ETL Project for Customer Data Profiles

Overview

An ETL (Extract, Transform, Load) project was implemented to build comprehensive customer data profiles from various source systems into a data warehouse.

Objective

The key objective was to create a single source of truth for customer data that provides insights into behavior, preferences, and attributes. The customer profiles enable personalization, targeting, reporting, and predictive analytics.

Data Sources

Key data sources utilized:

Sales transactions Voucher and gift card redemptions Web/app activity Location Comments Favorites and lists Club activity ETL Process

The ETL process extracts data from the source systems, transforms it into a unified format, and loads it into the customer profile tables in the data warehouse.

Key steps:

Perform transformations such as cleansing, standardization, and business logic Load the data into the customer profile tables using merge queries Schedule daily and weekly ETL jobs to refresh the customer profiles Output

The output is a set of customer profile tables containing attributes like:

Purchase history Voucher usage Favorite products Location Segmentation Activity recency

The customer profiles enable:

360 degree view of customers Targeting and personalization Customer analytics and segmentation Tracking trends and changes over time Insights to improve customer experience The standardized data warehouse structure provides a foundation for customer-focused initiatives across marketing, sales, and product teams.

Author Mohammed Torbati