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RetainCLM - AI Customer Lifecycle Management
Predict churn, understand customer behavior, and automate retention with AI.
Project description
RetainCLM is an AI-powered Customer Lifecycle Management platform designed to help businesses understand customer behavior, predict churn, and take action before customers leave.
I designed the product around the complete customer lifecycle, combining customer intelligence, machine-learning-driven churn prediction, segmentation, retention analytics, and automated retention workflows in one connected platform.
The system transforms customer data into actionable intelligence, helping teams identify at-risk customers, understand lifecycle movement, automate retention actions, and make more informed decisions around customer growth and recurring revenue.
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Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
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I analyzed 3M+ job postings across 161 countries and 93K companies to identify hiring trends, in-demand roles, work-mode patterns, and salary insights.
I used Python for data cleaning and preprocessing, SQL for analysis, and Power BI to build an interactive dashboard.
Key areas analyzed:
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This project helped me turn a large dataset into clear, actionable insights for understanding the global job market.
AI-Assisted Sales Intelligence Dashboard
Project Overview
This project is an end-to-end Sales Analytics solution built using Python, SQL, and Power BI.
The workflow includes:
Data Cleaning using Python (Pandas)
Data Storage using SQLite
Business Analysis using SQL Queries
Interactive Dashboard using Power BI
Automated Business Report Generation using Python
The goal of this project is to analyze sales performance, identify profitable products, monitor regional performance, and generate business recommendations.
Dataset
Dataset: Sample Superstore Dataset
Records: 9,994
Features:
Order Details
Customer Information
Product Information
Sales
Profit
Discount
Region
Category
Sub-Category
Tools & Technologies
Python
Pandas
SQLite
SQL
Power BI
Data Visualization
Business Intelligence
Project Workflow
CSV Dataset
↓
Data Cleaning (Python)
↓
SQLite Database
↓
SQL Analysis
↓
Power BI Dashboard
↓
AI Business Report
Dashboard Pages
Executive Overview Total Sales Total Profit Total Orders Profit Margin Regional Sales Analysis Category Performance Analysis
Product Performance Analysis Top Profitable Products Loss-Making Products Profit by Category Sales by Sub-Category
Sales Trends Analysis Sales Trends Over Time Profit Trends Seasonal Performance Insights Key Business Insights West Region generated the highest sales. Technology is the most profitable category. Several products generate significant losses and require pricing review. Sales performance varies significantly across regions. Automated AI Report The project includes a Python-based reporting system that automatically generates:
Total Sales
Total Profit
Business Insights
Strategic Recommendations
Author
Gourav Jangid
Aspiring Data Analyst
Open to Data Analytics Opportunities
Retail Sales Data Analysis — SQL
A SQL-based data analysis project focused on exploring retail sales data and extracting meaningful business insights from transactional records.
Key areas covered:
• Data exploration and preparation
• Sales and revenue analysis
• Product and category performance
• Customer and transaction analysis
• Aggregation using GROUP BY and HAVING
• Filtering and conditional analysis using WHERE and CASE WHEN
• JOINs and subqueries
• CTEs for structured analysis
• Window functions for ranking and comparative analysis
The project demonstrates how SQL can be used to transform transactional data into meaningful insights and answer practical business questions.
