https://github.com/tanyagarg25/fashion-industry-analysis-trends-insights-and-performance

The Fashion Industry Analysis project uses Azure SQL and Tableau to analyze sales trends and customer behavior, providing actionable insights through interactive dashboards to optimize pricing, inventory, and marketing strategies.
https://github.com/tanyagarg25/fashion-industry-analysis-trends-insights-and-performance

azure database datamodeling erd forms powerautomate sql tableau

Last synced: 19 days ago
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The Fashion Industry Analysis project uses Azure SQL and Tableau to analyze sales trends and customer behavior, providing actionable insights through interactive dashboards to optimize pricing, inventory, and marketing strategies.

• Host: GitHub
• URL: https://github.com/tanyagarg25/fashion-industry-analysis-trends-insights-and-performance
• Owner: tanyagarg25
• Created: 2024-12-16T23:47:42.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-12-20T23:03:45.000Z (4 months ago)
• Last Synced: 2025-02-13T16:36:21.963Z (2 months ago)
• Topics: azure, database, datamodeling, erd, forms, powerautomate, sql, tableau
• Homepage: https://public.tableau.com/views/FashionIndustryAnalysis/Story1?:language=en-GB&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link
• Size: 5.86 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Fashion-Industry-Analysis

## **Executive Summary**

**Objective:**

This project aimed to analyze the fashion industry’s sales performance using a combination of Azure SQL for database management and Tableau for visual analytics. By leveraging a relational data model and creating interactive dashboards, the objective was to uncover key trends, identify business challenges, and propose innovative strategies to drive growth.

**Context:** 

A sample dataset from open sources was integrated with a custom data collection form to create a relational data model. The database was hosted on Azure SQL, while Microsoft Forms and Power Automate facilitated real-time data entry and updates. Tableau dashboards provided actionable insights through visual storytelling, enabling strategic decision-making for business stakeholders.

## **Business Problem**

**Problem Identification:**

The fashion industry faces challenges in tracking sales performance, optimizing inventory, and understanding customer preferences. With scattered datasets and limited analytics capabilities, decision-making is often reactive rather than proactive.

**Business Impact:**

Inefficient data management and a lack of actionable insights hinder profitability and growth. A streamlined system for data integration, storage, and analysis is critical for identifying opportunities and addressing risks.

## **Data Modeling & Integration:**

**Developed a relational data model with two primary tables:**

**Sales Data Table:** 

Derived from a sample dataset, including fields like product category, sales amount, discounts, and customer segments.

**Custom Data Entry Table:** 

Designed to collect real-time feedback or additional sales data using Microsoft Forms, linked to the main dataset via shared keys.

Hosted the database in Azure SQL for secure and scalable data storage.

## **Data Collection:**

Used Microsoft Forms for new data entry, integrated with Azure SQL through Power Automate to enable seamless updates.

## **Visualization & Storytelling:**

* Built an interactive Tableau dashboard and storyboard to analyze sales trends, product performance, customer preferences, and regional variations.

* Designed KPIs and visualizations for quick diagnostic and exploratory analysis.

## **Languages & Tools**

Azure SQL, Microsoft Forms, Power Automate, Tableau

## **Results & Business Recommendations**

**Business Impact:**

* The project provided a centralized and structured approach to managing sales data, improving data accessibility and usability.

* Tableau dashboards allowed business stakeholders to quickly identify underperforming products, high-value customer segments, and regional sales patterns.

**Insights:**

* Key product categories with low profit margins were identified for targeted interventions.

* Regional sales data revealed discrepancies, indicating areas for focused marketing and inventory optimization.

* Customer segments showed varying responses to discounting strategies, providing opportunities to tailor promotions.

**Strategic Recommendations:**

* Revise pricing strategies for underperforming categories to improve profitability.

* Optimize inventory allocation based on regional sales trends to minimize stock-outs and overstock.

* Develop personalized marketing campaigns for high-value customer segments to increase engagement and loyalty.

## **Next Steps**

**Future Work:**

* Expand the relational data model to include additional data points, such as customer feedback and competitor benchmarking.

* Automate updates between Azure SQL and Tableau for real-time analytics.

* Incorporate advanced predictive models to forecast trends and enhance decision-making.

**Tableau Dashboard:** https://public.tableau.com/views/FashionIndustryAnalysis/Story1?:language=en-GB&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link