Encounter AI | UX Portfolio

Encounter AI
Case Study

Overview

Encounter AI is a start-up created to take on customer experience in the drive-thru via voice interaction. They've developed Mai a natural language processor thats goal is to modernize the drive-thru experience for customers and employees during the fast service restaurant experience.

The objective of this project is to obtain an understanding of the drive-thru customer and drive-thru operator to improve their experiences via Mai a natural language processor with machine learning AI capabilities.

Problem Statement

Drive-thru users need a way to complete their orders more accurately and a system that is knowledgeable with menu items, because they do not want to spend additional time in the drive-thru, receive inaccurate orders, and feel rushed while ordering.

Drive-thru operators do not want to produce inaccurate orders, disappoint stakeholders with drive-thru lines, and be exposed to COVID-19 while working as an essential worker.

Role and Responsibilities

User Researcher, Discovery Research Pre-Launch

Users and Audience

75% all restaurant transactions originate within the drive-thru with over 25 million orders occurring daily in the United States.

This percentage is growing steadly due to the COVID-19 pandemic changing the rate at which customers use the drive-thru due to social distancing. As of March 2020 the use of drive-thru services has increased more than 43% due to the COVID-19 pandemic.

Users aged 18-65 years old with average income were targeted for this research. 59% of drive-thru users are women and 34% are millennials. For the purposes of my user research I interviewed more woman than men.

6 women and 4 men were interviewed. With generational ranges dispersed amongst Generation Z to baby boomers.

Scope and Restraints

The research was conducted between the months of June 2022 to August 2022.

Many restraints presented themselves, due to COVID-19 face to face user interview were not an option. Interviews with users were conducted via zoom and over the phone.

Recruiting was difficult for fast food restaurant employees, but I was able to recruit via Facebook groups for Wendys and McDonalds.

Budgeting constraints presented themselves and I used tools that had free services for analysis.

User Research Methods

1. Secondary Research: Over 15 articles were reviewed for baseline prior to conducting user research interviews.

2. Competitive Analysis: Five of the main competitors researched and compared.

3. Qualitative User Interviews: Ten (n=10) users were interviewed, six being customers of a fast service restaurant, and four drive-thru employee at fast casual restaurants.

4. Stakeholder Interview: A qualitative interview was conducted with the Chief Operations Officer of Encounter AI seeking insight into the companies goals and mission.

5. Ethnographic observational Study: Four (n=4) users were observed using the drive-thru and completed a follow-up questionnaire.

Tools: Miro, Trello, Figma, and Sketch

Competitive Analysis

Five competitors were researched for Feature, Advantage, Strengths and Weaknesses. Apprente one of the top competitors has been acquired by McDonald's and is the furthest along as far as funding and resources. Encounter AI has the ability to machine learn the customer preferences, is able to up-sale to customers, and has several hours of recorded drive-thru transactions to analyze for machine learning.

Completing a competitive analysis presented me with some challenges as many of these companies do not list much about their technology and strategies.

UX Hypothesis

a. Conversational AI will improve the overall user experience by providing a clear and concise ordering process

b. Conversational AI will boost drive thru efficiency by being able to take orders at a quicker pace

c. Conversational AI will boost basket sizes by suggesting promotions and upselling menu items

Qualitative
User Interviews

Research Goals:

Fast Food Drive-thru Operator User

a. Understand the user, who they are, their lifestyle and would they be interested in working in drive thru to order at a quick service restaurant?

b. What are the tasks that a person who works in fast service drive-thru on a regular basis?

c. What are their pain points of working at a quick service restaurant drive thru?

d.What are they frustrated with? Identify any workarounds that they may have come up with (for instance going inside the restaurant to order etc.) Are there any specific aspects of drive thru ordering that they really like?

Drive-thru Patron User

a. Understand the user, who they are, their lifestyle and would they be interested in using a drive thru to order at a quick service restaurant?

b. What are the habits of a customer who uses fast service drive thru on a regular basis?

c. What are their pain points of ordering in a quick service restaurant drive thru?

d. What are they frustrated with? Identify any workarounds that they may have come up with (for instance going inside the restaurant to order etc.) Are there any specific aspects of drive thru ordering that they really like?

Stakeholder Interview

Qualitative Interview was conducted with the Chief Operations Officer to provide insight into the product and the Mai the natural language processor for the drive-thru.

Stakeholder Questionnaire:

1. Why was Encounter AI created?

2. When talking to fast food franchise owners what is there biggest challenge? Why?

3. What is your biggest motivation for starting this company? Why?

4. How does this differ from a Siri or Alexa? Why?

5. How would you describe Mai's capabilities and how will this change the fast food industry?

5. What do you think your AI machine learning will do for the fast industry?

Ethnographic Observation

Four (n=4) drive-thru patrons observed at Starbucks and Jack in Box over the course of two weeks.

The users ordered as usual and I observed from the passenger seat. The users were then asked a few follow-up questions upon finishing the order and receiving there food.

Follow-Up Qualitative Questions:

1. How did you feel about your service while using the drive-thru? why?

2. Was your order correct, if not why do you think your order was incorrect?

3. What could of have been improved from you experience in the drive-thru?

Research Synthesis
Methodology

1. Affinity Diagram

2. Empathy Mapping

3. User Personas

Tools: Miro, Trello, and Figma,

Affinity Mapping

The user interviews were compiled with observation interview notes and then categorized based on patterns.

Eight patterns emerged once the affinity mapping began.

Preferences, Things I don't like, Alternatives, Tidbits, Because of COVID-19, Fast Food Happy Place, and Why I use the drive-thru.

Upon mapping the notes from the qualitative interviews insights were notated and empathy maps were created.

Empathy Mapping

Two empathy maps were created to empathize with the drive-thru patron and the drive-thru worker. Each map lists what the two users think, feel, says, and does. In addition the pains and gains are listed below.

Empathy Map
Drive-Thru Patron
(Jay)

Empathy Map
Drive-Thru Worker
(Cynthia)

User Scenarios

Once the empathy maps were developed, User Scenarios were created to target the ideal users in both scenarios. Which include utilizing the drive-thru, as well as working in the drive-thru.

Outcomes & Insights

During my user interviews, the following insights were noted:

1.Drive-thru users tend to enjoy the food they received from the fast food restaurant, but do like the drive-thru process at all.

2. Drive-thru users like to have specialized knowledge about menu items and details such as ingredients, portions sizes, caloric information, and other details.

3. Drive-thru users do not want to spend additional time in the drive-thru and are frustrated with long lines. They will endure because of the tasty food.

1. Drive-thru workers enjoy their jobs, but do not like the pressure exhibited to lower drive-thru times.

2. Drive-thru workers are usually working in current role because it's one of the few choices they have

3. Taking the customer's money and orders takes up the majority of the time they are in the drive-thru

Therefore, I believe that giving users an alternative experience in the drive-thru with Mai a natural language processor, it will alleviate the time waiting in the drive-thru line and provide a more efficient process over time.

We might do this by incorporating Mai into drive-thru kiosks and providing the customer with a seamless interaction. Mai can provide a history of items ordered as well as, ensure the customers order is accurate 95% of the time.

Encounter AI Case Study