E-commerce Sales Optimization: A Data-Driven Approach to Profit Maximization

ICT 706
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Executive Summary
E-commerce solution is the best option which is used to manage all the sales, productivity,
consumer satisfaction & quality assurance. As we have seen in these days, improvement for e-
commerce solution is far better & that’s why this is spreading all over the world. Because of their
advantages e-commerce solution is being a leader in the development of digital scenario. It is
used in different kinds of areas like commercial sectors, IT sectors etc. This site is very easy to
find any kind of product or services.
The main motto of this report is to cover all the technological issues, which somewhere affect the
performance of any e-commerce solution. A reliable solution will be provided by an expert in
database management just to make sure that it won’t affect it anymore.
We will use strength, weakness & opportunity analysis (SWOT) method because it is used to get
some important information about any company. In a contraction, qualitative analysis method
will be used to search in-depth about all the business operations who are working in at present.
According to research, got to know that if we improve ROI (return on investment) value of any
company, then it would be the best solution and it will allow company’s owner to connect with
the real time in the consumer market.
All these solutions can be provided to any e-commerce company to enable all shipping free
choices for the customers. Even we can use effective marketing strategies to advertise all the
products in a proper manner.
This Report is going to have some recommendations from various research that has been done in
order to find out the better business implementation and create a plan using which the e-
commerce could gain more profit and can establish the better system plan.
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Table of Contents
Executive Summary.........................................................................................................................2
List of all Assumptions and Abbreviations.....................................................................................4
Background......................................................................................................................................5
Introduction......................................................................................................................................6
Research Methodology....................................................................................................................6
Qualitative Methodology.............................................................................................................6
Quantitative Methodology...........................................................................................................6
Analytical Findings..........................................................................................................................7
Dataset.........................................................................................................................................7
Data Mining and Clustering.........................................................................................................8
Monthly Sales Prediction.............................................................................................................8
Recommendation based on analysis............................................................................................8
Recommendations for the Company.............................................................................................13
Implemented Code.........................................................................................................................14
Conclusion.....................................................................................................................................15
Referencing....................................................................................................................................16
Appendix........................................................................................................................................16
List of Figures
Figure 1: Used Dataset.....................................................................................................................7
Figure 2: Linear Regression function..............................................................................................9
Figure 3: Polynomial Regression Function.....................................................................................9
Figure 4: Linear regression Model Output....................................................................................10
Figure 5: Polynomial Regression Model Output...........................................................................11
Figure 6: Code for Linear regression Graph Plotting....................................................................14
Figure 7: Polynomial Regression Graph Plotting Code................................................................14
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List of all Assumptions and Abbreviations
All the assumption, which is used in this file are written below:
 With delivery products, there should be delivery system.
 Dataset is mainly developed for books segments.
 There should be delivery personnel to deliver goods.
 The dataset should include a variety of products, which all are unique.
Abbreviations:
CSV= Comma Separated Values
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