Warm Food and Happy Customers
Adding a feature to UberEats
Intro
You can tell a lot about why a product is so popular by looking at the details in the User Experience design. This was by far the hardest challenge, simply because — it is difficult to add a new feature when a company has a superhuman team of UX designers. It took me at least three or four iterations to get to a feature that might make the cut.
Pre-Determined Goals
The challenge asked for a deliverable of a feature, I chose to give it a try with the UberEats app. The feature had to make sense in the real world and follow the design system Uber currently has.
Setting the Scope
I decided to delve deep into the user research, and find the pain points that would give me insight into where the feature could finds it usefulness. Also it was important to look at users that where on other apps, to have an idea of the behaviors that affect the industry.
I was interested in letting the research guide me to the answer.
The Actual Research and Discovery
I conducted 5 qualitative interviews in the Brickell area of Miami.
The pain points found where:
Slow Delivery Times
Sometimes food did not arrive at all
Looking at the map feature see drivers going to the restaurant pop up and disappear
Food warmth varies from warm to room temperature
The gains where: A feature that could provide picks for the fastest delivery places.
As I delved deeper into the research I began to find, the following insights:
- Driver’s don’t get paid to wait in line, and cancel picking up orders if they feel it will take a lot of their time
- Restaurants sometimes don’t begin preparing the meals until the drivers are there, other times they are unable to meet the current demand
- Driver’s cancel on the pickup restaurant based on prior experience (Surprisingly McDonald’s is a common one)
All of this leads to:
- User waiting longer than expected
- Driver taking the blame for the delays, or receiving poor tips
- An organizational behavior issue arrises as there are a lot of moving parts.
AHA Moment
The problem is definitely a wicked problem, because there are so many different ways that customer’s are not receiving warm food.
Using the Jobs To Be Done framework Helped to see the other side of the story. Not just the user but other key players in the process.
When The App User wants to eat in, They want to have it delivered as warm and fast as possible, So they can not worry about their dinner situation and focus on something else.
When the Driver wants to make money, They want to complete as many deliveries as possible, So they can double up on tips and fares.
When the Restaurant wants to make money, They want to sell as many products from their kitchen as possible, So they can increase profits and number of customers
The Solution
Introducing UberEats Top Pick.
- 47.8% more prime visual real estate for restaurants
- They can now show up on top of all other categories
- More visibility for restaurants and delivery times are much more accurate than other restaurants.
For a restaurant to enjoy this they must:
- Provide up to the minute accurate kitchen production lead times
- Be located in an optimum distance to the user, taking into consideration the rate of heat dissipation of the food (potential study in the future of heat retention practices Drivers could use)
What will this do:
- Accurate lead times means drivers waste less time waiting for meals to be prepped
- Drivers are less likely to cancel at places that are exact with the meal prep process
- Restaurants will post their lead times, in the optimum timeframe. Where overestimating the timeframe will result in unappealing delivery times for app users; underestimating the timeframe can result in driver’s canceling on the order.
This will benefit everyone:
- Drivers can deliver food thats still warm. Which hopefully means more tips.
- Users are happier with the speed and quality of the delivery
- Restaurants will compete for the Top Pick space, and the systematic solution will result in continually better service.
The User Flow
It really is going to look simpler than it really is.
It’s the first thing the app user is going to see. Hit or Miss. Thats why Big Data exists to tell us how to make this feature even more powerful.
Explaining the Design Patterns
Staying within the card size that is standard on the app, I increased the space available for an image, and created an icon that would be communicate, excellence and speed.
This card is positioned as the first image the user will interact with on the home screen.
Utilizing the Atomic Design System made it very easy to tweak the UI as usability tested showed the opportunity for improvement areas.
Hi-Fidelity Prototype
Conclusion
Keeping the presentation as brief and to the point as possible. This was not an easy project. The UX team at UberEats are incredible, stepping into their shoes for a week to find new opportunities took extraordinary effort. I wouldn’t be surprised if this feature already existed in some form. Thats what I mean by difficult.
After thought
How to integrate the Restaurant Interface to this system would be part of the next steps.
Machine Learning Algorithms applied to these lead times, can lead to “forecasted” forecasts which could result in formulas for restaurant capacity and production needs to be more predictable. In fact UberEats could supply the restaurants with a forecast model, where these kitchens can staff In advance to meet peak times.
Should the driver place the meal in the front seat, where the AC vents can increase the heat dissipation, or the backseat? Are there certain containers that provide better insulation? Whats the optimum distance for food to arrive warm?
These are questions to ponder as they make up the users experience with the service, the app, the drivers and the restaurant partners.
At the end of the day people just want to eat warm food, and be happy!
