Product Case Study

J. Drew Smith
Jul 19, 2018 · Unlisted

Welcome to my case study! As a self-proclaimed foodie, I immediately connected with the wait staff prompt. I hope you enjoy the walkthrough of my design process and my approach to the wait staff review problem.


Initial Assumptions

Upon reading the prompt, I started thinking about my experience with restaurant servers. I imagined how good it would feel to finally express my frustration with that brunch place that always hurries us to leave or that steakhouse where the server was never around and we were forced to flag other staff for assistance. But I’m already making assumptions about how to interpret this prompt. So let’s take a step back (or maybe ten).


By identifying and analyzing the key concepts in the prompt I uncovered some of the complexity I would have otherwise overlooked.


There is a huge variety of restaurants! Bars, coffee shops, cafes, food trucks, catering, fast food, casual dining, and formal dining could all be considered restaurants. Each of them with a different type of service provided.


The server’s role also varies significantly depending on the type of restaurant. Bartenders, assembly line server, and baristas all prepare food in addition to serving the dines. Table-side servers, on the other hand, are focused on the diner’s experience.


Though they all come for food, diners eat out for different reasons and with different people. They could be celebrating an event, spending time with friends, spending time with a significant other, or doing business.


Restaurant service is much broader than the behavior of the server. Even if I focus on the server’s behavior, what do diners care about? Servers provide diners with many things from advice and assistance to entertainment.

Research Survey

At this point it was clear that I needed more information to understand diners’ mental model for restaurant service. I created a survey with the goal of learning the answers to questions like these:

  • What does “good service” mean to an eater today?
  • What expectations do diners have for the service at different types of restaurants?
  • When is service a priority for diners when picking a place to eat?


To create the visualizations for each of these questions I counted each vote for service as a factor as a positive and each vote against service as a factor as a negative vote.

In the 5 restaurants types that scored lowest, the server is also frequently preparing your food. I wondered if this made the distinction between food and service more difficult for Diners to recognize.
People generally saw service as a much more important factor when thinking about an occasion. But even among occasions birthdays, anniversaries, dates, and professional meals stood out.
The importance of server traits proved much less conclusive. Knowledge and proactivity were generally rated highly but people held mixed positions for each of the other traits.
In addition to the quantitative questions, some people responded with their qualitative experiences. Reading through them I was impressed by how many ways a restaurant experience could stand out for good or bad.

Scope & Constraints

Based on the results of the survey, I decided to focus on diners who are eating out for a special occasion, or on a date at a casual sit-down or fine dining restaurant. Each of these scored very highly in term of service as a deciding factor.

I speculated that Professional Meals and Catered Events would have distinct needs. I decided to not focus on them for this case study.

User Interview — Server

Vlad recently started working as a server at a casual sit-down restaurant on the Upper West Side in New York City. He has previous experience working at similar restaurants and fine dining restaurants.

  • Thinks the most important thing when looking for a job is getting a referral.
  • Thinks core skills are roughly the same in casual and fine dining but diners have different expectations from fine dining.
  • Enjoys casual restaurants because they are more social.
  • Gauges diner’s satisfaction through tip amount.
  • Hesitant about public reviews because his reputation gets him his job.

Key Insight

Servers rely on their reputation to get and keep jobs. Internet trolls could ruin their chances for future employment.

Server’s Problem

There’s currently no way for Servers to prove that diners love their service so restaurants only consider job seekers on a referral basis. They are limited by their personal connections.

User Interview — Diner

Julia eats out frequently with friends/family, and is currently planning an anniversary dinner with her significant other.

  • Thinks about service in terms of restaurants not servers.
  • Prefers it when the server doesn’t interrupt too much, especially on a special occasion.
  • If out with friends, is okay with more conversational server.
  • Loves it when Servers bring surprise dishes to recognize the occasion.
  • Feels awkward giving critical feedback in person even if manager asks.
  • If service is bad, makes a mental note not to come back.

Key Insight

Diners prefer different types of service depending on the occasion, who they are with, and their own personality. There is no one type of “good service.”

Diner’s Problem

Diners have no way of knowing what type of service to expect from different restaurants.


“Praise in public, criticize in private” — Vince Lombardi

My Hypothesis

If I design a way for diners, like Julia, to leave nuanced ratings and reviews of their server at restaurants they visit, then Servers, like Vlad, can use their personal profile of reviews and ratings to get noticed at restaurants where they don’t have a personal connection.

Comparative Research

Before diving in, I looked for analogous experiences that were well-executed. This allowed me to notice the considerations they made and anticipate similar ones for my goal.

Rate My Professor

  • Gives insight at the individual level (Professor) and collective level (School/University).
  • Allows for ratings across a spectrum of traits.
  • Simple review process for users with optional longer responses.

LinkedIn for Recruiters

  • Allows businesses to connect professionals without a previous connection.
  • Gives professionals a platform to display their skills, experience, and accomplishments.
  • Allows professionals to curate parts of their profile, but also allows for democratic voting on traits that professional don’t control.


How do you make it easy for a diner to leave a nuanced restaurant service review? How can the user quickly navigate to a specific restaurant and server? How will servers curate their profiles? I started exploring these interface questions with some quick sketches.

I focussed on a mobile layout because for a diner to leave a review during a meal or at a restaurant they will almost certainly be using a mobile device.


I switched to the computer once I had a broad sense of the different views I would need. Going back to the user interviews notes, survey responses, and inspirational products helped remind me of different considerations to make to refine the wireframes.

This is also where I started to consider platform and design patterns. I chose Android and Material Design patterns because a native experience would give the user access to tools like their phone, GPS directions, and notifications.

My Vision

People deserve great service when they eat out. But that means different things to different people.

To solve this problem I envision an application where diners can find and review restaurants using a wide range of traits or qualities they care about. While at the same time matching servers to restaurants where they can provide the experience diners are looking for. I illustrated four scenarios to show the potential of this vision:

  • A Diner looks for a restaurant with polite, and professional service for her anniversary dinner.
  • After her dinner the Diner rates and reviews her server and gives them a suggestion on how to make their service even better.
  • Server is sees Diner’s comment, features it on their profile, and flags a suggestion for inappropriate content.
  • Server receives an offer to interview from restaurant that has seen the Server’s profile. They consider the restaurant’s own feedback and accept.

Scenario Walkthroughs

For each scenario I created a workflow to help me think through the potential variations. You’ll see those workflows alongside a walkthrough video of my designs prototype for each scenario.

Scenario #1: Diner Finding a Restaurant

A Diner looks for a restaurant with polite, and professional service for her anniversary dinner.

In this scenario you see the Diner take advantage of this when they use the phone to make a reservation after deciding on a restaurant.

Scenario #2: Diner Leaves a Review

After her dinner the Diner rates and reviews her Server and gives them a suggestion on how to make their service even better.

All servers are rated on traits that had a value consensus in the survey. Traits with more subjective or situational value are optional and can be used to describe some of the unique qualities of a server.

Scenario #3: Server Receives a Review

Server sees Diner’s review, features it on their profile, and flags another review for inappropriate language.

Giving Servers the ability to flag inappropriate Testimonials and Suggestions should help address Vlad’s concern that internet trolls would harm his employment prospects.

Scenario #4: Server Receives an Interview Request

Server receives an offer to interview from restaurant that has seen the Server’s profile. They consider the restaurant’s own feedback and accept.

Having the data to prove his effectiveness should make Vlad’s job search easier. But why should Vlad have to search for a job at all? In this scenario you see my vision for a future where restaurants looking for servers like Vlad reach out to him.


My Experience Using the App

Thinking of myself as the user, I really like the idea that I could get a detailed breakdown of the service provided at a restaurant. This is something that I prioritize when planning to eat out. That said, without relevant information about food quality and ambiance of a restaurant I would have trouble making a final decision.

What would I change?

I would show how these ratings could be surfaced in other contexts that I believe are a more natural starting place for people deciding where to eat out (Yelp, Google Maps, etc.).

What would I do next?

A problem I hadn’t considered yet is the problem of fake reviews. I would like to explore ways to verify diners. Fake review are a big problem in this space. “[On] Yelp as many as 25 percent of reviews submitted to the website are fake.” I think there are ideas like scanning receipts that could make leaving a review easier for the Diner as well.

I would also like to interview some restaurant managers to find out about their recruiting/hiring process. That would help me understand how this service would ideally integrate into their process.

Lessons Learned

This project reinforced the complexity that exists even behind seemingly commonplace decisions. I enjoyed it not only because I find the experience of eating at restaurants interesting, but also because it challenged me to separate my personal views and focus on the users. Thank you for the excuse to explore this topic (and try some new restaurants in the process)!


J. Drew Smith

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