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

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<div style="background:#AE0000; line-height:0.3em; font-family:montserrat; font-size:120%; border-left:#FFE2C0 solid 15px;"><div style="border-left:#fff solid 5px; padding:15px;"><font color="#fff"><strong>'''Project Background'''
 
<div style="background:#AE0000; line-height:0.3em; font-family:montserrat; font-size:120%; border-left:#FFE2C0 solid 15px;"><div style="border-left:#fff solid 5px; padding:15px;"><font color="#fff"><strong>'''Project Background'''
 
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[[File: Abouteatigo1.png|700px|centre| upright=6]]
  
 
<div style="color:#212121;">
 
<div style="color:#212121;">
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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.  
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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. Our sponsor is very keen on making eatigo a 'habit' service, such that when people thinking of dining out, they think of booking through eatigo.
  
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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).  
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With this overarching objective, our project aims to set the initial steps in understanding the distinctive customer profiles within the entire base. This would help eatigo understand which customers they need to focus on more and the key booking patterns across all their customers.
  
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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.
 
  
 
<div style="background:#AE0000; line-height:0.3em; font-family:montserrat; font-size:120%; border-left:#FFE2C0 solid 15px;"><div style="border-left:#fff solid 5px; padding:15px;"><font color="#fff"><strong>'''Objectives'''
 
<div style="background:#AE0000; line-height:0.3em; font-family:montserrat; font-size:120%; border-left:#FFE2C0 solid 15px;"><div style="border-left:#fff solid 5px; padding:15px;"><font color="#fff"><strong>'''Objectives'''
 
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[[File: Eatigoobj.png|700px|centre| upright=6]]
  
 
Based on this background, the objective of our project is as follows:
 
Based on this background, the objective of our project is as follows:
  
 
1. Business Objective:  
 
1. Business Objective:  
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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.  
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To understand and find variations in the number of bookings and booking patterns of customers, and to identify ways of developing distinctive customer profiles based on this.  
  
 
2. Technical Objective:
 
2. Technical Objective:
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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.
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To learn and apply statistical techniques to uncover associations between various variables, so that we can use to fulfill the business objective.
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<br>   i. Understand the Data
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<br>             i. Understand the Data
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<br>   ii. Understand the typical booking journey
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<br>             ii. Identify the booking patterns across customers
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<br>  iii. Identify the influencing factors at each stage of booking
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<br>             iv. Cluster the customers based on key booking variables
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<br>   iv. Cluster the customers based on variables with the highest influence
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<br>             v. Understand interpretation of clusters
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<br>   v. Check viability of customer cluster
 
  
  
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We have have data about the following categories:
 
We have have data about the following categories:
 
# Vendor Data
 
# Vendor Data
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# Transaction Data
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# User Data
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# Redemption Data
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# Reservation Data
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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.
  
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Due to confidentiality we cannot share data records. However, a glimpse of the metadata is attached below :
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[[File: EatigoData.png|700px|centre| upright=6]]
  
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Due to confidentiality, we cannot share our data records and analysis. However, here is a glimpse into our metadata (the key variables) and a link to more comprehensive metadata :
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<br />
  
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[[File: Wiki_MainTable_Meta.png|700px|centre| upright=6]]
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<br />
  
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{| class="wikitable"
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|-
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|[[Media:Interim_MetaDataAnalysis_Group1_final.pdf|Link to Metadata1 (For EDA)]]
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|-
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|[[Media:UserClusteringSheet_Metadata_Group01.pdf|Link to Metadata2 (For Clustering)]]
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|}
  
 
'''References:'''
 
'''References:'''
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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)
 
   "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>]]
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[[https://wiki.smu.edu.sg/ANLY482/ANLY482_AY2017-18T2_Group01%3A_Project_Findings<font color="Blue">Read about our Findings! </font>]]
  
 
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Latest revision as of 18:58, 15 April 2018


Home

Team

Project Overview

Project Findings

Project Management

Documentation

Main Page


Project Background


Abouteatigo1.png

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. Our sponsor is very keen on making eatigo a 'habit' service, such that when people thinking of dining out, they think of booking through eatigo.

With this overarching objective, our project aims to set the initial steps in understanding the distinctive customer profiles within the entire base. This would help eatigo understand which customers they need to focus on more and the key booking patterns across all their customers.


Objectives
Eatigoobj.png

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

1. Business Objective: To understand and find variations in the number of bookings and booking patterns of customers, and to identify ways of developing distinctive customer profiles based on this.

2. Technical Objective: To learn and apply statistical techniques to uncover associations between various variables, so that we can use to fulfill the business objective.
i. Understand the Data
ii. Identify the booking patterns across customers
iv. Cluster the customers based on key booking variables
v. Understand interpretation of clusters


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.

EatigoData.png

Due to confidentiality, we cannot share our data records and analysis. However, here is a glimpse into our metadata (the key variables) and a link to more comprehensive metadata :

Wiki MainTable Meta.png


Link to Metadata1 (For EDA)
Link to Metadata2 (For Clustering)

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! ]


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