Difference between revisions of "AY1718 T2 Group21 Midterm Findings"
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| − | + | [[ANLY482 AY2017-18 Term 2 | <font color="#FFFFFF">BACK TO PROJECTS</font>]] | |
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<div style="background: #688E26; line-height: 0.3em; font-family:Century Gothic; border-left: #FAA613 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#000000"><strong>Executive Summary</strong></font></div></div> | <div style="background: #688E26; line-height: 0.3em; font-family:Century Gothic; border-left: #FAA613 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#000000"><strong>Executive Summary</strong></font></div></div> | ||
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| + | <strong>Problem Summary:</strong> | ||
| + | Brainsmith, an e-commerce company that sells children educational products has been operating for over two years but their website conversion rates have been lower than industry average. Using customer behaviour patterns and purchase data - we hope to help identify website traffic patterns in order to identify possible methods to help the company increase their conversion rates | ||
| + | |||
| + | <br> | ||
| + | |||
| + | <strong>Definitions:</strong> | ||
| + | <p style="margin-left: 40px"> | ||
| + | * User: Every person who has every accessed the site | ||
| + | * Customer: A website user that has made at least 1 purchases | ||
| + | * User: A website user that has not yet made any purchases</p> | ||
| + | |||
| + | <br> | ||
| + | |||
| + | <strong>Conversion Rates:</strong> | ||
| + | <br>We segmented conversion into two approaches: | ||
| + | <p style="margin-left: 40px">1. Customer Retention | ||
| + | With customer behaviour data and information such as website pages clicked before purchasing and number of user sessions before purchase- we hope to identify factors that correlate with | ||
| + | <br> | ||
| + | 2. Customer Acquisition | ||
| + | With information on both Customers and Users, we hope to find correlations between the two sets of data. | ||
| + | </p> | ||
| + | |||
<br/> | <br/> | ||
<div style="background: #688E26; line-height: 0.3em; font-family:Century Gothic; border-left: #FAA613 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#000000"><strong>Data Processing</strong></font></div></div> | <div style="background: #688E26; line-height: 0.3em; font-family:Century Gothic; border-left: #FAA613 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#000000"><strong>Data Processing</strong></font></div></div> | ||
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<br> | <br> | ||
| + | <strong>Data Cleaning:</strong> | ||
| + | <br>The data cleaning process was two fold: | ||
| + | <p style="margin-left: 40px">1. Rechecking for human error: Matching of all corresponding web behaviour with the customers - pages visited and actions taken on the website, since the variables and data set were defined through human web-crawling and manual entry | ||
| + | <br>2. Adapting and creating some sub-data files: This was done for ease of access to load onto R and briefly for Tableau and to de-aggregate our data, keep it succinct, useful and effective | ||
| + | We recoded columns in our data, using R, as per our statistics analysis required. | ||
| + | </p> | ||
| + | <br> | ||
| + | Using preliminary visualisations, We clean these observation this out of our analysis, so as to avoid bias and skew. | ||
| + | |||
<br/> | <br/> | ||
| − | <div style="background: #688E26; line-height: 0.3em; font-family:Century Gothic; border-left: #FAA613 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#000000"><strong>Visualisation</strong></font></div></div> | + | <div style="background: #688E26; line-height: 0.3em; font-family:Century Gothic; border-left: #FAA613 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#000000"><strong>Exploration and Visualisation</strong></font></div></div> |
| − | + | ||
| + | <strong>Data Exploration Methodology: </strong> | ||
| + | <br>Most of our exploratory research and insight derivation has been through a trial basis by loading our relevant data onto Tableau. | ||
| + | We looked at scatter plots, box plots, histograms and bar charts with varying degrees of complexity depending on the number of variables involved and made sense from a business perspective. | ||
| + | Keeping in mind our business objectives, and the emphasis laid on different factors by our client, we focused our attention on certain key variable that we are going to be discussing. | ||
| + | |||
| + | <br>Initially, when analysing basic level data variables, for example the Average Session Duration on users on the website, as well as the Total No. of Page Views per customer, we found anomalies in terms of outliers, like these ones. These could be the founders and managers of the company in-charge of the website themselves, or teams like us, working in tandem projects with them. | ||
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| + | |||
<br> | <br> | ||
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<br> | <br> | ||
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Revision as of 22:33, 25 February 2018
Problem Summary:
Brainsmith, an e-commerce company that sells children educational products has been operating for over two years but their website conversion rates have been lower than industry average. Using customer behaviour patterns and purchase data - we hope to help identify website traffic patterns in order to identify possible methods to help the company increase their conversion rates
Definitions:
- User: Every person who has every accessed the site
- Customer: A website user that has made at least 1 purchases
- User: A website user that has not yet made any purchases
Conversion Rates:
We segmented conversion into two approaches:
1. Customer Retention
With customer behaviour data and information such as website pages clicked before purchasing and number of user sessions before purchase- we hope to identify factors that correlate with
2. Customer Acquisition
With information on both Customers and Users, we hope to find correlations between the two sets of data.
Data Cleaning:
The data cleaning process was two fold:
1. Rechecking for human error: Matching of all corresponding web behaviour with the customers - pages visited and actions taken on the website, since the variables and data set were defined through human web-crawling and manual entry
2. Adapting and creating some sub-data files: This was done for ease of access to load onto R and briefly for Tableau and to de-aggregate our data, keep it succinct, useful and effective
We recoded columns in our data, using R, as per our statistics analysis required.
Using preliminary visualisations, We clean these observation this out of our analysis, so as to avoid bias and skew.
Data Exploration Methodology:
Most of our exploratory research and insight derivation has been through a trial basis by loading our relevant data onto Tableau.
We looked at scatter plots, box plots, histograms and bar charts with varying degrees of complexity depending on the number of variables involved and made sense from a business perspective.
Keeping in mind our business objectives, and the emphasis laid on different factors by our client, we focused our attention on certain key variable that we are going to be discussing.
Initially, when analysing basic level data variables, for example the Average Session Duration on users on the website, as well as the Total No. of Page Views per customer, we found anomalies in terms of outliers, like these ones. These could be the founders and managers of the company in-charge of the website themselves, or teams like us, working in tandem projects with them.
