Project Overview

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PROJECT OVERVIEW

PROJECT MANAGEMENT

ANALYSIS

FINAL DELIVERABLE

Company Information

Yelp is a ratings and recommendation website for businesses to connect with their users. Their main source of revenue comes from paid advertisements from businesses. To expand their business, they wanted to monetize the analytics that comes with the vast amounts data on Yelp. With that, they opened a new department for recommending solutions to businesses using analytics. Our group aims to make use of the given dataset to derive possible actionable solutions for businesses.

Problem Statement

At Yelp, we want our users to be matched with the best business, providing satisfied customers. Today, we are only able to promote the best matched business of choice to the users. However, this might not match the user’s expectations but only be the best of all existing choices. Therefore, we will use analysis in the area of text and sentiment to identify areas of improvement for businesses to be a better match to customer expectations. As customer expectations would be better met, we are hopeful that they would influence their friends to be a supporter of Yelp as well.

Primary Objective: Text and Sentiment Analysis

1. How can existing businesses leverage on the reviews and tips provided by the Yelp community to improve existing attributes of their business (e.g. good service, but can improve in terms of pricing)
2. Based on comparison to reviews and tips of competitors within the same business category, how can businesses better suit the preferences and tastes of users?
   1. What is the distinction between popular businesses versus less popular businesses? What are the attributes value (i.e. Happy Hour: True) that cause a business to become popular? What are the attributes value (i.e. Alcohol: None) that cause a business to become unpopular? (Generally, popular businesses have many check-ins and minimum business rating of 4 while unpopular businesses have few check-ins and max business rating of 2
   2. What are the differences between the preferred attributes for different business categories / locations?

Secondary Objective: Data Visualisation

Tertiary Objective: User Clustering