Difference between revisions of "ANLY482 AY2017-18T2 Group01: Project Overview"

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We are waiting on the data from the sponsor, which we will be receiving once the Non-Disclosure Agreement is signed.
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We have have data about the following categories:
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We have currently requested for the following categories of data:
 
 
# Vendor Data
 
# Vendor Data
 
# Transaction Data
 
# Transaction Data
 
# Redemption Data
 
# Redemption Data
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Some of the variables that we expect to receive in these datasets include the following:
 
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{| class="wikitable"
 
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! Vendor Data !! Transaction Data !! Redemption Data
 
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| Restaurant ID || User ID || User ID
 
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| Country || Restaurant Booked ID || Restaurant Booking ID
 
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| Restaurant Name || Country || Country
 
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| Cuisine|| Number of Diners || Number of Diners
 
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| Location (Neighbourhood) || Date & Time of Present Booking || Date & Time of Present Booking
 
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| Opening Hours|| Date & Time of First Booking || Discount Received
 
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| Peak Hour Time|| Booking Type|| Promotion Code
 
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| Planned Discounts || Booking Status || Booking Status
 
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| Maximum Capacity || Booking History || Showing up History
 
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| || Cuisine Preferences|| Cuisine Preferences
 
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|}
 
  
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Due to confidentiality we cannot share data records. However, a glimpse of the metadata is attached below :
  
 
References:
 
References:
 
   “Singapore Among Top Spenders, Asia Pacific Survey” (http://www.todayonline.com/singapore/singapore-among-top-spenders-asia-pacific-dining-survey)
 
   “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/)  
 
   “How Eatigo has Disrupted the Food Space.” (https://ecommerceiq.asia/eatigo-food-commerce/)  
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  "What's Next for the Sharing Economy" (https://www.bcg.com/en-sea/publications/2017/strategy-technology-digital-whats-next-for-sharing-economy.aspx)
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[[https://wiki.smu.edu.sg/ANLY482/ANLY482_AY2017-18_T1_Group1%3A_Project_Findings<font color="Blue">Read about our Methodology and Findings! </font>]]
 
[[https://wiki.smu.edu.sg/ANLY482/ANLY482_AY2017-18_T1_Group1%3A_Project_Findings<font color="Blue">Read about our Methodology and Findings! </font>]]
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Revision as of 20:48, 25 February 2018


Home

Team

Project Overview

Project Findings

Project Management

Documentation

Main Page


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. Transaction Data
  3. Redemption Data

Due to confidentiality we cannot share data records. However, a glimpse of the metadata is attached below :

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 Methodology and Findings! ]


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