PROJECT WORK: Milestone 3 (First Draft) – “One-Pager” and Prototype Progress

Plates | PROJECT WORK: Milestone 3 (Final Draft) – “One-Pager” and Prototype Progress (Show to TA)

Questions:

1. One-line, solution-agnostic description/title.
2. Describe the opportunity in a solution agnostic way, such that more options could be brainstormed.
3. Describe, briefly, the potential benefits in the customer’s words.
4. Summarize a mock customer case study involving this work (with quantifiable impacts).
5. Top three areas of uncertainty, ranked by value of reducing uncertainty.
6. Current plan to explore those areas of uncertainty (link).
7. Summarize prior research related to this opportunity. What do we know?
8. Leading signals we might observe if this is working (and not working).
9. What other options did you consider (including benefits) (link)?
10. Why now, compared to alternatives? Really, truly, why now? Cost of delay (link)?
11. Brief summary of technologies involved (if known). Detail any areas of specialization required.
12. Rough time frame (e.g. 1-3w, 1-3m, 1-3q, link).

Answers:

1. By connecting people through affordable, convenient, and enjoyable meals, we hope to be a user’s bridge between a good meal and good company.
   1. Description: bridging good meals and good company
2. While many food delivery options exist, challenges lie in making food options more affordable and convenient, while also enabling the nuture of genuine social interactions. For college undergraduate and graduate students who don’t cook, don’t have time or a car to get off-campus food, are money-conscious, or are seeking new relationships over a shared meal, opportunities that connect people through affordable, convenient, and enjoyable meals offer promising opportunities to explore.
3. Potential benefits from Plates, from the customers’ point of view:
   1. As a 1st-year international graduate student, I feel relieved to find Plates as a way to meet other people on campus and potentially make some friends, especially since I sometimes feel lonely being on campus in another country.
   2. As a money-conscious college student, I am excited to find that Plates offers food at prices comparable to those found by directly ordering in restaurant (and is cheaper than apps like DoorDash).
   3. As a college student who is concerned about feeling uncomfortable or unsafe in a 1-on-1 match, I feel reassured that Plates sets up matches between 4-6 people, as a group setting would feel safer and potentially lessen awkward silences and interactions. 
   4. As a college student who had previously tried 1-on-1 friend-meeting apps like Bumble BFF, I am hopeful for the Plates group match to potentially meet more people and build more connections.
   5. As a junior who spent much of the start of my college life online, I feel glad that Plates offers a platform to get to know others, as I felt it was challenging to find friends so far.
4. Mock Case Study: 
   1. It’s midterms week in college and May wants to treat herself to non-dining hall food as a study break. As a money-savvy college student, May has never used Plates but decides to order from them for the first time for convenience and new social connections. She feels that she has been spending too much time alone in her room studying and writing her essays, and would like to treat herself a bit and socialize, but her friends are busy. May opens her newly-downloaded app and sees that the restaurant of the day is Urban Momo—she’s excited since she’d been wanting to try it out, but the restaurant is usually too far for her to access as she doesn’t have a car, and the food is usually more pricey on apps like DoorDash. After creating her account, May adds her interests and hobbies to her profile. She browses the menu and finds what she wants to order. Right as she’s about to check out, she is surprised the price is way cheaper than what she saw on Doordash; the price is comparable to those from direct orders at the restaurant. After making the order, she’s then automatically matched with four new individuals, based on their shared interests and grade level. 
   2. Quantifiable Impacts: 
      1. Social
         1. May goes on a plate with 4 other people who matched her interests and year. They are all Freshmen, but have never met each other before because they live in different dorms across campus. 
      2. Price
         1. Her tofu bowl was originally $28 on Doordash, but her final total was $14.95 — almost half the entree’s price. The bulk order from the same restaurants as her plate matches made the overall price cheaper.
5. Top 3 Uncertainties (ranked by value of reducing uncertainty)
   1. The implementation of driver and restaurant interfaces in the first iteration of the Plates application.
   2. The challenge of growing a user base when the majority of students are used to ordering on UberEats or DoorDash.
   3. The accommodation for varying eating schedules since Plates only offers delivery services between certain time frames. 
6. Plan to explore uncertainty
   1. We can potentially focus only on the student user prototype and hone in on the technical aspects of our matching algorithm.
   2. We can publicize through popular media platforms such as Fizz, Instagram, and GroupMe.
   3. We can create a wider range of time slots for food delivery so that students can eat delicious food when they most want it. 
7. Prior research related to this opportunity:
   1. Many food delivery companies are more individual-focused, in which customers place an order for only themselves
   2. Some companies (e.g. Doordash) allow for group orders, which brings in a relatively more community-oriented aspect to the ordering process compared to others
   3. Most food delivery platforms, such as Uber Eats and DoorDash, are expensive and include many additional fees (e.g. delivery fee)
   4. Stanford Dining provides a convenient way to eat food with others, but is not necessarily cheap
   5. Measuring Affordability
   6. DashPass – $4.99/month, 11% service fee (more affordable)x
   7. EatsPass – $9.99/month, 15% service fee
   8. Measuring company’s social emphasis involves multiple people eating together; low-cost, low-effort to socialize
8. Leading signals that we have observed so far are demonstrated by our experience prototypes: through different survey results, users have indicated that they plan dinner ahead by 1.5-2 hours, and they would be willing to try out Plates as a way to meet new people. However, we acknowledge that these are weak signals and what users say they want may not be reflective of their interactions with the product. Therefore, leading signals that indicate whether or not our product is effective after deployment would include application retention rate, the number of orders placed over time, and the rate of feature usage (ie. food ordering). 
9. Options that we were considering:
   1. One on one vs group matching: After speaking with Christina during the science fair last week, we realized that group matching is the best option because this not only ensures safety for all parties involved, but also reduces the possibility of flaking. If we were to create one on one matching, then the safety risks are greater as it would not be in a group setting. Moreover, with one on one matching, if one person were to cancel or not show up, then the entire match would not work.
   2. Pre-ordering vs day of ordering: As we’re aiming for an MVP by the end of the quarter, we decided to prioritize the day of ordering but hope to implement pre-ordering in the future!
   3. Multiple delivery times a day vs one delivery time a day: Logistically, we tried to choose the path of least resistance by minimizing the amount of drivers and workers needed for the food delivery. Thus, we chose one delivery time a day!
   4. Completely anonymous matching vs profile and interests matching: After conducting several interviews, we concluded that users would prefer to know some information about who they would be matched with. This way, all individuals involved in the match know that they have common interests.
10. Our closest competitor is Foodie.Earth, a virtual food truck in the bay area. Other competitors include UberEats and DoorDash, which provides online order and food delivery. How Plates distinguishes itself from our competitors is that we emphasize the social aspect of friend matching– in which we connect university students over group meals– and affordability, as we offer meals at restaurant prices or lower through bulk ordering. We think that since the pandemic, people have become more accustomed to ordering food online and having it delivered, as well as becoming more eager to meet new people (since not much social interaction occurred over the pandemic). Therefore, we think that Plates is timely answering a demand in the market.
11. Plates matching algorithm, App frontend and backend are our technical focus.
    Plates will incorporate Collaborative Filtering (CF) to match users in groups of 5 based on their individual demographics and interests. CF is believed to be a suitable underlying technique for recommender systems on social networks, because CF gathers tastes of similar users. If a user includes a specific interest tag, it will produce a number 1 or 0,  where the user either shares that interest or they don’t. A matrix will be generated, and the users that share the similar/same interests will be positioned on the matrix in close proximity. the system will return the best match for each user. We will create our app with ReactNative, a Javascript based framework to write Android and iOS apps. We created our look-and-feel prototype on Figma and will implement the user interface in ReactNative. As a food-order and user-matching platform, plates need to store food order data and user profiles. Plates will use Firebase as the database and backend.
12.  Our 1-3w time frame:
    1. 0-1w: 
       1. Finish look-and-feel in Figma
       2. Finalize user flow
       3. Create a functional CF (friend matching algorithm) code 
    2. 1-2w:
       1. Create clickable prototype
    3. 2-3w:
       1. Final presentation