Difference between revisions of "ANLY482 AY2017-18 T2 Group 05 Project Overview"

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! style="font-size:15px; text-align: center; border-top:solid #ffffff; border-bottom:solid #2e2e2e" width="150px"| [[Analysis of User and Merchant Dropoff for Sugar App_-_Project Overview| <span style="color:#3d3d3d">Background</span>]]
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! style="font-size:15px; text-align: center; border-top:solid #ffffff; border-bottom:solid #2e2e2e" width="150px"| [[R2Z_-_Project Overview| <span style="color:#3d3d3d">Background</span>]]
 
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! style="font-size:15px; text-align: center; border-top:solid #ffffff; border-bottom:solid #ffffff" width="150px"| [[Analysis of User and Merchant Dropoff for Sugar App_Data Source| <span style="color:#3d3d3d">Data Source</span>]]
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! style="font-size:15px; text-align: center; border-top:solid #ffffff; border-bottom:solid #ffffff" width="150px"| [[R2Z_-_Data Source| <span style="color:#3d3d3d">Data Source</span>]]
 
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! style="font-size:15px; text-align: center; border-top:solid #ffffff; border-bottom:solid #ffffff" width="150px"| [[Analysis of User and Merchant Dropoff for Sugar App_Methodology| <span style="color:#3d3d3d">Methodology</span>]]
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! style="font-size:15px; text-align: center; border-top:solid #ffffff; border-bottom:solid #ffffff" width="150px"| [[R2Z_-_Methodology| <span style="color:#3d3d3d">Methodology</span>]]
 
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<!------- Details ---->
 
<!------- Details ---->
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==<div style="background: #95A5A6; line-height: 0.3em; font-family:helvetica;  border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Introduction and Project Background</strong></font></div></div>==
+
==<div style="background: #DD597D; line-height: 0.3em; font-family:calibri;  border-left: #CFCFCF solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Introduction</strong></font></div></div>==
 +
For any entity to conduct business online, they must be able to accept payments from customers, and this involves a third-party to facilitate the online transfer of funds. Such entities are called electronic Payment Gateways , and this includes PayPal, Stripe, and our project sponsor.
  
−
As of 2016, Singapore has the highest smartphone penetration rate in the world at 88%, ahead of other countries such as Hong Kong, Australia and the U.S. [1]. Most smartphones today are able to use GPS track the user’s location, opening up opportunities for location-based marketing.  
+
When a customer makes a purchase on a merchant’s website, our sponsor helps to process the credit card payment. This is done by transferring key information between the payment portal (e.g. merchant’s website) and the merchant’s registered bank account. For each successful transaction that our sponsor processes, they apply a commission, called a Merchant Discount Rate (MDR) , from the transaction.  
  
−
Our sponsor seeks to take advantage of this by offering local deals tailored to the user’s location using a mobile app. It helps local small businesses such as cafes, restaurants, and small retail shops get discovered and market to users close in proximity. Whenever the user opens the app, they can see a list of discounted items from merchants nearby.  
+
==<div style="background: #DD597D; line-height: 0.3em; font-family:calibri;  border-left: #CFCFCF solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Business Motivations and Objectives</strong></font></div></div>==
 +
While our sponsor handles millions of transactions across the globe, the company has not fully been able to derive any conclusive analysis from its transaction data.
  
−
By definition, our sponsor exists as a platform in a two-sided market, enabling two groups of end-users to come “on board” by charging one or both sides [2]. Examples include Alibaba, eBay, Groupon, Uber and many more e-commerce companies. Like many other two-sided markets, it connects users and merchants and earns a premium for connecting these two groups by charging the transactions made between the two groups.  
+
By providing our team with their data, our sponsor hopes to gather a deeper understanding of it. The project objectives include developing meaningful insights by performing exploratory data analysis. Using the results drawn from the findings, recommendations will be formulated to aid in future business decision making.
  
−
==<div style="background: #95A5A6; line-height: 0.3em; font-family:helvetica;  border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Business Problems and Motivations</strong></font></div></div>==
+
==<div style="background: #DD597D; line-height: 0.3em; font-family:calibri;  border-left: #CFCFCF solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Meetings with Project Coordinator, Project Sponsor and Team</strong></font></div></div>==
−
As our sponsor is a relatively young startup in Singapore, it has not yet attained a critical mass of user and merchant numbers. As such, user growth and user experience is vitally important. To reach this critical mass, our sponsor needs to minimise user and merchant attrition, and retain vital segments of both groups in order to preserve the network effect of its platform. This is done by enhancing the app experience for both users and merchants.
+
Our team aims to meet our Project Coordinator, Professor Kam Tin Seong on average of at least once every week in order to ensure that we are progressing on the right track. We also met our project sponsor once a month to update them on our progress and validate our analysis, while our internal team meetings are held at least twice a week. All minutes can be found under "Project Documentation".
  
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'''Merchant Retention and Acquisition'''
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==<div style="background: #DD597D; line-height: 0.3em; font-family:calibri;  border-left: #CFCFCF solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Project Objectives</strong></font></div></div>==
 +
We aim to study the complexity of the online transactions carried out by our sponsor’s merchants. In particular, we hope to assess the performance of our sponsor’s merchants, by using number of approved transactions per merchant as a benchmark.
  
−
Redemption rate will be used as our main performance indicator for measuring merchant performance since the only channel for generating revenue is via upsell. This is the main factor of merchant attrition as merchants with little to zero uptake will perceive the application as a waste of time and choose to drop off. Since our sponsor is in a two-sided market, a high attrition rate of merchants may result in users also ceasing usage. For our sponsor, there is a strong need to retain and attract merchants who are performing well and to improve or perhaps eliminate merchants who are not.  
+
This project shows how we use funnel plots and line of best-fit graphs to compare the performance among entities in a group in an unbiased manner. Though both funnel plots and line of best-fit graphs are prevalent in medical research , they are under-utilized in e-commerce analytics. In our case study, the line of best-fit graphs provide a better model fit. By combining features of funnel plot with line of best-fit, we can identify star and laggard merchants.  
  
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'''User Retention'''
+
Moving on, we want to study the characteristics of transactions carried out by key merchants. We use logistic fit to establish any correlation between independent variables and the number of approved transactions. For independent variables that exhibit correlation, we conduct further analysis by utilizing decision trees to map expected approved-to-rejected transaction ratio for key merchants.
  
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To build a successful application, our sponsor needs to grow as fast as possible while retaining existing users. Many users have become dormant or stop buying after the first purchase. Analysis can be applied to find out why they have turned dormant/ stopped buying and identify solutions that may be able to attract them.
+
In summary, we show how exploratory and confirmatory techniques can be used as source of business intelligence - setting performance benchmarks for each merchant and improving their  approved-to-rejected transaction ratio.
  
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==<div style="background: #95A5A6; line-height: 0.3em; font-family:helvetica;  border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Project Objectives</strong></font></div></div>==
+
==<div style="background: #DD597D; line-height: 0.3em; font-family:calibri;  border-left: #CFCFCF solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Scope of Project</strong></font></div></div>==
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'''Merchants'''
+
{| class="wikitable"
−
 
+
|-
−
The objective of this paper is to show how the funnel plot may be more accurate and unbiased in comparing the performance between entities in a group. The following are some objectives we hope to achieve in our research study.
+
! Task !! Description 
−
 
+
|-
−
*Identifying over-performing merchants (star merchants) and under-performing merchants (laggard merchants)
+
| Gather Requirements || Confirm and gather sponsor requirements
−
*#Visualize and compare the performance between merchants in the same group
+
|-
−
*#Set benchmarks for individual merchants
+
| Initial Research and Preparation || Conduct preliminary data exploration and define project objectives and scope
−
*Examine underlying factors that affect redemption rate of merchants
+
|-
−
 
+
| Project Proposal || Prepare project proposal and Wikipage
−
'''Users'''
+
|-
−
* Determine the relationship between Redemption and time of day
+
| Data Exploration and Preparation || Ensuring that data is clean and can be analysed using analytical software; We have done data preparation, which include – Interactive Binning and finding the top reason code descriptions. After which, we conducted exploratory data analysis.
−
* Determine the relationship between User Growth and Revenue
+
|-
−
* Determine if the 75/25 rule holds for user i.e. 25% of the Users contribute more than 75% of the revenue
+
| Model Building || Through research findings and experience, we will attach suitable models to our data. We have used Interactive Binning, Line of Fit and Time Series Analysis to generate insights.
−
 
+
|-
−
'''Geospatial'''
+
| Project Revision (Mid-Term) || Assisted by RDP through obtaining feedback during our sponsor meeting.
−
*Determining the relationship between user location and merchant location (i.e. do users really go to places near them or are they more willing to travel to visit certain merchants?)
+
|-
−
*Determining the relationship between redemption rate and merchant location
+
| Mid-term Preparation || Prepare mid-term report, presentation and Wikipage.
−
*Identifying popular areas and unpopular areas (i.e. the proportion of merchants in an area should be proportion to the number of orders it receive)
+
|-
−
*Recommending potential locations for new merchants
+
| Model Validation and Refinement || Conduct independent sample t-tests (e.g. Ensure the results are similar when attached to different years of study) and refine analysis of data.
−
*Finding the areas that has the Users with highest spending
+
|-
−
*Develop a method to analyse cannibalisation rates
+
| Insights and Recommendations || Create visualisation from analysis results and formulate recommendations for our sponsor.
−
*Validate cannibalisation methodology using real world data
+
|-
−
 
+
| Project Revision || Assisted by sponsor through obtaining feedback during our sponsor meeting; Align our final deliverables with sponsor requirements.
−
'''Two-Sided Market'''
+
|-
−
*Detecting Network effect if present
+
| Final Preparation || Prepare abstract and full paper, final Wikipage update and final presentation with necessary deliverables.
−
*Solving the chicken-or-egg problem - i.e. which group, Users and Merchants, add more value to Revenue?
+
|}
−
 
 
−
==<div style="background: #95A5A6; line-height: 0.3em; font-family:helvetica;  border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Scope of Project</strong></font></div></div>==
 
−
The scope of our project includes the following:
 
−
 
 
−
*Data collection - Collating datasets from our sponsor's SQL database
 
−
*Data preparation - Setting and censoring the data. We will also be filtering users and merchants based on location, where we will only focus only on the Singapore region
 
−
*Analysis of merchant performance (redemption rate) using funnel plot analysis
 
−
*Analysis of the network effect in a two-sided market using time-series analysis
 
−
*Analysis of the cannibalization effect between outlets using geospatial analysis
 
−
*Refinement - Get client feedback and refine the model
 
−
*Derive recommendations and solutions for Sugar
 
−
 
 
−
==<div style="background: #95A5A6; line-height: 0.3em; font-family:helvetica;  border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Project Deliverables</strong></font></div></div>==
 
−
* Project Proposal
 
−
* Mid-term Report
 
−
* Final Report
 
−
* Project Poster
 
−
 
 
−
==<div style="background: #95A5A6; line-height: 0.3em; font-family:helvetica;  border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>References</strong></font></div></div>==
 
−
[1] The Connected Consumer Survey 2014/2015. (2015). Retrieved January 10, 2016, from https://www.consumerbarometer.com/en/graph-builder/?question=M1&filter=country:united_states,china,hong_kong_sar,korea,malaysia,singapore,australia
 
−
 
 
−
[2] Rochet, J. C., & Tirole, J. (2004). Two-sided markets: an overview (Vol. 258). IDEI working paper.
 

Latest revision as of 15:44, 15 April 2018

HOME

 

PROJECT OVERVIEW

 

PROJECT FINDINGS

 

PROJECT DOCUMENTATION

 

PROJECT MANAGEMENT

 

ANLY482 HOMEPAGE

Background Data Source Methodology

Introduction

For any entity to conduct business online, they must be able to accept payments from customers, and this involves a third-party to facilitate the online transfer of funds. Such entities are called electronic Payment Gateways , and this includes PayPal, Stripe, and our project sponsor.

When a customer makes a purchase on a merchant’s website, our sponsor helps to process the credit card payment. This is done by transferring key information between the payment portal (e.g. merchant’s website) and the merchant’s registered bank account. For each successful transaction that our sponsor processes, they apply a commission, called a Merchant Discount Rate (MDR) , from the transaction.

Business Motivations and Objectives

While our sponsor handles millions of transactions across the globe, the company has not fully been able to derive any conclusive analysis from its transaction data.

By providing our team with their data, our sponsor hopes to gather a deeper understanding of it. The project objectives include developing meaningful insights by performing exploratory data analysis. Using the results drawn from the findings, recommendations will be formulated to aid in future business decision making.

Meetings with Project Coordinator, Project Sponsor and Team

Our team aims to meet our Project Coordinator, Professor Kam Tin Seong on average of at least once every week in order to ensure that we are progressing on the right track. We also met our project sponsor once a month to update them on our progress and validate our analysis, while our internal team meetings are held at least twice a week. All minutes can be found under "Project Documentation".

Project Objectives

We aim to study the complexity of the online transactions carried out by our sponsor’s merchants. In particular, we hope to assess the performance of our sponsor’s merchants, by using number of approved transactions per merchant as a benchmark.

This project shows how we use funnel plots and line of best-fit graphs to compare the performance among entities in a group in an unbiased manner. Though both funnel plots and line of best-fit graphs are prevalent in medical research , they are under-utilized in e-commerce analytics. In our case study, the line of best-fit graphs provide a better model fit. By combining features of funnel plot with line of best-fit, we can identify star and laggard merchants.

Moving on, we want to study the characteristics of transactions carried out by key merchants. We use logistic fit to establish any correlation between independent variables and the number of approved transactions. For independent variables that exhibit correlation, we conduct further analysis by utilizing decision trees to map expected approved-to-rejected transaction ratio for key merchants.

In summary, we show how exploratory and confirmatory techniques can be used as source of business intelligence - setting performance benchmarks for each merchant and improving their approved-to-rejected transaction ratio.

Scope of Project

Task Description
Gather Requirements Confirm and gather sponsor requirements
Initial Research and Preparation Conduct preliminary data exploration and define project objectives and scope
Project Proposal Prepare project proposal and Wikipage
Data Exploration and Preparation Ensuring that data is clean and can be analysed using analytical software; We have done data preparation, which include – Interactive Binning and finding the top reason code descriptions. After which, we conducted exploratory data analysis.
Model Building Through research findings and experience, we will attach suitable models to our data. We have used Interactive Binning, Line of Fit and Time Series Analysis to generate insights.
Project Revision (Mid-Term) Assisted by RDP through obtaining feedback during our sponsor meeting.
Mid-term Preparation Prepare mid-term report, presentation and Wikipage.
Model Validation and Refinement Conduct independent sample t-tests (e.g. Ensure the results are similar when attached to different years of study) and refine analysis of data.
Insights and Recommendations Create visualisation from analysis results and formulate recommendations for our sponsor.
Project Revision Assisted by sponsor through obtaining feedback during our sponsor meeting; Align our final deliverables with sponsor requirements.
Final Preparation Prepare abstract and full paper, final Wikipage update and final presentation with necessary deliverables.
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