ANLY482 AY2017-18T2 Group01: Project Overview

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Project Background

Our project sponsor, eatigo, currently has 2 million registered users. However, reservation and reactivation rates remain low as only 30% of eatigo's subscribers have ever made a reservation. Further, even amongst this 30%, 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.

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).

Through our project, aim to improve reservation rates amongst subscribers who have never made a booking and reactivation rates amongst subscribers who have made a fewer number of bookings per month. We plan to do this by analysing transaction, redemption and vendor data to reveal booking patterns across customer & vendor types.

Objectives

Based on this background, the objective of our project is as follows:

1. Business Objective: To find the booking preferences and key influencing factors that result in a higher number of bookings and then cluster customers based on this to increase number of repeat bookings per customer.

2. Technical Objective: To learn and apply statistical tools and data visualization techniques to uncover associations between various variables, and obtain other insights that we can use to fulfill the business objective.
i. Understand the Data
ii. Understand the typical booking journey
iii. Identify the influencing factors at each stage of booking
iv. Cluster the customers based on variables with the highest influence
v. Check viability of customer cluster

Data Sources

We have have data about the following categories:

1. Vendor Data
2. User Data
3. Reservation Data

We combined these tables into a consolidated sheet and had 688795 records of raw data. After cleaning, we have 684920 records. We also made a User Sheet and Vendor Sheet for specific clustering analysis.

Due to confidentiality we cannot share data records. However, here is a link to our metadata :

Link to Metadata

References:

 “Singapore Among Top Spenders, Asia Pacific Survey” (http://www.todayonline.com/singapore/singapore-among-top-spenders-asia-pacific-dining-survey)
 “How Eatigo has Disrupted the Food Space.” (https://ecommerceiq.asia/eatigo-food-commerce/) 
 "What's Next for the Sharing Economy" (https://www.bcg.com/en-sea/publications/2017/strategy-technology-digital-whats-next-for-sharing-economy.aspx)

[Read about our Findings! ]