Distribution of Sales by Category

• Loaded the dataset and displayed the initial rows to understand its structure.
• Summarized sales data by category using a pie chart to visualize the distribution.

1. Identifying the Cohort: Determine the first purchase date for each customer to assign them to a cohort.
2. Tracking Subsequent Purchases: For each subsequent period, check if the customers from each cohort made any purchases.
3. Calculating Retention Rates: Calculate the percentage of customers from the initial cohort who made purchases in the following periods.

Cohort Analysis Preparation

• Converted the 'Order Date' to datetime format for accurate processing.
• Created a new dataframe to identify the first purchase date for each customer and assigned them to a monthly cohort.
• Merged this cohort information back into the original dataset to facilitate further analysis.

Cohort Retention Analysis

• Created an 'Order Period' column to reflect the month of each order.
• Grouped the data by 'Cohort Month' and 'Order Period', then counted unique customers in each period.
• Formulated a retention matrix as a pivot table, showing how many customers from each cohort made purchases in subsequent months.
• Calculated retention rates by dividing customer counts per period by initial cohort sizes and converted these rates into percentages for better readability.