Case Study
2 to 3 min read
INTRODUCTION AND RESULTS
DoorDash Allergy Filter is a feature designed for users with foodborne allergens who need to find their allergens quickly and easily.
Results
User Satisfaction: 83% increased.
Time on Task: 12 second average improvement.
CLIENT
DoorDash
TEAM SIZE
Individual
TIMELINE
Sept - Oct 23'
(6 Weeks)
DELIVERABLES
Prototype
DISCLAIMER! I am not affiliated with DoorDash. This is only a conceptual case study.
DELIVERABLES
PROBLEM DISCOVERY
A family member ordered food once from DoorDash, and suffered an accident that required the use of an epipen.
Traditionally, the dish doesn’t contain their food allergen. However, this got me thinking. Does DoorDash have anything to protect users with food allergies?
USER RESEARCH
I surveyed at least thirty participants with food allergies via social media and reddit to validate my assumptions. Additionally, the objective was to discover their frustrations when it comes to ordering food via delivery.
Respondents stated that they enjoy the convenience of having food delivered but face issues during the ordering process.
Respondents were asked to share a why the moderation process was so difficult. Based on the stories of those I surveyed, I mapped the user journey of ordering food.
Food Selection
Customization 😡
Payment
Delivery
93% stated that their problems are mostly in the customization phrase.
I narrowed my focus down to the problems faced during the customization phase and summarized the most common pain points of my respondents.
Food Customization
• No quick way to identify food allergens.
• Limited allergen information.
• Incomplete menu listings.
• Forced to call store to confirm food allergens.
• Must research dish ingredients.
SECONDARY RESEARCH
To gather ideas on how other companies tackle food allergens, I chose to do a competitive research.
As of 2023, the ONLY food delivery application involving food borne allergens is Uber Eats.
SWOT Analysis
I chose to do a SWOT analysis to deep dive into the positive and negative aspects of UberEat's allergen feature.
Uber Eats Analysis
Strength
Easy one button press filter.
No competitor on the market.
Weakness
Filter applies to restaurants. Not food specifically.
Restaurants can "opt-in" for marketability reasons.
Lack of allergen customization.
Opportunity
No emerging competitor on the market.
Updated versions of filter in future.
Threats
Startup companies using an allergen filter.
Consumer backlash from inaccurate information.
Conclusion: UberEats lacks a food allergen tool; choosing to sort restaurants instead. DoorDash currently has an easy opportunity to capture the market of users with foodborne allergens.
Challenge Faced
The information gathered from the SWOT Analysis inevitably ended up being of little help seeing as Uber Eats DOES NOT filter out specific food, only restaurants. However, I did take take notes of the easy one button press filter during my ideation phase.
PROBLEM STATEMENT
So, users with food allergens enjoy ordering food delivered because it's convenient.
But…
People with food allergies state it's a problem to order from DoorDash because it lacks an allergen filter.
IDEATION
I believe in the phrase "don't need to reinvent the wheel". Therefore, I came to the choice to create a food allergen filter - furthermore, users are already familiar with using filters for e-commerce, blogs, ect.
Instead, I opted to ideate innovative aspects that enhanced the user experience of a filter.
A major complaint faced by my respondents face was restaurants not knowing about uncommon allergens. For example, some respondents reported they could not eat foods containing urushiol; which includes mangoes, apples, pears, fennel, ect.
A smart filter lets the user input the name of allergens or conditions into a field and detects their food allergens in a dish.
Smart Filter Benefits
Encompasses multiple food allergens.
Includes allergic conditions such as Alpha Gal Syndrome.
To take advantage of the smart filter, integrated AI into the design. I also weighed the pros and cons of implementing AI.
Pros
Greatly reduces time on task.
Synergizes very well with the smart filter.
Cross-reference the internet to spot allergens on dishes lacking information.
Cons
Subject to future laws and regulations affecting AI.
Requires constant maintenance.
Potential for high development costs.
Storyboard
There are currently no food allergen filters integrating artificial intelligence or smart filters. I drafted storyboards to demonstrate my idea.
Step 1 - Setting up your Smart Filter
Step 2 - AI Highlighting Food
I drafted a few ideas but ultimately discarded them. I ended up using artifacts of old ideas.
Token Popups
Obstructs the user interface.
Requires multiple additional clicks.
Only triggers when clicking on a dish to view more details.
Food Item Headers changing to Red/Yellow
Severe WGAC contrast issues. DoorDash's background is a very bright white and does not contrast with yellow.
Does not "scream" at the user to pay attention enough.
Flow Chart
SOLUTION
Creating a smart filter with integrated AI greatly reduces the time on task while greatly increasing the visibility of possible food allergens for users.
DESIGN
Helping Users Find Allergens
After discovering the solution, the next step was to design an interface that delivered the solution intuitively.
I used Figma to develop the wireframes.
Final Product
DELIVERY
With our ideation and wireframes now complete, it was now time to design the solution.
Prototype
I asked a few participants with food-borne allergens to order food with the allergen filter to simulate the food ordering process with a prototype.
Results
Before and After
Majority of the participants stated the burger and food icon poorly represented food allergies. I replaced it with a peanut — a common food allergen.
Participants stated the peanut represents food allergies better.
Next Steps
Designing for Colorblind
LEARNINGS AND TAKEAWAYS
Thank you for reading! This is my very first case study. Although imperfect in the process, I will continue to build off the mistakes I made in order to optimize my process as a UX Practitioner.
Insights
Too many tools. I did not need empathy maps, prioritization matrixes, ect. I had collected too much raw data that didn't need to be used to get to the final solution.
Ask YOURSELF questions. Next time I want to ask myself "Do I really need this?" to develop a solution. In the real world, it would be a waste of my company's time to perform unneeded analysis such as the aforementioned empathy maps for this product.
Takeaways
Design ONLY what's needed. I found that since I am working with an established product, there's no need to make wireframes of the homepage, restaurant selection, ect. Only design what's needed.
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