CASE STUDY ON E-COMMERCE DATA

Chan Man Ho s216652 Hui Nok Hei s216865 Chan Ho Wai s216649 Wong Wing Chun s226424.

[Audio] For this case study we will be looking at the data from an e-commerce store. We will be analyzing the data to get insight on customer behavior and business performance. Our report will be broken down into three sections. First we will give an overview of the dataset. Second we will discuss our methodology. Lastly we will present our analysis results. Our results will be broken down into different sections so that you can grasp the most useful information quickly..

[image] c - kaggle Create @ Home Q Competitions Datasets Models O Code https://www.kaggle.com/datasets/mervemenekse/ecommerce&taset q Search Data Card Code (2) Discussion (2) Suggestions (0) Analyzing the purchases of our customers for 1 year in America E-commerce dataset. How are their customer's online buying habits? Columns name and meanings: Order_Date: The date the product was ordered. Aging: The time from the day the product is ordered to the day it is delivered. Sign In Register Expected update frequency Not specified Tags Business E-Commerce Services.

[Audio] We have obtained an e-commerce dataset from Kaggle which holds significant data for thousands of online transactions incorporating details of customers product information and attributes such as delivery time and item categories. With this dataset we can gain knowledge about shopping conduct and investigate the correlations between customers and products..

[Audio] We are examining the purchase data of an American e-commerce business over the course of one year. The dataset has information on the spending habits and preferences of American customers as well as the recurrence and magnitude of their orders. This data will give us useful insights on the performance of the business and how to better satisfy their customers..

[Audio] We are examining an e-commerce dataset containing 15 elements of data for a case study. Our objective is to explore the details of this data in order to gain insight into customer behavior and further predict their requirements..

[Audio] This slide shows the fields to be found in the data of the e-commerce research project. As you can see there are several fields which are labeled for easier understanding. The fields give information about the customers their purchase history and the products that they have bought. All of this data is very useful in understanding the consumers and their preferences allowing us to draw conclusions and insights into the e-commerce industry..

[Audio] This presentation covers a case study of e-commerce data. We will take a look at the unique customer identifier the order date and the aging which is the time it takes from the day the product is ordered to the day it is delivered.This information allows us to analyze customer orders detect patterns and identify any areas for improvement..

[Audio] The case study on e-commerce requires data about the device type customer login type and gender of the customer. This information is essential to gaining an understanding of the e-commerce landscape. With this knowledge we can gain valuable insights into how customers interact with the website or app and thus aid in improving the overall e-commerce experience..

[Audio] The slide focuses on the product sales and product category associated with respect to a given e-commerce data. Product purchased is the main element of the data which is followed by the sales amount and product category. All these three together provide a thorough overview of the e-commerce data..

[Audio] The slide examines the discount profit and quantity of an e-commerce business. The discount rate is given as a percentage with the profit being the money earned and the quantity indicating the unit amount of the product sold..

[Audio] The Order Priority field of e-commerce data indicates how important a customer's order is with possible values from "Critical" to "Normal". This helps the system prioritize orders accordingly. The Payment Method field specifies the payment type used such as debit credit cash or money order. Lastly the Shipping Cost field indicates the cost paid by the customer for their order to be shipped..

[Audio] We created a data pipeline to automate the analysis of e-commerce data in this project. This pipeline made development more efficient and allowed for better reproducibility of the data analysis ultimately improving the engineering process..

[Audio] Technology has drastically altered the manner in which businesses operate. The emergence of the internet along with that of mobile devices and e-commerce has allowed companies to reach a greater international target audience. To create our CASE STUDY ON E-COMMERCE data a multitude of technologies and strategies were used. Database management systems data visualization software and various statistical assessments were employed to comprehend critical information and patterns from the data. The results were a comprehensive insight on the habits of online shoppers as well as potential remedies to improve the user experience..

[Audio] We explore the process of performing data analysis on E-commerce data in this slide. Utilizing tools such as Access and Power BI we can visualize the data from Access S-Q-L and develop insightful data models. This allows for a more comprehensive understanding of our data and enhances our decision-making..

[Audio] An analysis of the e-commerce dataset revealed a range of purchasing habits among users. Results showed preference for certain items helping to gain insight into customer behaviour. This information could be useful for businesses to gain a better understanding of their customers and formulate strategies to attract them with appropriate promotions and offers..

[Audio] Analysis of e-commerce data uncovered the most popular categories among customers. Accessories shoes clothing and electronics were the highest ranked. Moreover customer reviews and product images were found to have a significant impact on purchase decisions..

[Audio] Our analysis reveals that the fashion category was the most prominent product category with more than half of total sales attributed to it. Combined with the home and furniture category the two categories accounted for a notable 80% of the total sales of which the latter made up 30%. This case study underlines the importance of e-commerce data..

[Audio] Based on our analysis Autos & Accessories had the third highest amount of orders accounting for 14 percent. On the other hand Electronics had the smallest share of orders amounting to only 5 percent. This suggests that customers are more likely to purchase from the Autos & Accessories category rather than from the Electronics category..

[Audio] Our finding number 2 showed that there is clear variation in performance between different categories in the e-commerce sector in terms of profitability. The data suggests that businesses should direct their resources accordingly to focus on the most profitable categories..

[Audio] The results showed that the Fashion category was the most profitable making up 57% of the total profits while the Home & Furniture category contributed 24%..

[Audio] It's clear that the e-commerce store achieved success by heavily investing in the Autos & Accessories category. This category accounted for 13% of profits despite making up only 8% of all sales. As a result this category had the highest profit margin of any of the store's products at 24%." From this data it's clear that the e-commerce store experienced success by investing heavily in Autos & Accessories. This category brought in 13% of the store's profits despite making up only 8% of all sales. This resulted in the highest profit margin of any product at 24%..

[Audio] We found that product profitability is an essential component in evaluating the success of e-commerce businesses. To measure profitability we examined the revenue generated by each product. Using the data from this examination we compared profitability to sales volume in order to make better decisions regarding resource allocation..

[Audio] Our findings indicate that fashion is the most profitable product category. T-shirts accounted for 9% watches and running shoes 8% each jeans 7.67% and formal shoes 7.35% of all profits. This shows that fashion products have the potential to generate a large return for our company..

[Audio] Our fourth finding is that user profitability is highly dependent on user flow and usage of the platform. Our research shows that customers who actively use the platform over longer periods of time generate the highest profits. We recommend focusing on long-term user engagement to maximize user profitability..

[Audio] Our findings reveal that guest users contributed 4% of the profits while new and first signup users accounted for less than 1%. Notably registered members were responsible for a whopping 96% of the profits..

[Audio] Our key finding number five is that desktop computers are the most profitable devices followed by tablets and then mobile phones. Data accumulated indicates that mobile phones generate less profit than tablets and desktops..

[Audio] Our findings demonstrate that web users are much more lucrative than mobile device users in terms of e-commerce data. Our analysis indicates that web users generated 93% of the profits whilst mobile devices only accounted for 7%. Concentrating marketing endeavors on web users could be an advantageous business decision..

[Audio] Analyzing the data of the e-commerce industry is essential to understand the significance of fashion and to maximize user membership. This analysis provides businesses with the opportunity to capitalize on the most up-to-date fashion trends and maintain user engagement. Furthermore it creates new possibilities for profitability..

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