy to include them.

From ideas to sketches

Once features were decided, I used Crazy-8 sketching, a rapid ideation technique, to quickly brainstorm design ideas for each.

This approach helped spark creativity and generate a variety of design concepts in a short amount of time.

It’s safe to say that if my sketches were a cake, they’d be the ones that fell apart in the oven, but hey, remember, perfection is overrated, messy sketches are just part of the artistic process! 😌🍰

Building the initial prototype

I created mid-fidelity prototype based on the previous sketches.

Home Page: this serves as the user’s starting point, featuring notification alerts, their user profile, and a main card that quickly takes them to the search page with a single click.

It also includes various sections and tabs for different types of places that users can explore and save for later.

Search by page: here, users will find advanced filtering options to tailor their search to their preferences.

They can choose from various activities, moods, set the distance, and select a price range. Additionally, users can filter places based on their ratings. To see the results, users simply click the “Show Result” button.

Result page: this page displays the details of the best recommendation based on the user’s chosen filters and preferences. It includes information such as the location, price range, mood tags, and a link to the place on Google maps. Users will also find attached pictures and at least a video of the place that they can scroll through.

If the user is satisfied with the results, they can either save them for later or check the location. Users can easily return to the homepage through the navigation bar or explore similar places by tapping on “view similar places”.

Conducting usability tests

I conducted moderated usability testing sessions to observe user interactions and collect feedback, and here are some of the findings:

Search by page:

• Users struggled with the rating selection section, taking more time to process it.
• Some users wanted to choose “Any rating” but this choice was not available.

Results page:

• Users attempted to scroll through the main picture instead of noticing the gallery below.
• Several users overlooked the Google Maps link.
• Interestingly, 3 out of 5 users were expecting to find a link to the place’s social media account rather than a location link.

Iterating and Refining

Made some iterations based on usability study and feedback.

Search by page:

Made the rating section simpler: by removing unnecessary stars, reorganizing the buttons and adding “Any rating” option.

Results page:

• Added social media accounts: because several users stated it was important for them to check them on social media. Also added the buttons in a form of icon button providing some form of iconography to make it more user-friendly.
• Improved the results page: by adding actual similar options cards instead of just adding “view similar results” button.
• Allowing users to scroll through the main image: while also displaying the total number of photos and the number of images currently being viewed.
• Improved the clarity of the location link: by changing it to button instead of a link because users didn’t notice it, and some users mistaken the link for other purposes.

High-fidelity prototyping

I made a high-fidelity prototype based on the previous mid-fidelity prototype.

Also, before getting started with high-fidelity design, I looked for inspiration on Mobbin, a platform that displays UI/UX patterns from top mobile apps.

I went with Android Material Design Theme Kit — The Material UI Kit in Figma because I wanted to save some time and also make sure it’s consistent.

Iterating and refining

I made some iterations to enhance accessibility following WCAG guidelines. This involved changing lighter shades, such as grays and the primary pink color, to darker shades to achieve better contrast, I also improved contrast by changing the background color from a pale pinkish white to a brighter white.

User testing and KPI insights

After that I conducted unmoderated sessions using Lookback, with the following tasks:

1. Find a place to hang out from the home page by getting an immediate recommendation, search by filters, and when you get results, you can complete the task.
2. Save the place recommendation you got for later, and check all your saved places, when you get there, you can end the task.

The main KPI I focused on was increase task success rates.

Test insights:

1. It was difficult to navigate using the navigation bar because the icons were too small.
2. Users spent extra time on the result page.

Some iterations were made based on users’ experience and standard UI practices:

Enlarging the navigation bar icons.

Adjusting the line spacing in the results page and ensuring its consistent all over the page.

Engineer’s handoffs

I used Zeplin to export Figma designs and prepare them for Engineering Handoff. It’s a bit ironic because, well, there were no actual engineers this time — but it was a part of the Nanodegree.

Design handoff in Zeplin is done through a link that includes essential resources such as color palette, text style catalog and designs so developers can access everything they need to translate designs into code efficiently.

Conclusion

So basically, the solution turned out to be Outing Places Recommendations App, designed to simplify finding the best recommendations by focusing on user priorities like pictures, videos, and price range, along with advanced filters reducing the time and effort needed.

Okay, looking back, to be honest, I could’ve improved the UI to give the app a cleaner look and conducted more user testing. Despite these areas for improvement, this project has been a valuable learning experience.

Looking ahead, the app has growth potential, possibly evolving into a dynamic social platform. However, this would require serious testing and validation to ensure user resonance. Personally, I’m hopeful this concept will become a reality someday, making outing planning easier.