Difference between revisions of "Project Overview"

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[[ANLY482 AY2017-18 T2_Group_05|  
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<font color="#F5F5F5" size=2><b>HOME</b></font>]]
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| style="padding:0.3em; font-size:100%; background-color:#35383c;  border-bottom:3px solid #35383c; text-align:center; color:#828282" width="11%" | [[Project Overview |<font face = "Trebuchet MS" color="#FFFFFF" size=2><b>PROJECT OVERVIEW</b></font>]]
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[[ANLY482_AY2017-18_T2_Group_05 Project Overview|
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<font color="#F5F5F5" size=2><b>PROJECT OVERVIEW</b></font>]]
  
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| style="padding:0.3em; font-size:100%; background-color:#FFFFFF;  border-bottom:3px solid #35383c; text-align:center; color:#828282" width="11%" | [[Project Management |<font face = "Trebuchet MS" color="#000000" size=2><b>PROJECT MANAGEMENT</b></font>]]
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[[ANLY482 AY2017-18 T2 Group 05 Project Findings|
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<font color="#F5F5F5" size=2><b>PROJECT FINDINGS</b></font>]]
  
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| style="padding:0.3em; font-size:100%; background-color:#FFFFFF;  border-bottom:3px solid #35383c; text-align:center; color:#828282" width="11%" | [[Analysis | <font face = "Trebuchet MS" color="#000000" size=2><b>ANALYSIS</b></font>]]
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[[ANLY482 AY2017-18 T2 Group 05 Project Documentation|
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<font color="#F5F5F5" size=2><b>PROJECT DOCUMENTATION</b></font>]]
  
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| style="padding:0.3em; font-size:100%; background-color:#FFFFFF;  border-bottom:3px solid #35383c; text-align:center; color:#828282" width="11%" | [[Final Deliverable | <font face = "Trebuchet MS" color="#000000" size=2><b>FINAL DELIVERABLE</b></font>]]
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[[ANLY482 AY2017-18 T2 Group 05 Project Management|
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<font color="#F5F5F5" size=2><b>PROJECT MANAGEMENT</b></font>]]
<div style="background: #fee5d9; padding: 5px; font-weight: bold; line-height: 1em; text-indent: 15px; border-left: #cb181d solid 32px; font-size: 20px"><font color="#cb181d">Company Information</font></div>
 
  
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Yelp is a ratings and recommendation website for businesses to connect with their users. Their main source of revenue comes from paid advertisements from businesses. To expand their business, they wanted to monetize the analytics that comes with the vast amounts data on Yelp. With that, they opened a new department for recommending solutions to businesses using analytics. Our group aims to make use of the given dataset to derive possible actionable solutions for businesses.
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<font color="#F5F5F5" size=2><b>ANLY482 HOMEPAGE</b></font>]]
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! style="font-size:15px; text-align: center; border-top:solid #ffffff; border-bottom:solid #2e2e2e" width="150px"| [[Project Overview| <span style="color:#3d3d3d">Background</span>]]
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<div style="background: #fee5d9; padding: 5px; font-weight: bold; line-height: 1em; text-indent: 15px; border-left: #cb181d solid 32px; font-size: 20px"><font color="#cb181d">Problem Statement</font></div>
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! style="font-size:15px; text-align: center; border-top:solid #ffffff; border-bottom:solid #ffffff" width="150px"| [[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"| [[Methodology| <span style="color:#3d3d3d">Methodology</span>]]
At Yelp, we want our users to be matched with the best business, providing satisfied customers. Today, we are only able to promote the best matched business of choice to the users. However, this might not match the user’s expectations but only be the best of all existing choices. Therefore, we will use analysis in the area of text and sentiment to identify areas of improvement for businesses to be a better match to customer expectations. As customer expectations would be better met, we are hopeful that they would influence their friends to be a supporter of Yelp as well.
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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>Introduction</strong></font></div></div>==
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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.  
  
<div style="background: #fee5d9; padding: 5px; font-weight: bold; line-height: 1em; text-indent: 15px; border-left: #cb181d solid 32px; font-size: 20px"><font color="#cb181d">Primary Objective: Text and Sentiment Analysis</font></div>
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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.
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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>Business Motivations and Objectives</strong></font></div></div>==
New Businesses
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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.
  
How can new businesses leverage on insights from the reviews and tips provided by the Yelp community to identify business opportunities? How can new businesses understand the trends, tastes and preferences of the country / state / city / neighbourhood to tailor their business propositions to best fit the preferences of users?
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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. 
  
#What are the number of businesses under a unique category within a specific location? Is there a saturation of specific businesses? Is there a preference for specific operations within the area?
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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>Meetings with Project Coordinator, Project Sponsor and Team</strong></font></div></div>==
#Is there a trend for a specific type of restaurant within an area?
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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".
#Is there a lack of certain types of restaurants in the area?
 
  
Existing Businesses
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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>==
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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.
  
Based on comparison of reviews and tips among competitors in the same business category, how can businesses find out the benchmarks and extract best practices to improve their businesses?
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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.
  
#What differentiates the popular businesses versus less popular businesses? What are the attributes consumers value (i.e. Happy Hour: True) that cause a business to become popular? What are the attributes that consumers value (i.e. Alcohol: None) which cause a business to become unpopular? (Generally, popular businesses have many check-ins and minimum business rating of 4 while unpopular businesses have few check-ins and max business rating of 2)
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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.
#What are the differences between the preferred attributes for different business categories / locations?
 
  
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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: #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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{| class="wikitable"
 
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<div style="background: #fee5d9; padding: 5px; font-weight: bold; line-height: 1em; text-indent: 15px; border-left: #cb181d solid 32px; font-size: 20px"><font color="#cb181d">Secondary Objective: Data Visualisation</font></div>
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! Task !! Description 
<div>
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|-
 
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| Gather Requirements || Confirm and gather sponsor requirements
<div>
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Interactive Dashboard for Sentiment Analysis Visualization
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| Initial Research and Preparation || Conduct preliminary data exploration and define project objectives and scope
 
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Visualisation of attributes / services that people look out for in businesses for specific country, state and city and business category based on sentiments.   
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| Project Proposal || Prepare project proposal and Wikipage
#Word cloud
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#Bar Charts for Rankings
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| 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.
#Pie Charts for Polarity Percentages
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|-
#Line Charts for time series analysis of ratings and change in preferences
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| 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.
</div><br>
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|-
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| Project Revision (Mid-Term) || Assisted by RDP through obtaining feedback during our sponsor meeting.
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|-
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| Mid-term Preparation || Prepare mid-term report, presentation and Wikipage.
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| Model Validation and Refinement || Conduct independent sample t-tests (e.gEnsure the results are similar when attached to different years of study) and refine analysis of data.
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|-
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| Insights and Recommendations || Create visualisation from analysis results and formulate recommendations for our sponsor.
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|-
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| Project Revision || Assisted by sponsor through obtaining feedback during our sponsor meeting; Align our final deliverables with sponsor requirements.
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|-
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| Final Preparation || Prepare abstract and full paper, final Wikipage update and final presentation with necessary deliverables.
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Revision as of 15:22, 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.