E-commerce e Social Commerce

Project Overview

This project demonstrates how to use Gaio DataOS to build a complete analytical solution for E-commerce and Social Commerce, covering everything from data extraction to predictive modeling and interactive dashboards.

---

Development Steps

1. Data Extraction

• Connect multiple data sources: PostgreSQL, MySQL, and CSV files.

• Use ETL blocks in Gaio to:

  • Clean and transform data

  • Create relationships across tables

  • Store intermediate data in temporary tables like:

    • tmp_orders, tmp_customers

• Create final analytical tables:

  • customers_sales

  • orders_sales

  • abandoned_carts_sales

  • social_interactions

---

2. Data Preparation

• Build a unified flat table named sales_ecommerce that combines:

  • Sales, visits, customer profile, social engagement, and abandoned cart data

• This table is optimized for dashboards, exploration, and machine learning.

---

3. Predictive Analytics

3.1 Churn Prediction

• Use the sales_ecommerce table.

• Engineer features like: last purchase, frequency, engagement.

• Use AutoML to predict churn probability.

• Output tables:

  • forecast_churn: churn score per customer

  • forecast_churn_metrics: model performance (ROC, accuracy, etc.)

3.2 Customer Profiling

• Generate customer features: RFM, purchase gaps, preferred categories.

• Apply AutoCluster (K-Means) for customer segmentation.

• Interpret clusters based on behavior and buying patterns.

3.3 Demand Forecasting

• Aggregate order data by time, category, and channel into tmp_order_demand.

• Apply time series models using the Forecast module.

• Output tables:

  • forecast_order_demand

  • forecast_order_demand_metrics

---

4. Interactive Dashboards

Use Gaio’s visual dashboard builder to create:

• Sales Ecommerce Overview:

  • Total sales, channels, abandoned carts, social engagement.

• Customer Detail View:

  • Individual customer metrics and lifecycle behavior.

• Forecast Demand Panel:

  • Projected sales by time, product category, and channel.

• Churn Monitoring:

  • Identify and track at-risk customers.

• Customer Clustering:

  • Visualize segmented profiles for marketing and personalization.

---

Technologies Used

• Visual ETL workflows in Gaio DataOS

• Temporary and Final Tables for data versioning

• AutoML and Clustering with assisted interface

• Dynamic Dashboards with filters, graphs, and tables

• Discovery Module for AI-generated insights (recommended)

---

Expected Outcomes

• Reduced time for building analytical workflows

• Centralized multi-source data in a single platform

• Actionable predictions for churn and demand planning

• End-to-end visibility across ecommerce performance

---

Download this project

57KB

bkp_app-44_25-07-22_17-48-11_sales_ecommerce.zip

archive

Abrir

1MB

Tables E-commerce and Social Commerce csv.zip

archive

Abrir