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'''
 
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[[File: Abouteatigo1.png|700px|centre| upright=6]]
  
 
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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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As described, the overall objective of our project is to help eatigo increase number of bookings per customer. 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 8 regions, and 700 restaurants.
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[[File: Eatigoobj.png|700px|centre| upright=6]]
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Based on this background, the objective of our project is as follows:
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1. Business Objective:
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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.  
  
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From our EDA, we would like to answer the following questions from our datasets :<br />
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2. Technical Objective:
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1. Analysis of Vendor Data
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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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* Variation of booking patterns across cuisines
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<br>             i. Understand the Data
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*Variation of booking patterns across neighbourhoods
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<br>              ii. Identify the booking patterns across customers
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*Variation of peak and downtime across restaurants
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<br>              iv. Cluster the customers based on key booking variables
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*Understanding the potential of optimizing discounts provided by restaurants within the same neighbourhood
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<br>              v. Understand interpretation of clusters
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2. Analysis of Customer (Transaction & Redemption) Data:
 
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* Variations of booking patterns across customers that have made bookings previously
 
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* The average gap between the time booking is made and the time customers have to show up at the restaurant
 
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* The customer clusters by restaurant type, location, timing and discount preferences
 
  
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The current objectives may be subject to further changes after we have obtained the actual data.
 
  
 
<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>'''Data Sources'''
 
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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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# Vendor Data
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# User Data
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# Reservation Data
  
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We have currently requested for the following categories of 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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# Vendor Data
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# Transaction Data
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[[File: EatigoData.png|700px|centre| upright=6]]
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# 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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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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{| class="wikitable"
 
|-
 
|-
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! Vendor Data !! Transaction Data !! Redemption Data
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|[[Media:Interim_MetaDataAnalysis_Group1_final.pdf|Link to Metadata1 (For EDA)]]
 
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|-
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| Restaurant ID || User ID || User ID
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|[[Media:UserClusteringSheet_Metadata_Group01.pdf|Link to Metadata2 (For Clustering)]]
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|-
 
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| Country || Restaurant Booked ID || Restaurant Booking ID
 
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|-
 
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| Restaurant Name || Country || Country
 
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|-
 
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| Cuisine|| Number of Diners || Number of Diners
 
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|-
 
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| Location (Neighbourhood) || Date & Time of Present Booking || Date & Time of Present Booking
 
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|-
 
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| Opening Hours|| Date & Time of First Booking || Discount Received
 
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|-
 
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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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'''References:'''
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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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[[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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  "What's Next for the Sharing Economy" (https://www.bcg.com/en-sea/publications/2017/strategy-technology-digital-whats-next-for-sharing-economy.aspx)
 +
 
 +
[[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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