This project leverages a dataset from Yelp to explore the connection between user activity (reviews, tips, and check-ins) and how well a restaurant performs (measured by review count and ratings). The analysis aims to identify factors contributing to a restaurant's success and provide insights for businesses in the food industry.
For a detailed report on this analysis, check out the report here.
- Problem Statement
- Research Objectives
- Hypothesis
- Data Overview
- Analysis and Findings
- Recommendations
In the competitive world of restaurants, identifying what makes a business thrive is essential for all involved. This project explores the relationship between user engagement (reviews, tips, and check-ins) and restaurant performance (review count and ratings) to uncover insights that can help businesses succeed.
- Quantify the correlation between user engagement (reviews, tips, check-ins) and review count/average star ratings.
- Analyze the impact of sentiments on reviews count and average star ratings.
- Identify time trends in user engagement.
- Higher levels of user engagement (more reviews, tips, and check-ins) correlate with higher review count and ratings for restaurants.
- Positive sentiment expressed in reviews and tips contributes to higher overall counts for restaurants.
- Consistent engagement over time is positively associated with sustained business success for restaurants.
- The dataset is a subset of Yelp data, containing information about businesses across 8 metropolitan areas in the USA and Canada.
- The original data is shared by Yelp as JSON files, including business, review, user, tip, and check-in data.
- The JSON files are stored in databases for easy data retrieval.
- Out of 150k businesses, 35k are open restaurants.
- The analysis focuses on the distribution of business success metrics (review count and average rating).
- Higher ratings generally correspond to higher average review, check-in, and tip counts.
- Restaurants rated 4 stars exhibit the highest engagement, with a downward trend for ratings above 4.
- Few restaurants with over 50 reviews have a 5-star rating, suggesting a more balanced range of reviews for consistently popular places.
- Strong positive correlations exist between review count, tip count, and check-in count.
- Higher activity in one area tends to be associated with higher activity in others, indicating interlinked user engagement.
- Higher-rated businesses exhibit increased user engagement across reviews, tips, and check-ins.
- Maintaining high service and quality standards drives more reviews, check-ins, and tips, which are critical metrics of customer engagement and satisfaction.
- Philadelphia emerges as the top city with the highest success score, combining high ratings and active user engagement.
- Other top cities include Tampa, Indianapolis, and Tucson, suggesting thriving restaurant scenes in these areas.
- Successful businesses, particularly those with higher ratings (above 3.5), exhibit consistent or increasing user engagement over time.
- High-rated restaurants maintain steady or growing levels of user engagement, reflecting ongoing customer satisfaction.
- Higher counts of "useful," "funny," and "cool" reviews suggest greater user engagement and satisfaction, contributing to a restaurant's success.
- The busiest hours for restaurants, based on user engagement, span from 4 pm to 1 am.
- Knowing peak hours allows businesses to optimize staffing levels and resource allocation during these times.
- Utilize insights from the analysis of various metrics (user engagement, sentiment of reviews, peak hours, elite users) to make informed decisions and drive success.
- Collaborate with elite users and leverage their influence to amplify promotional efforts, increase brand awareness, and drive customer acquisition.
- Adjust operating hours or introduce special promotions to capitalize on increased demand during peak hours.
- Less successful businesses should focus on strategies to enhance user engagement over time, such as improving service quality and responding to customer feedback.
- Cities with high success scores present opportunities for restaurant chains to expand or invest further.
This project provides valuable insights into the factors contributing to restaurant success and offers data-driven recommendations for businesses in the food industry to improve their performance and customer engagement.