ICT706: Data Analysis and Recommendations for E-commerce Growth

This report analyzes an e-commerce company's sales data to identify strategies for enhancing performance. It employs SWOT analysis and qualitative data analysis to understand current operations. The report recommends a customer-centric information architecture, integrated content and SEO, and optimized product advertising. Data mining techniques, including hierarchical clustering (divisive and agglomerative methods), are used to identify key geographic regions and products for targeted sales efforts. The report also explores the impact of free shipping and provides recommendations for improving sales, including focusing on festive seasons, implementing a return policy, ensuring timely delivery, and improving website user interface. The implementation plan focuses on improving sales in lagging regions and employing marketing strategies for underperforming products, ultimately aiming to boost the company's overall growth and profitability through data-driven insights.

ICT706

Executive Summary
Electronic commerce or e-commerce is a business model which helps an organization to do
business on an electronic network or commonly known as the internet. E-commerce has almost
all the product or services like books, music, financial services and plane tickets.
This report mainly covers how to cover those aspects which are going to give a hike to the
performance of the company. And those aspects are going to be stated by the data scientist to
ensure success in the performance of the company.
All the useful information about the company is going to be collected through a SWOT analysis
method. To know about the current business operations and the solution provided by the
company in more depth qualitative data analysis method will be taken in use.
To increase the performance of the company it is necessary to have an information architecture
which is customer centric which is productive. The content and SEO should be integrated to
bring your business in front.
The product should be advertised in such a way that it becomes easy for the consumer to search
the product on the e-commerce sites. There should also be a relaxation for the consumer on the
shipping charges of the product to improve the business of the organization.
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Table of Contents
Executive Summary.........................................................................................................................1
List of Assumptions and Abbreviations..........................................................................................3
Background......................................................................................................................................4
Introduction......................................................................................................................................5
Research Methodology....................................................................................................................6
Descriptive or Analytical:............................................................................................................6
Applied or Fundamental:.............................................................................................................6
Quantitative or Qualitative:..........................................................................................................6
Conceptual or Empirical:.............................................................................................................7
Analytical Findings..........................................................................................................................8
Dataset..........................................................................................................................................8
Data Mining:................................................................................................................................9
Divisive method:........................................................................................................................10
Agglomerative method:..............................................................................................................11
Recommendation for the Company:..............................................................................................14
Implementation plan:.....................................................................................................................15
Code Implementation.....................................................................................................................16
Conclusion:....................................................................................................................................18
References:....................................................................................................................................19
Appendix........................................................................................................................................20
List of Figures
Figure 1: Books Dataset Snippet.....................................................................................................8
Figure 2: Training the Data using the Linear regression.................................................................9
Figure 3: Testing the data using the Linear Regression................................................................10
Figure 4: Sales Graph for Different Region..................................................................................11
Figure 5: Region Recommendation...............................................................................................12
Figure 6: Product Recommendation..............................................................................................12
Figure 7: Code for Data Prediction................................................................................................16
Figure 8: Final Prediction Over Dataset........................................................................................17
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List of Assumptions and Abbreviations
The Abbreviations that are used in this report are:
 Comma Separated Value – CSV
 Tab Separated Value – TSV
Assumptions for this report:
 A test dataset is developed for this process
 The Test Dataset will be divided into the Training Set and the Test Set
 Geographic Locations is added with it
 The Products are unique in this
 The need for a Delivery Person
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Background
This E-commerce company has been set up a long time back. At the beginning as it was not very
recognized it used to deals in very fewer regions and had a less amount of products to start with
but after some time passed it expanded its business from very few regions to a lot of regions and
not only with regions but it also expanded in the products because of which the company start
getting recognition .
This company works on almost all the domains which include Gadgets, Books, Toys, Clothes
and household items. Thinking about its customers the company delivers the product on the
doorsteps of the customer.
After having a successful glorious year for so long the company has hit the rocks and is an
immediate need for the strategies to expand its business like before or more than that. And to do
that the company has thought of taking in use the data analytics to get back its position in the
market. Through data analytics, the company will know about the regions from where they can
attain the highest performance.
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Introduction
As the Current scenario is where everything revolves around the e-commerce all the companies
are just competing with each other by surpassing other to be on the top but since the requirement
of the market changes every time. It is not very easy to cope up with the situations.
In this report, it mainly highlights an e-commerce organization who is searching for methods to
enhance the profit of the company with the help of data analytics. Its main aim is to know the
estimate of the monthly sales data for the segments of the book of the company. This report
stores the details about the products which has been sold in the books segment by the company
in the last few months.
The main agenda of this report is to work on the possibilities which are going to increase the
profit of the company by taking help of the Python programming to work on that possibility.
This report also contains the method which is going to produce the output.
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Research Methodology
Research is a process which defines and redefines all the problem that came up, working on
assumptions or answers, gather and evaluate the data and reach to a conclusion and at last, all
that endings are tested in order to see if it just the assumptions or not. Research is putting extra
knowledge to already available knowledge to make it more advanced. Research is something
where to get to know more about something to actually available data by going in more deep
through study, observation, comparison, and experiment.
Research has been categorized as follows:
Descriptive or Analytical:
In descriptive research, it includes surveys and the inquiries of all the found facts. The main
thing which descriptive research revolves around is on what has happened and what is
happening. In descriptive the things which are measured is the frequency of shopping,
preferences of people or something like that. On the other hand, analytical research deals with
the information which is already available and through that make the critical evaluation.
Applied or Fundamental:
Applied research is something which works on giving quick solutions to the problem of an
organization. Fundamental research deals with the thought of a theory.
Quantitative or Qualitative:
Quantitative research where quantity is measured. It works for something related to quantity or
amount. Qualitative research revolves around the things related to quality which includes
behavior science where it researches about the human behavior. To apply qualitative research is
comparatively difficult and it needs guidance from experimental psychologists
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Conceptual or Empirical:
Conceptual research deals with some conceptual idea. Philosophers take help of these for
generating ideas and concept or make some change in something in the ideas which already
exists. Empirical research depends on experience or observation. It is based on data where the
conclusions are verified by observation or experiment.
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Analytical Findings
The report uses a dataset of books segment. The dataset includes only the books which are sold
by the company.
Dataset
The dataset includes:
 Product name,
 Product price,
 Shipping type
 Monthly sales
 Geographic region
 No. Of customers who bought the product,
 Customer type
Figure 1: Books Dataset Snippet
Figure 1 shown above illustrates a part of the dataset which also includes the metadata in the
books dataset
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Data Mining:
Data mining is the method which companies take in use where all the raw data is converted into
useful information. With the help of software, patterns can be looked in large batches of data, it
will help to learn more about the customers and develop marketing strategies which are effective
and help in increasing the sale and decreasing the cost.
As the process of data mining helps in determining the sales and give high revenues it is used by
a lot of e-commerce companies. Data mining is dependent on data collection and storing that
data in warehousing. Its main aim is to gather the information from the dataset and deliver it in
such a way that it is understandable. For our company data mining can help in by providing the
ways to improve the productivity, what is going to happen if free shipping methodology will be
put in action, which region should be targeted to increase the sales, which product should be
given the highest priority to improve the sales.
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Figure 2: Training the Data using the Linear regression
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Figure 3: Testing the data using the Linear Regression
The output of the data mining is according to the data type. Clustering contains the dataset
having the same kind of data. Hierarchical Clustering deals with forming clusters that have a
predetermined ordering from top to bottom. It is categorized in two methods:
Divisive method:
This method is also considered as top-down where every observation is assigned to an individual
cluster and then divide the previously achieved cluster into two alike clusters. In comparison
with agglomerative method gives more accurate hierarchies but when the concept is considered it
is more complex.
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