Ecommerce Analysis Dashboard
Ecommerce Analysis Dashboard
About this project
šš»ššæš¼š±šš°šš¶š¼š»:
āŗ In this project, we have analyzed an Ecommerce dataset. An ecommerce dashboard is a user interface that provides a centralized location for managing and analyzing various aspects of an online business, such as sales, customer behavior, and inventory.
āŗ They can also integrate with various platforms such as email marketing, payment gateways, and shipping providers to provide a holistic view of the ecommerce operations.
šš®šš®:
āŗ We found this dataset from Kaggle. We have an e-commerce sales dataset from India with 3 csv files -List of Orders, Order details, Sales target.
āŗ List of Orders-This dataset contains purchase information. The information includes ID, Date of Purchase and customer details.
āŗ Order Details- This dataset contains order ID, with the order price, quantity, profit, category and subcategory of product.
āŗ Sales target-This dataset contains sales target amount and date for each product category.
šš½š½šæš¼š®š°šµ:
āŗ Acquire the ecommerce sales data from the business's database and import it into Power BI.
āŗ Use Power BI's data modeling and transformation features to clean and prepare the data for analysis. This will involve tasks such as filtering out irrelevant data, creating calculated columns and tables, and establishing relationships between tables.
āŗ Create a set of interactive visualizations to represent the key metrics of the business such as total sales, revenue by product category, customer demographics, and more.
āŗ Use Power BI's built-in data visualization features such as charts, tables, and maps to create a visually appealing and easy-to-use dashboard.
āŗ Use Power BI's filtering and sorting capabilities to allow users to customize the data displayed on the dashboard based on their specific needs.
āŗ We decided to do RFM Analysis which gives a more clarity to the business about it's customers.
āŗ To conduct RFM Analysis, we used Alteryx to apply different conditions & formulas on the dataset. Calculated Recency, Frequency & Monetary Scores.
āŗ Created visuals for RFM Analysis & found out most valuable customers.
āŗ Use the insights gained from the dashboard to make data-driven decisions and drive business growth.
šŖšµš®š š¶š š„šš šš»š®š¹ššš¶š?
āŗ RFM (Recency, Frequency, Monetary) analysis is a marketing technique used to segment customers based on their purchase behavior.
āŗ It involves analyzing the recency (how recently a customer made a purchase), frequency (how often a customer makes a purchase), and monetary value (how much a customer spends) of their transactions.
āŗ The RFM model assigns a score to each customer in each category (1-5) and then combines the scores to create a composite RFM score.
āŗ Customers are then segmented based on their composite RFM score, with the highest scoring customers considered the most valuable.
šš²š®šššæš²š š¼š³ ššµš¶š šš®ššµšÆš¼š®šæš±:
āŗ Source Parameter which helps to change datasets files easily.
āŗ Filter panel for easy visualization experience.
āŗ Insights window to find out key business driven factors.
āŗ Tooltips give more details to the cost factor.
āŗ Interactive visuals with cross filtering
āŗ RFM Analysis technique used to segment customers based on their purchase behavior.
š²šš š°ššššššš:
šÆ ššššš¼šŗš²šæ šš²šš®š¶š¹š:
ā¢ 63% of total orders is from Clothing category.
ā¢ 68% orders are Profitable, 30% orders are giving Loss & 2% are None.
ā¢ Most Profitable months are March and November.
ā¢ All the categories have achieved more than 70% of target revenue.
ā¢ Electronics making the most revenue i.e., 37%
ā¢ Electronic games & Tables sub-categories orders are not profitable to the business.
šÆ š¢šæš±š²šæ šš²šš®š¶š¹š:
ā¢ There are total 331 customers with 440 unique orders.
ā¢ Madhya Pradesh is the most profitable state i.e., Rs.7K
ā¢ Tamil Nadu, Andhra Pradesh & Punjab are the states which are making loss.
ā¢ Most orders are from the customer age bucket of 41-50 i.e., 389 and Clothing is the popular category.
ā¢ Indore, Delhi, Allahabad and Mumbai are cities with most profits.
ā¢ Average Cost price & Selling price is approximately Rs. 1 Lakh per state.
šÆ š„šš šš»š®š¹ššš¶š:
ā¢ There are 2 customers with 444 RFM Score.
ā¢ RFM Analysis shows that the company has more count of Gold customers and less count of Platinum customers.
ā¢ Average Recency Score is 2.50, Average Frequency Score is 1.90 & Average Monetary Score is 2.31.
ā¢ In Clothing Category, we have more monetary score.
ā¢ Due to loss making orders we have more Bronze customers.
ā¢ Age bucket of 41-50 are more likely to be Platinum customers.
