FoodBack

A mobile app that provides feedback management for food truck owners.

Team: Gautam Yadav, Zoe Teoh, Junhui Yao, Ali El-Sadany

My Role: UX Researcher and Designer

Context: Interaction Design Overview Project

Methods: Think-Aloud, Storyboarding, Prototyping

Problem Space

How might we increase profits by increasing the current customer base by changing services according to reviews on food preferences (too spicy or salty) on the basis of the fixed menu provided by the customer?

The Solution

A mobile app that provides feedback data management assistance for food truck owners with the following features:

01

  • Sorting (based on rating, alphabetical, chronological)
  • Filtering (by category, ratings, by customers )

02

  • Rating distribution

  • Statistics based on the time → know whether employees do better/ worse at certain times

03

  • Being able to broadcast to customer

  • Personalized Feedback

04

We motivate customers to provide feedback by:

  • Provided a QR code on the food truck so that customers can easily access as they wait for their food or if they order online pickup/delivery they get asked to fill review

  • Provide incentives by giving them a chance to win a free meal

Process

01

Empathize

We did four contextual inquiries (Kung Fu Tea, PGH Halal Truck, Indian on Wheels, Tartan Express) to understand our target users (owners, employers, students).

After the contextual inquiries, we held an interpretation session and created an affinity diagram, to hear about the interviews and discuss what was learned, as well as to find congruence across our contextual inquiries. We were able to gather a series of meaningful insights through our affinity diagramming session. Some insights debunked hypotheses that we had previously, while others affirmed hypotheses that we had developed, and still more insights raised points that we had not considered. 

Key findings from contextual inquiries

02

Define

We created personas to create reliable and realistic representations of our key audience segments for reference. 

Persona: First-time Customer

Persona: Food Truck Owner

Persona: Regular Customer

How might we 

provide a system that enables food truck owners to better connect with customers?

03

Ideate

With personas and data from users in mind, we generated storyboards and did speed dating to verify the needs.

04

Prototype and Test

Low fidelity mobile sketches

User testings

We conducted five usability tests and in order to gain insights, we decided to write the different comments we got on post-it notes and organized them by category.

01

The determination of keywords requires a standard.

Users are confused about the choice of keywords in the top suggestions section. For example, beef sandwiches and too spicy are both keywords for top suggestions. Nevertheless, the beef sandwich is like a menu option while too spicy is more like an emotional expression. In the next iteration phase, we could work on the criteria for the selection of keywords.

02

Confusion about the number on the right side of the keyword needs test explanation.

Users are confused about the meaning of the number. They interpreted it either as a score or the people(our initial intention). We plan to add text explanations in the form of a floating display to resolve the problem.

03

Besides the function of update customers (Group feedback), individual replies should be taken into consideration.

 Individual may have extreme feedback and ratings which are worth replying by food truck owners individually otherwise it may jeopardize the reputation of the food truck.

High fidelity prototypes

Reflection

I have learned to appreciate the power of words through either verbal interviews and nonverbal observation. Abductive reasoning is widely used in the process of synthesis. When doing synthesis, confounding factors could exist in the explanation of a phenomenon. In the case of analyzing the pain points of the food truck owners and customers, cold weather constraints point out the option of delivery and order-in-advance feature.

Wireframes

 © 2020 by Junhui Yao. 

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Junhui​ Yao

Research + Design