EatWith.com Analysis
Presented by -- Bhavana Rangaraj
Yiyi ChenDamma D. Dixon
Jing Fan
Introduction
• Founded in 2012
• U.S based start up company
• Airbnb for Home-Cooked Meals
• It works simple as 1.2.3!
Problem Statement
• Who is more popular?
• Which cuisine is more popular? • How we define our own popularity formula?
• How could we improve EatWith.com?
Data Characteristics
• Data Set - 307 records
• Host Data
• Event Data
• Data Types: Nominal, Ordinal and Ratio – No Interval Data
• Free Form Data
Data Characteristics• Variables selected influence popularity directly and indirectly.
• Popularity = (# of comments/ # of guests)*(rating/ max. rating)
Data Collection & Processing
• Manual Data Collection
• Manual Massaging of Data
• Tools: Spreadsheet, SPSS and Content Analyzer
Content Analysis123456789
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Regression Analysis Analyzing the influence of independent variables on dependent variable.
3 different regressions were conducted
Based on Cuisine
Based On Formula Based on Meal Type
Regression Analysis
Regression Analysis
Recommendation
• Results of analysis are somewhat biased
• EatWith hosted events
• EatWith award for best host
• Networking for Chefs
• Encouraging hosts to post short videos of events
Thank You