Growing loyal users by increasing perceived value on UberEats

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Challenge

Since the outbreak of the coronavirus pandemic, active users on major grocery delivery apps have increased dramatically, while food delivery apps are not experiencing a similar surge.

The chart on the left(Apptopia, 2020) shows erratic fluctuations in daily downloads, which could mean a number of things including but not limited to:

a scarcity in returning/regular users,
an inflation of session numbers resulting from customers price shopping service fees across multiple apps, and/or
an indication of the negative effects of new user promotions on user retention.

The underlying challenge is increasing value perception. Price-sensitive customers still remain skeptical and the reality is that food delivery apps might only be used in time of “dire” need if they continue to focus on short-term approaches like promotions and locking in users with subscriptions.

How can food delivery apps acquire more loyal customers in the long run?

Challenge Overview

Tools

Figma, Whimsical, InVision

Design Scope

Redesigning a portion of UberEats’ mobile app on iOS

Duration

3-Day UI Challenge

25-27 April 2020

Exploration - Understanding the Problem

(The New York Times, 2020) Link to Original Source

This accelerated shift to food delivery has also increased customers’ needs and expectations for the food ordering process. Consequently, food delivery platforms have adapted by rolling out new features, from providing contact-free delivery options to promoting local restaurants and convenience stores, but nothing has been done to increase perceived value.

Once promotions dry up, customers either go all in and sign up for subscriptions, but otherwise, they’re left with outrageous extra fees that add up to a markup of 7 percent to 91 percent more than what you would pay directly to the restaurant (The New York Times, 2020).

Exploration - Defining User “Wants”

Americans who have not tried a third-party restaurant delivery service say fast delivery (31%), restaurant selection (28%), low order minimums (27%), and first-use coupons (26%) would motivate them to try it (Upserve, 2020).

So I wondered, “What would distinguish a happy user “wanting” to order from a skeptical user “needing” to order, and how can we convert “needing” users to “wanting” users?

I imagined two different types of users, Nancy and William, on opposite ends of the spectrum.

Will represents the target user group for this case.

According to Upserve, one of the biggest determinants that create urgency in users is restaurant selection (28%), which refers to the pool of options users can choose from.

Based on the mini persona creation exercise, I made an assumption that users who “want” to order are less price-sensitive than users who “need” to order. Will would likely select a restaurant select with a specific dish or cuisine in mind, while Nancy would select one from a list that offers promotions, deals, and quickest delivery times.

Refinement - Ideation

In order to satisfy both Nancy and William’s motivations, delivery apps must provide a quick search method and personalized experience that would match their price and value perception.

In other words, users must perceive that they’re paying for and nothing more than what they want, rather than compromising for a better value.

How are users “compromising for a better value”?

Due to the nature of third-party providers, users have to pay extra to get their food. Extra fees include delivery, service, and even surge fees, that leave users no other choice but to subscribe to their services.
In addition, apps set order minimums and users end up having to add on unwanted items to their order or order multiple meals at a time to avoid paying delivery fees.

I mapped Nancy and Will’s user flow to demonstrate the two ways users are compromising for a better value. I decided to examine user flow on UberEats because I was most familiar with the particular food delivery app.

Refinement - Developing the solution

Specific to Broad Approach

After users have chosen a dish of their choice, they are often met with “points of compromise,” which give them a sense that they’ve paid more than they should.

So instead, users select 2 dishes from the beginning of their selection process, so that:

Order minimum requirements are likely to be met, and
they earn free delivery, as a corollary

Current User Experience Offered by Delivery Apps

In order to delve deeper into the specific to broad selection approach, I went through 3 popular food delivery apps to examine the selection methods they provide to their users. As a result, I realized that they seldom encourage methods that allow users to order purely from their cravings/”wants” at the moment. Instead, most consisted of aggressive recommendations based on price, delivery times, and popularity.

Real-life Testing from User’s Perspective

Then, I wanted to know how effective the “Search (Manual)” methods were when the Specific to Broad Approach was put in use in 3 major apps.

I simulated two scenarios and checked to see if the first 5 restaurants from the search results aligned with the dishes I searched for:

Scenario 1: Basil Fried Rice & Papaya Salad
Scenario 2: Pizza & Mozzarella sticks

After analyzing the competitive landscape, I noticed that search results did not align very well with users’ search queries, across all 3 apps. Postmates yielded the closest matches, but regardless, all three didn’t yield results strictly based on the keywords from the restaurant’s menu contents, leaving users confused and proving the Search (Manual) method futile.

Match scores were higher in Scenario 2 (Pizza & mozzarella sticks) than Scenario 1, which could suggest that the algorithms yield better results with popular, common search queries. However, we should take into account that the high score could have been possible because “pizza” and “mozzarella sticks” are often complementary dishes and are likely to be sold together.

Design - Applying the solution to UberEats

I decided to apply my idea to UberEats because the existing information architecture had a separate tab for Search, and this aligned with my goal to offer more Specific to Broad Selection methods for users.

Sitemap & Wireframes

I sketched the current and new sitemap, as well as lo-fi wireframes to organize my ideas.

Prototyping on Figma

I mocked up relevant pages from the existing UberEats Mobile App and created a UI Patterns Library. Then, I referred to the paper wireframes to design a new feature to be integrated in the Search Tab.