Difference between revisions of "AY1718 T2 Group21 Final Findings"
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| + | Using Market Basket Analysis and Data-Driven Customer Segmentation and Profiling to Increase e-Commerce Sales of A Children’s Educational e-Commerce Business from India | ||
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| + | <div style="background: #006400; 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>Abstract</strong></font></div></div> | ||
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| + | As the online market rapidly grows today, many businesses turn to the Internet to start their own businesses, setting up e-commerce sites to attract customers from all over the world. Apart from actual sales, owners aim to improve other sales metrics to boost their business, focusing their resources on better marketing materials. With greater outreach, e-commerce sites collect an abundance of data from their visitors. | ||
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| − | <br/> | + | Working with an online business from India, Brainsmith, the project analyses the significant factors to provide bundling and association rules that could help increase sales. First, exploratory methods were conducted to find customer purchasing behaviour and transactions. Then, Market Basket Analysis was used to analyse the products purchase patterns to find the best bundling packages for Brainsmith. Using two years of website traffic data, the paper focuses on product bundling associations to provide relevant recommendations to boost the website’s sales and conversion rates. |
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| + | <!-- 1.Introduction --> | ||
| + | <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>1. INTRODUCTION</strong></font></div></div> | ||
| + | This paper makes use of data analytics techniques to arrive at analysis results, to better optimise processes and sales practices for Brainsmith, a children’s education company based in India that designs and delivers premium educational, learning products and content for early childhood learning and healthy brain development. Data analytics has developed and been implemented over time, and useful results have been observed for a variety of business industries and academic areas. Generically, data analytics is the process of examining data sets to reach conclusions about the information they contain, and increasingly with more specialised tools and software. The company conducts its sales through its website, over the period that we observe their data – and we utilise data analytics to find patterns in the purchase behaviour of their customers according to the data recorded. | ||
| + | <br><br> | ||
| + | The paper takes inspiration from recent literature, that tracks bundling and market baskets in offline product sales, and attempts to further extrapolate it in an e-commerce capacity. While there has been considerable research on the business models involving MBA, this paper hopes to bridge the gap in the internet sales age, and emulates results that have worked in physical stores and attempts to see the results in a virtual environment. | ||
| + | The outline of the paper is as follows: introduction and motivation behind selecting the client and overall analysis, the literature review – giving a fair characterisation of the literature and research already out there that is relevant to the concepts we’re testing in this paper. Next, the next third part shares the relevant motivation for market basket analysis, and the methodology used implement it, while the fourth section discusses the implementation of MBA and the results we obtained from it. The fifth section mentions our business recommendations according to our understanding of the business and industry, while the final sixth part comprises of a brief conclusion and limitations related to the dataset. | ||
| + | <br><br> | ||
| + | The outline of the paper is as follows: first, there’s an introduction and motivation behind selecting the client and overall analysis. Following this is the literature review - giving a fair characterisation of the literature and research already out there that is relevant to the concepts we’re testing in this paper. The third part then, shares the relevant data structuring for market basket analysis, as well as the methodology used to implement it; which leads onto the next section discussing the implementations of MBA, and the results obtained from it. The paper wraps up with a section on business recommendations according to our understanding of the business and industry, while the final part comprises of a brief conclusion and limitations related to the dataset. | ||
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| + | <!-- End Introduction --> | ||
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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>Further Exploration</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>Further Exploration</strong></font></div></div> | ||
Revision as of 15:22, 10 April 2018
Using Market Basket Analysis and Data-Driven Customer Segmentation and Profiling to Increase e-Commerce Sales of A Children’s Educational e-Commerce Business from India
As the online market rapidly grows today, many businesses turn to the Internet to start their own businesses, setting up e-commerce sites to attract customers from all over the world. Apart from actual sales, owners aim to improve other sales metrics to boost their business, focusing their resources on better marketing materials. With greater outreach, e-commerce sites collect an abundance of data from their visitors.
Working with an online business from India, Brainsmith, the project analyses the significant factors to provide bundling and association rules that could help increase sales. First, exploratory methods were conducted to find customer purchasing behaviour and transactions. Then, Market Basket Analysis was used to analyse the products purchase patterns to find the best bundling packages for Brainsmith. Using two years of website traffic data, the paper focuses on product bundling associations to provide relevant recommendations to boost the website’s sales and conversion rates.
This paper makes use of data analytics techniques to arrive at analysis results, to better optimise processes and sales practices for Brainsmith, a children’s education company based in India that designs and delivers premium educational, learning products and content for early childhood learning and healthy brain development. Data analytics has developed and been implemented over time, and useful results have been observed for a variety of business industries and academic areas. Generically, data analytics is the process of examining data sets to reach conclusions about the information they contain, and increasingly with more specialised tools and software. The company conducts its sales through its website, over the period that we observe their data – and we utilise data analytics to find patterns in the purchase behaviour of their customers according to the data recorded.
The paper takes inspiration from recent literature, that tracks bundling and market baskets in offline product sales, and attempts to further extrapolate it in an e-commerce capacity. While there has been considerable research on the business models involving MBA, this paper hopes to bridge the gap in the internet sales age, and emulates results that have worked in physical stores and attempts to see the results in a virtual environment.
The outline of the paper is as follows: introduction and motivation behind selecting the client and overall analysis, the literature review – giving a fair characterisation of the literature and research already out there that is relevant to the concepts we’re testing in this paper. Next, the next third part shares the relevant motivation for market basket analysis, and the methodology used implement it, while the fourth section discusses the implementation of MBA and the results we obtained from it. The fifth section mentions our business recommendations according to our understanding of the business and industry, while the final sixth part comprises of a brief conclusion and limitations related to the dataset.
The outline of the paper is as follows: first, there’s an introduction and motivation behind selecting the client and overall analysis. Following this is the literature review - giving a fair characterisation of the literature and research already out there that is relevant to the concepts we’re testing in this paper. The third part then, shares the relevant data structuring for market basket analysis, as well as the methodology used to implement it; which leads onto the next section discussing the implementations of MBA, and the results obtained from it. The paper wraps up with a section on business recommendations according to our understanding of the business and industry, while the final part comprises of a brief conclusion and limitations related to the dataset.
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TO BE UPDATED
