Ironhack Challenge 2 : Wireframing Uber Eats App

I decided to explore the Uber Eats app. It’s an incredibly popular food delivery app that is exceptionally easy to use. I wanted to understand how they have achieved this.

These are my primary reasons for working on the Uber Eats app:

· It’s a product I have used regularly since it launched. I’m a fan of how easy they have made it to get food delivered.

· It’s addictive and I wanted to know why.

· It takes a complex challenge and makes it feel simple.

· I have always been intrigued by the logistics of Uber Eats and I want to look in detail at how they make the user feel so connected to the order they have made.

· I wanted to better understand how Uber connects users to the process by allowing them to track their order from start to finish.

· One of the things that has frustrated me in the past with Uber is that once you hit Place Order — there’s no going back. You cannot change or cancel your order without calling the restaurant. I wanted to see if there is a logical reason for this.

· Finally, although the UX is easy to use, it has a large number of different design elements on the screen and I thought this will make it a good challenge for me to copy and learn from.

Task Analysis

There are three key user experience elements that Uber does exceptionally well to make ordering food with them as frictionless as possible:

· Browse hundreds of local restaurants

The apps search function has clearly been designed around an algorithm that is working in the background to recommend restaurants it thinks the user will most likely order from. My assumption is that Uber is recommending things to me based on a number of factors. These appear to be:

· My location

· My order history

· Offers

· How quickly food can be delivered to me

These factors create a hot list of restaurants and dishes that Uber thinks will prompt me to order food.

I can also easily choose to search by food categories or restaurant names and can filter results by a number of different categories (top rates, most popular, price, distance etc).

· Order from the restaurant’s menu

Wireframing Uber Eats App

Uber Eats appears to also use some form of recommendation algorithm to present users with the dishes they think will be most popular on a restaurant’s menu. It could be that these are simply set by the restaurant but without doing further research and speaking to restaurant owners I can’t know this for sure. The prompt to order ‘popular’ dishes is a strong one and is especially helpful when browsing longer menus.

I find navigating the menus the hardest part of using the application as side dishes, offers and extras don’t appear until a primary dish has been chosen. This can make price comparisons hard to evaluate. It’s also impossible to order items from multiple restaurants at the same time. Orders are lost when a user switches between restaurant and adds new items to their cart.

· Follow your order in the app

I find this feature to be what really differentiates Uber from competitors in the food delivery space. It’s incredibly accurate and also gives the user a real sense of reassurance that their order is being taken care off. Being able to track all the stages connects users to the ordering experiences and keeps them engaged with the process. I personally find that it adds an element of excitement and fun.

The current order status looks as follows:

· Order made

· Order confirmed

· Order being cooked

· Rider dispatched

· Rider waiting for pick up

· Food ready for pick up

· Rider on the way

· Rider close

· Food delivered