Drop Shipping Analysis

Business Problem

In the highly competitive e-commerce industry, understanding customer behavior and optimizing operations are key to success. The provided dataset contains detailed information about customer orders, including product details, pricing, shipping, and payment information. This data presents an opportunity to gain insights into various aspects of the business and identify areas for improvement.

A few areas that you can focus your analysis on:

1. Customer Behavior Analysis: Understanding the behavior of customers is crucial for personalizing the shopping experience and improving customer retention. We will analyze the data to identify trends and patterns in customer behavior, such as preferred product categories, payment methods, and order frequencies.
2. Sales Performance Analysis: We will evaluate the performance of different product categories and brands in terms of sales volume and revenue. This analysis can inform inventory management decisions and marketing strategies.
3. Operational Efficiency: We will analyze the order and shipping dates to assess the efficiency of the order fulfillment process. Identifying any delays or inefficiencies can help improve customer satisfaction and reduce operational costs.
4. Pricing Strategy: By analyzing the relationship between price, quantity ordered, and discounts, we can gain insights into the effectiveness of the current pricing strategy and identify opportunities for optimization.

Data Analysis Approach

To address these business problems, conduct a comprehensive data analysis, including exploratory data analysis, feature engineering, data visualization and hypothesis testing. The analysis will be guided by a set of challenge questions designed to explore different aspects of the data and generate actionable insights.

The ultimate goal of this project is to leverage data to make informed decisions that enhance customer satisfaction, improve operational efficiency, and increase profitability.

Deliverables

You can analyze the data in any tool you like (Tableau, Power BI, python, R, Excel, etc.) But, your manager
would like a dashboard. The dashboard will be used by upper management to monitoring performance.

She would also like for you to generate a slide deck to present your analysis and recommendations to the VP of Human Resources of the company. She would like to know the factors that impact attrition and which areas of the company are impacted the most.

The slide deck can be done in Google Slides, PowerPoint, or any other tool. Just save it as a PDF.

Additional Instructions

Feel free to explore the data however you see fit. We have provided some guided questions to help direct your analysis and spark your own ideas.

Download the Data

Get the data here.

Data Dictionary

The dataset contains the following columns:

1. order_id: The ID of the order.
2. customer_id: The ID of the customer.
3. product_id: The ID of the product.
4. category: The category of the product.
5. brand: The brand of the product.
6. price: The price of the product.
7. quantity: The quantity of the product ordered.
8. discount: The discount applied to the order.
9. order_date: The date the order was placed.
10. shipping_date: The date the order was shipped.
11. shipping_address: The shipping address for the order.
12. shipping_city: The city to which the order was shipped.
13. shipping_state: The state to which the order was shipped.
14. shipping_zip: The zip code to which the order was shipped.
15. payment_method: The payment method used for the order.
16. payment_date: The date the payment was made.
17. payment_amount: The amount paid for the order.
18. refund: Whether a refund was issued for the order.
19. refund_date: The date the refund was issued.

Guided Questions

Exploratory Data Analysis (EDA)

1. What is the total number of orders in the dataset?
   • Understanding the total number of orders can give an idea about the scale of the business.
2. How many unique customers are there?
   • This can help in understanding the customer base of the business.
3. What is the distribution of orders across different product categories?
   • This can provide insights into which categories are more popular and generate more orders.
4. What is the distribution of orders across different brands?
   • This can provide insights into which brands are more popular and generate more orders.
5. What is the average order value?
   • This can provide insights into the financial aspects of the business.
6. What is the distribution of order quantities?
   • This can provide insights into the typical size of orders.
7. What is the distribution of discounts?
   • This can provide insights into the pricing strategy of the business.
8. What is the distribution of payment amounts?
   • This can provide insights into the financial aspects of the business.
9. How many orders have been refunded?
   • This can provide insights into the quality of products or customer satisfaction.
10. What is the time range of the dataset?

• This can provide insights into the time period covered by the dataset.

Feature Engineering

11. Can we create a feature representing the time delay between order date and shipping date?

• This can provide insights into the shipping efficiency of the business.

12. Can we create a feature representing the time delay between order date and payment date?

• This can provide insights into the payment processing efficiency of the business.

13. Can we create a feature representing the total value of an order (price * quantity – discount)?

• This can provide a more accurate representation of the financial value of an order.

14. Can we create a feature representing whether an order was paid in full (payment amount >= total value of order)?

• This can provide insights into the payment behavior of customers.

15. Can we create a feature representing the profitability of an order (total value of order – cost of goods sold)?

• This can provide insights into the profitability of the business. Note that this requires additional information about the cost of goods sold.

Data Visualization

16. Can we visualize the distribution of orders over time?

• This can provide insights into the seasonality or trends in the business.

17. Can we visualize the distribution of orders across different product categories?

• This can provide insights into which categories are more popular.

18. Can we visualize the distribution of orders across different brands?

• This can provide insights into which brands are more popular.

Hypothesis Testing

19. Is there a significant difference in the average order value between different product categories?
    • This can help identify which categories are more lucrative.
20. Is there a significant difference in the average order value between different brands?
    • This can help identify which brands are more lucrative.
21. Is there a significant difference in the refund rates between different product categories?
    • This can help identify which categories have more quality issues or customer dissatisfaction.
22. Is there a significant difference in the refund rates between different brands?
    • This can help identify which brands have more quality issues or customer dissatisfaction.
23. Is there a significant difference in the shipping time (from order date to shipping date) between different product categories?
    • This can help identify which categories have longer shipping times.
24. Is there a significant difference in the shipping time (from order date to shipping date) between different brands?
    • This can help identify which brands have longer shipping times.

Time Series Analysis

30. Can we analyze the trend of orders over time?
    • This can provide insights into the growth of the business.
31. Can we analyze the seasonality of orders?
    • This can provide insights into the peak and off-peak periods for the business.
32. Can we forecast future orders based on historical data?
    • This can help in planning and inventory management.
33. Can we analyze the trend of refunds over time?
    • This can provide insights into the quality control of the business.
34. Can we forecast future refunds based on historical data?
    • This can help in quality control and customer service.

Customer Behavior Analysis

35. Can we identify the most loyal customers (e.g., those who make the most orders or have the highest total order value)?
    • This can help in customer relationship management and targeted marketing.
36. Can we identify the customers who are most likely to request a refund?
    • This can help in quality control and customer service.
37. Can we analyze the ordering patterns of customers (e.g., frequency, regularity)?
    • This can provide insights into customer behavior and preferences.
38. Can we analyze the refund patterns of customers (e.g., frequency, reasons if available)?
    • This can provide insights into customer satisfaction and product quality.

Geographic Analysis

39. Can we analyze the distribution of orders across different cities or states?
    • This can provide insights into the geographic market of the business.
40. Can we identify the cities or states with the highest order values or quantities?
    • This can help in market analysis and targeted marketing.
41. Can we identify the cities or states with the highest refund rates?
    • This can provide insights into customer satisfaction and product quality in different regions.

Payment Analysis

42. Can we analyze the distribution of payment methods?
    • This can provide insights into customer preferences for payment methods.
43. Can we identify the payment methods with the highest order values or quantities?
    • This can provide insights into the financial aspects of the business.
44. Can we identify the payment methods with the highest refund rates?
    • This can provide insights into the reliability or risk of different payment methods.

Product Analysis

45. Can we identify the most popular products (e.g., those with the most orders or highest total order value)?
    • This can help in inventory management and targeted marketing.
46. Can we identify the products that are most likely to be refunded?
    • This can help in quality control and supplier management.
47. Can we analyze the price distribution of products?
    • This can provide insights into the pricing strategy of the business.
48. Can we analyze the quantity distribution of products in orders?
    • This can provide insights into customer preferences and order sizes.

Advanced Analysis

49. Can we build a recommendation system to suggest products to customers based on their previous orders?
    • This can enhance the shopping experience and increase sales.
50. Can we use clustering techniques to segment customers based on their ordering behavior?
    • This can help in customer relationship management and targeted marketing.

Machine Learning

25. Can we build a predictive model to forecast future orders?
    • This can help in planning and inventory management.
26. Can we build a predictive model to identify orders that are likely to be refunded?
    • This can help in quality control and customer service.
27. Can we build a predictive model to estimate the order value based on other features (e.g., product category, brand, discount)?
    • This can help in sales and revenue forecasting.
28. Can we build a predictive model to estimate the shipping time based on other features (e.g., product category, brand)?
    • This can help in logistics and customer service.
29. Can we build a predictive model to segment customers based on their ordering behavior?
    • This can help in marketing and customer relationship management.