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