ANLY482 AY2017-18 T1 Group1: Project Overview

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Introduction

Our project sponsor, eatigo, currently has 2 million registered users. Out of this, only 30% have ever made a reservation through Eatigo. Even amongst this 30%, reservation and reactivation rates remain low as each customer makes only 1.6 bookings on average per month. Our sponsor believes that there is higher potential, given their presence in markets such as Singapore and Thailand, both of which are amongst the top 3 spenders in South-East Asia when it comes to dining out.

Motivation

Eatigo’s business model is such that it earns revenue when customers show up for their booking. For every customer that does show up, Eatigo receives a certain fee from the vendors. Therefore, it is crucial for eatigo to have a good vendor listing (so customers are interested in making their booking through eatigo) and ensure that customers are aware of these vendor listings and discounts available (so customers are incentivized to make their booking through eatigo). To do this, our sponsor would like us to develop intelligence about customer dining patterns by utilizing Eatigo’s transaction, redemption and vendor data. Eatigo can leverage on this intelligence to not only optimize yield management but also attract new vendors.

Objectives

As described, the overall objective of our project is to help eatigo increase number of bookings per customer and potentially attract more vendors. With this in mind, at this stage, our aim is to conduct an Exploratory Data Analysis (EDA) to understand the vendor choice, booking and redemption patterns of eatigo customers across its 6 regions, and 700 restaurants.

From our EDA, we would like to answer the following questions from our datasets :
1. Analysis of Vendor Data

• Variation of booking patterns across cuisines
• Variation of booking patterns across neighbourhoods
• Variation of peak and downtime across restaurants
• Understanding the potential of optimizing discounts provided by restaurants within the same neighbourhood

2. Analysis of Customer (Transaction & Redemption) Data:

• Variations of booking patterns across customers that have made bookings previously
• The average gap between the time booking is made and the time customers have to show up at the restaurant
• The customer clusters by restaurant type, location, timing and discount preferences

The current objectives may be subjected to further changes after we have obtained and look at the actual data.

References:

 “Singapore Among Top Spenders, Asia Pacific Survey” (http://www.todayonline.com/singapore/singapore-among-top-spenders-asia-pacific-dining-survey)

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