> For the complete documentation index, see [llms.txt](https://docs.gaiodataos.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.gaiodataos.com/use-cases/e-commerce-e-social-commerce.md).

# E-commerce e Social Commerce

<figure><img src="/files/Vbx2CIYR0lRgSnmdur1Y" alt=""><figcaption></figcaption></figure>

### 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**.

***

## &#x20;Development Steps

### 1. Data Extraction

<figure><img src="/files/bCSFhpwKlcVVNUJ9p0UT" alt=""><figcaption></figcaption></figure>

* 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

<figure><img src="/files/x9SkYni29jXRmLW75Kze" alt=""><figcaption></figcaption></figure>

* 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**

<figure><img src="/files/MMYcHwulopokiPx22VYX" alt=""><figcaption></figcaption></figure>

* 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**

<figure><img src="/files/9x1zOr5cWzBZyiuSD5cO" alt=""><figcaption></figcaption></figure>

* 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

{% file src="/files/2AriijVg8OUV8pcSyzjJ" %}

{% file src="/files/xbgTVkGtVfMOg7Xurw6C" %}