Improving Sales for an E-Commerce Store: A Data Analytics Project Overview and Suggestions - Essay Prowess

Improving Sales for an E-Commerce Store: A Data Analytics Project Overview and Suggestions

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E-Commerce Project Overview

E-Commerce is the activity of buying or selling products through online services or over the Internet. The e-commerce market has changed the way business is transacted, whether in retail or business-to-business, locally or globally.

The dataset of this project is from an online store, which includes product information and customer transaction records from 2016 to 2018. The ultimate goal of the company is to improve the sales. In this project, you’re supposed to find out the online shopping behaviors of the customers and make marketing suggestions for the store in order to increase the customers’ life-time value to the company.

Dataset

The dataset has 7 tables, which include customer transaction information for each order and the product information:

onlineshop_customers.sql

onlineshop_orders_items.sql: one order can contain more than one item.

onlineshop_orders.sql: ‘fulfilled’ indicates the order was completed.

onlineshop_products_sku.sql: ‘sku’ represent stock keeping unit, which is used to track inventory in the store.

onlineshop_product.sql

onlineshop_traffic.sql: page_views: each time a user visits a web page, it is called a page view; sessions: a session is a group of user interactions with your website that take place within a given time frame; avg_session_in_s: average seconds in one session.

onlineshop_transactions.sql: status success indicates the transaction was completed.

Assignment

The objective of this E-Commerce project is to test your SQL skills as well as data analytics skills. Firstly, because the dataset is in .sql format, you will need to load the dataset into MySQL Workbench. Feel free to utilize visualizations and researched material to support your responses to some of the tasks and questions. Also, remember to show your calculations.

  1. Describe the relationship between each table.
  2. Find the Product type, Product style and SKU number for each product. (You may join different tables together to get your proper result)
  3. What’s the total number of orders for each product type? What’s the most popular product type?
  4. What’s the monthly total sales and monthly total orders? How about the total orders for each product over time?
  5. How about traffic over time e.g. page views, sessions? Do you see any relationship between traffic and orders over time?
  6. Select and calculate important business metrics, e.g. churn rate, retention rate, conversion rate. Justify your selection and explain and interpret the result.
  7. The goal for almost all companies is to boost sales. With that in mind, based on the metrics that you calculated, what business suggestions are you able to provide in order to achieve the goal. (CLO 1,3,4,6,7,8,10)

Assignment Preview:

  1. Describing the relationship between each table:
  • The “onlineshop_customers” table contains information about the customers who made purchases on the online store, such as their name, email address, and sign-up date.
  • The “onlineshop_orders_items” table contains information about the items that were included in each order, such as the product name, quantity, and price. This table also includes a foreign key to the “onlineshop_orders” table, which links the items to the order in which they were purchased.
  • The “onlineshop_orders” table contains information about each order that was placed on the online store, such as the order date, total price, and fulfillment status. This table also includes a foreign key to the “onlineshop_customers” table, which links the order to the customer who placed it.
  • The “onlineshop_products_sku” table contains information about the stock keeping units (SKUs) for each product, such as the product name, style, and type. This table also includes a foreign key to the “onlineshop_products” table, which links the SKU to the corresponding product.
  • The “onlineshop_products” table contains information about the products that are available on the online store, such as their name, description, and images.
  • The “onlineshop_traffic” table contains information about the website traffic on the online store, such as the number of page views, number of sessions, and average time spent per session.
  • The “onlineshop_transactions” table contains information about the transactions that were made on the online store, such as the transaction date, amount, and status (success or failure).
  1. To find the product type, product style, and SKU number for each product, you can join the “onlineshop_products_sku” table with the “onlineshop_products” table on the product_id column.

  2. To find the total number of orders for each product type, you can join the “onlineshop_orders_items” table with the “onlineshop_products_sku” table on the product_id column and then group the results by product type. The most popular product type would be the one with the highest number of orders.

  3. To find the monthly total sales and total orders, you can use the “onlineshop_orders” table and group the results by month, summing up the total price for each month to get the total sales, and counting the number of orders for each month to get the total orders. To find the total orders for each product over time, you can join the “onlineshop_orders_items” table with the “onlineshop_products_sku” table on the product_id column, and group the results by product and month.

  4. To analyze traffic over time, you can use the “onlineshop_traffic” table and group the results by month, measuring the number of page views, number of sessions, and average time spent per session. To check the relationship between traffic and orders over time, you can join the “onlineshop_traffic” table with the “onlineshop_orders” table on the date and analyze the results.

  5. Some important business metrics that can be calculated for this e-commerce store include:

  • Churn rate: The percentage of customers who stop making purchases on the store over a certain period of time.
  • Retention rate: The percentage of customers who continue making purchases on the store over a certain period of time.
    • Conversion rate: The percentage of website visitors who make a purchase on the store. To calculate these metrics, you can use data from the “onlineshop_customers” table, “onlineshop_orders” table, and “onlineshop_traffic” table.
    1. Based on the metrics that you calculated, there are several business suggestions that can be made to improve sales for the e-commerce store:
    • Focusing on customer retention: A high retention rate is an indication that customers are satisfied with their experience on the store and are likely to continue making purchases. The company can work on strategies to retain these customers, such as offering loyalty programs, personalized discounts, and excellent customer service.
    • Improving the conversion rate: A low conversion rate can indicate issues with the website’s usability or the product pricing, discounts and promotions are not well planned. Company should try to find ways to optimize the website for conversions and run A/B tests to improve the shopping experience for the customers.
    • Utilizing customer data: By analyzing the customer data, the company can gain insights into their shopping behavior, preferences, and demographics. This can help the company personalize their marketing efforts and target the right customers with the right products at the right time.
    • Analyzing traffic and order data over time: By analyzing traffic and order data over time, the company can gain insight into how their website is performing and when customers are most likely to make a purchase. This can help the company optimize their marketing efforts and improve the customer experience.

    This is a brief overview of the project and some of the steps that can be taken to complete it, but keep in mind that each step requires more detailed work, and your approach may change based on the specific requirements and context of the project.


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