Capstone Project

 

 

Capstone Project – Final Report

<Market Research for Restaurant in New York City>

Nayeong Lee

July 30, 2020

 

 

I.              Introduction

I.1. Background

The project has New York City as a target. New York City is the largest and planned city in the United States in the southern part of New York. It is the busiest and most famous city in the world and is known as the capital of the world as a center of politics, economy, culture, fashion, etc. Also, it is one of the most famous urban tourist destinations in the United States and is loved by many tourists.

 

I.2. Problem and Interest

If someone wants to run a restaurant in New York City, how should he plan his management? Which of New York City's residents or tourists has the better proportion of potential restaurant customers? Anyone who wants to start a new restaurant business in New York may be interested in this project.

 

I made my virtual client for this project. My client recently moved to New York City after getting married. He wants to run a new restaurant in the city to maintain his livelihood. He has just moved to the city and has spent most of his life in the eastern part of the United States, so he knows little about the tastes of people in the region.

He wants to find out the ‘type’ of restaurant that people there prefer and their taste buds. Especially, he wants to avoid being a same kind of restaurant with the other competing restaurants that are overwhelmingly well-received. He gave me his home address and asked me to do market research within a 3 km radius.

 

 

II.          Data acquisition and cleaning

 

II.1. Data sources

I only used Foursquare location data for my project. I can get information about what kind of spots is around a specific location, as well as distance, category, address, ratings and reviews of each spot.

 

II.2. Data cleaning

Scraped data included a variety of spots, including parks, bars, gyms, and merchandise stores as well as restaurants. I've only picked out restaurants from these places.

After data cleaning, there was a list of 36 restaurants left in the data.

 

 

III.      Methodology

 

III.1. Distribution of the categories

I sorted out the categories of each restaurant to see which kind of restaurants are the most distributed. Although sales at each restaurant are unknown, but I can give my client information about which foods are common and which are rare. Based on the information, he will be able to determine what kind of food he will sell.

 

 

III.2. Restaurants with good ratings

I selected only restaurants with a rating of 9/10 or higher and make a list. The list includes the categories of each restaurant. I can give this information to client to avoid competition with overwhelmingly well-rated restaurants.

 

III.3. Customer review analysis

I have set this section as a plan for my project, but since my account is free, there is a limit to the number of times I can inquire users' reviews. I wanted to pick out the 10 restaurants with the most customer reviews and make a list of only the reviews that include the 'taste', but it was impossible with my account.

So, I only loaded the number of reviews of users in this code. This will allow my client to know which restaurants are popular and popular.

 

 

IV.                Results

Foursqure location data were collected within a radius of 3km around the customer's location. As a result, I got information about a total of 36 restaurants.

  The restaurants had a total of 20 categories. The category that corresponds to the largest number of restaurants is ‘Italian restaurant’, with a total of five locations located. Next were four ‘pizza places,’ three ‘sea restaurants’ and three ‘sandwich places.’ In other categories, only one or two restaurants existed.

  I looked up the ratings of the restaurants, and they all scored over 9.0. I could see that every restaurant around had a good rating. The criteria and data that I originally designated do not correspond with each other. However, if you change the criteria to 9.5 and interpret the data, the results will change. All of their ratings had values between 9.1 and 9.4. An overwhelmingly high rating of 9.5 or higher did not present. This could be good news for my client.

 

 

V.              Discussion

There are five Italian restaurants around my client, accounting for the largest number of categories. Also, Italian restaurants are the places where the most post-visit reviews exist. My client said he wanted to avoid competition with the most popular restaurants, so he had better not plan an Italian restaurant.
 My client wants to know the taste of his local people. This may be inferred into the categories of restaurants with many post-visit reviews. The five restaurants with the highest number of reviews were Italian, French, pizza, Australian and seafood restaurants. Therefore, other Western foods are more popular in his region than American food.

 

VI.          Conclusion

My project was conducted to conduct a survey of the restaurant market in a New York City area. The limitation of my Foursquare account allowed me to get only information about 36 restaurants, but I seem to have obtained meaningful results through data analysis. I'd like to give a management recommendation based on my results to someone who wants to run a restaurant in New York like my hypothetical client.