€23.88 – €27.91
Transformation Steps:
- Data Cleansing:
- Remove Duplicates: Identify and eliminate duplicate transaction records based on transaction ID to ensure data integrity.
- Handle Missing Values: Fill in or remove records with missing customer or transaction data. For example, if customer demographic information is missing, flag these transactions for further investigation.
- Standardize Date Formats: Convert the purchase date field into a consistent format (e.g., YYYY-MM-DD) to enable accurate time-based analysis.
- Data Enrichment:
- Join Customer Demographics: Merge the transaction data with a customer demographics table to enhance the dataset with information such as age, gender, and location. This will facilitate demographic-based analysis.
- Data Aggregation:
- Sum Purchase Amount by Category: Group the data by product category and aggregate the total sales amount for each category to prepare for reporting.
- Calculate Time-Based Metrics: Create derived columns for year, quarter, and month from the purchase date to facilitate time-based performance analysis.
- Data Transformation:
- Normalize Customer Demographics: Create customer segments (e.g., high-value, low-value, new) based on purchase history and demographic characteristics. This will help in targeted analysis by customer profile.
- Derive New Metrics: Calculate metrics such as average purchase value per customer and total transactions per category to enrich the analysis.