UX Case Study: Add a Feature | Waze App

Project: Add a Feature (Mobile) Application Concept

Role: UX/UI Designer

UX Design Tool: Adobe Xd

Timeline: 5 Day Sprint

The Brief: Analyze an already existing and highly adopted app and incorporate a new feature into the existing product. The feature you develop will be based on an area of functionality to be explored and compared to user input.

The App: Waze


In this case study, I will cover the process and steps I took to develop a new feature for the Waze application. This will include the user research and prototyping tools that I used, my discoveries, my challenges, and the biggest takeaways from this project.

I chose Waze for my individual project due to its popularity and unique interactive interface. As a Google Maps user, I was curious to discover what the hype was all about.

FYI: Did you know?

Google bought Waze for $966 million in June 2013 to add social data to its mapping business. Waze’s 100 employees received about $1.2 million on average, the largest payout to employees in Israeli high tech.” — Waze Mobile

Before conducting user research, I needed to gather Waze’s existing data to increase the effectiveness of my primary research. For my secondary research, I dove into the Waze website, published articles, app store reviews, and previous surveys. These were the results that piqued my interest:

  • Waze has around 30 million active users.
  • Most people use it in conjunction with other navigation apps, such as Google Maps and Apple Maps.
  • The interactive feature gained his popularity due to its police alerts.
  • Waze provides users real-time updates on the best routes to take.
  • Reviews suggested that the application lacks community advantages.
  • Verbal directions and alerts seem to be poor.
  • The interface is cluttered and overwhelming due to the number of icons on the screen

Business’ outcome: Waze develops practical solutions that empower people to make better choices, from taking the fastest route to leaving at the right time, to sharing daily commutes.

After collecting information revealing a broader understanding of Waze, it was time to learn about its competitors.

The challenge with designing a new feature for an existing application is adding value to the product and still retaining its mission. In order for me to accomplish that, I needed to analyze Waze’s competitors. I had to quantify the value of their features in comparison to Waze’s features. This analysis would help me discover the gap in the market.

My first competitive feature analysis included: Waze, Google Maps, Apple Maps, and Mapquest.

As you’ll learn from my primary research, I needed to learn about the following indirect competitors: Strava, MapMyRide.

Competitive Feature Analysis

As you noticed from my competitive feature comparison above, Waze had three areas with opportunities for design: non-driver attributes, parking spot distinction, and community hotspot.

I should mention that I had to go back to this tool due to rich data gathered in my user research.

My goal here was to identify Waze’s ideal position by analyzing current business needs and opportunities in the marketplace. The insights I gathered from my secondary research validated that Waze’s users take advantage of navigation applications for their accurate and reliable functionalities. The competitive feature comparison also suggested that they use these applications for non-motorized transport.

The above data helped me create a market positioning chart to visualize Waze’s current position as opposed to its competitors. From there, I was able to locate its future position that would help bridge the gap in the market.

Market Positioning Chart

Now it was time to learn more about the users’ mental models by conducting primary research.

The challenge was to dig deeper into certain areas that didn’t focus on Waze or the most obvious use of navigation systems. I first sent out a survey which received 39 responses that gave me a broad understanding of user’s gains, pain points, and jobs surrounding navigating applications. Based on the results, I conducted 5 user interviews in which I got insights surrounding pedestrian concerns, mainly involving cycling. This is where I uncovered knowledge gaps around cycling applications and sent out a second survey.

From my results, I discovered that most responders use specific applications for cycling and for driving due to routes suggestions and special features.

These were some of the quantitative insights I received:

  • 80% of responders use Google Maps for non-driving directions.
  • 44% of responders walk, run, or bike often.
  • 75% of responders use navigation apps to find the best routes.

The qualitative data included the following:

  • “I share my location with my friends via text or send the Google location to them”
  • “In my mind, Waze seems more driver focus than pedestrian”
  • “I go from my Apple Maps and also Waze”


I needed to make sense of all the data I gathered from my secondary and primary research in order to convert them into actionable information. I first summarized all the data from my first survey and interviews into an Affinity Map. When I discovered the users' needs and pain points surrounding pedestrian directions, I sent my second survey to identify similarities in users’ gains, pain points, and jobs using both cycling and navigating applications.

After sorting those insights into the affinity map, I was able to distinguish the customer’s main values using Waze.

Gains: Shows quicker routes — Shows backroads — Good for long routes

Pains: Interface is cluttered —only 4% of responders use Waze for non-driving direction — Pop-ups when driving

I then identified my users’ main jobs to be done: Quickest routes (Functional) — Waze is interactive (social) — Best routes make users’ feel confident(Emotional)

It was time to understand how the users achieve a task using both navigating and cycling applications. The task analysis involved a user trying to get cycling direction to go to Walgreens. The challenge here is that the task would be accomplished using Google Maps because Waze’s current state does not offer cycling directions. There I was able to breakdown the phases and distinguish the flow of the steps the user needed to complete the task.

This is when I added the emotional level of the user attempting to go on a cycling journey and created a journey map. Fortunately, a user walked me through his process of cycling while juggling two different applications to complete his task. I was able to put in the low and high points that happened in his journey based on the rich data received from our conversation. I was able to hone in on those frustrating areas and discover the biggest opportunities for design.

As you can see from the image above, Sebastien’s main pain points were:

  • Finding his way back to the main road
  • His free trial for sharing location ending soon
  • Losing momentum while switching between apps

I was able to come up with 3 Problem Statements from Journey Map:

  1. Sebastien needs to find a way to share his live location free of charge because he doesn’t want to worry about getting separated from his friends.
  2. Sebastien needs to find a way to reroute quickly during his ride because he doesn’t enjoy getting lost.
  3. Sebastien needs to find a way to get directions and record his route at the same time because he doesn’t want his ride to get interrupted.

These problem statements then turned into HMW to keep ideation focused on our users’ needs.


The challenge with an individual user research design project is having to ideate solo. This leads to a high risk of bias and falling in love with the first idea that comes to mind. Thankfully, for this project, I was able to pair up with two of my classmates (Vlada Tkach and Lauren) to collaborate during the ideation phase. As we brainstorm our good and bad ideas, we managed to come up with non-obvious solutions that would add value to Waze. Better solutions were created.

It was time to prioritize Impact vs. Effort features by using the MoSCoW method. This tool helped me filter the must-haves, should-haves, would-haves and won't-have features to really hone into the solutions that would add the most value to Waze’s users. Based on the data gathered from the users, I knew the feature must include the following:

  • Pedestrian directions
  • Audio Notifications
  • Verbal directions
  • Route tracker

To verify that the features added to the Moscow chart would create gains, relieve pains, and add value to the services and products, I had to go back to the product segment of my Value Proposition Canvas. I was able to pinpoint the following benefits for some of the solutions:

  • Offering popular routes would help guarantee the journey.
  • Pedestrian directions would help set expectations and save time.
  • Verbal direction and audio notifications would reduce distractions.

Before I reveal the minimum viable product — Let’s do a recap:

We know that Waze does not offer pedestrian routes and directions. When our user goes cycling, they usually need both navigation and cycling applications to complete their journey: The cycling application to record their routes and the navigating application for direction.

This leads us to our MVP:

Our Waze application allows users to identify the best routes and hazards on their paths.

The new Pedestrian feature will help customers select the best routes for non-driver users with a recording system and verbal directions that will help them avoid interruptions and welcome accessibility functions.

For the JTBD Framework, I came up with a Main Feature Story to recap and explain the main problems the feature will solve.

When I’m cycling, I want to be able to navigate to my route without any unexpected distractions and also keep a record of my journey for future rides because it makes me feel confident and excited for next time”

Since the pedestrian feature would be built from scratch, it was challenging to plan the exact route the users’ would take to achieve their goal. I needed to create a User Flow Chart to help me break down the sequence of the tasks, including the grouping of each screen, and how users will navigate them.


Lo-Fi Wireframes

Once I settled on a design idea, I moved forward with sketching a set of low-fidelity wireframes and conducted a usability test with 5 users. I added all potential attributes that would be useful to Waze’s users while using the Pedestrian feature, including:

  • Live Location Share
  • Audio Notification
  • Verbal Directions
  • Popular routes

As you’ll notice from the image above, I circled the areas that needed improvement. These enhancements were also suggested by the 5 testers.

Usability test’s quantitative data:

  • 83.3% direct success.
  • 277.3 sec to complete the task. (which was too long)

Usability test’s qualitative data:

  • The user asked for color contrast to differentiate directions and labels during the feature’s tutorial.
  • Indication of the route being recorded after turning it on.
  • Precise directions to avoid confusion.
Mid-Fi Prototype

At this stage, I was concerned about the flow of the feature and needed to verify if it met my user’s expectations. After testing my mid-fi prototype with 5 users, I was able to gather rich data that later help me enhance the flow of the feature.

Usability test’s quantitative data:

  • 15 average clicks.
  • 1 min 13.6 seconds to complete the task.

Usability test’s qualitative data:

  • The user asked for the direction instructions to be shortened.
  • There was a confusion between cycling and pedestrian icon.
  • The mid-fi showed too many screens to click through

After gathering data from both the low-fidelity and Mid fidelity usability testing, I was able to refine the attributes of the feature and improve the user flow steps.

The areas for improvement were as followed:

  • The screens were reduced.
  • The recording switch was added.
  • The instruction labels were shortened.

The challenge with adding a feature to an existing application was with finding the style guide for the designs. I had to make sure that I could mimic the exact color scheme, typography, and overall layout of the screens. Thankfully, due to extensive research and chrome UX extensions, I was able to identify certain assets.

Hi-Fi Prototype

Given the time constraint of this sprint, there are a few features I was unable to add to the application. During the ideation process, there were features that stood out to me for future steps, including:

  • Adding a meeting point to map.
  • Saving routes for offline purposes.
  • Ability to report hazards verbally using keywords.

While designing Waze’s new feature individually, I found myself going back to several of the tools and living documents. This helped me come up with a solution that would add the most value to the application. I also found myself overcoming several challenges by trusting the process. Here are a few of my takeaways from this project:

  • There are many great ideas, always select the one that will add the most value to your product and your user.
  • Each step facilitates the next — trust the process.
  • You have to be creative when adding a new feature to a well-known interface without any public resources available (Style Guides).
  • Don’t fall in love with your ideas — including those that aren’t your first.

Thank you for taking the time to read my case study. I would appreciate any feedback. Let’s connect on Linkedin.

Product Designer