Customer Churn Analysis with Power BI and Python

🚨 Why do customers leave? Built a Data-Driven Churn Analysis to find out. I recently worked on a Customer Churn Analysis project to understand *why customers stop using a service* — and how businesses can reduce it. 🔍 What I did: • Cleaned and transformed raw customer data using Python (Pandas) • Analyzed churn patterns using SQL (joins, aggregations, segmentation) • Built an interactive Power BI dashboard to track churn metrics 📊 Key Metrics: • Overall Churn Rate • Churn by Contract Type • Churn by Monthly Charges • Customer Segmentation Insights 💡 Key Insights: • Customers on **month-to-month contracts churn ~3x more** than long-term users • Higher monthly charges are strongly correlated with churn • New customers (low tenure) have the highest churn risk ⚡ Business Impact: These insights can help businesses: • Improve retention strategies • Optimize pricing models • Target high-risk customers proactively 🛠 Tools Used: Python (Pandas) | SQL | Power BI 📌 Next Step: Planning to extend this by building a simple churn prediction model. Would love your thoughts and feedback! #DataAnalytics #Python #SQL #PowerBI #ChurnAnalysis #DataAnalyst #BusinessIntelligence

Excited to share my new Ecommerce Sales Dashboard project built using Excel + Python + Power BI 📊✨ This dashboard helps analyze: ✔ Total Sales – 25M ✔ Total Orders – 113K ✔ Total Quantity – 603K ✔ Average Order Value – 224.97 Key Insights Included: 🔹 Sales by Product Category 🔹 Orders by Customer Gender 🔹 Delivery Type Analysis 🔹 Sales by Location 🔹 Sales Trend Over Time 🔹 Sales by Zone This project helped me improve my skills in: • Data Cleaning • Data Visualization • KPI Analysis • Dashboard Designing • Business Insights Generation Tools Used: 🔸 Excel 🔸 Python 🔸 Power BI I am continuously working on real-world analytics projects to improve my Data Analyst skills and build a strong portfolio. #PowerBI #Python #Excel #DataAnalytics #DataAnalyst #Dashboard #BusinessIntelligence #Ecommerce #LinkedInProjects #DataVisualization #PortfolioProject

📊 Customer Behavior Dashboard | Power BI + Python + SQL Project Excited to share my latest Customer Behavior Dashboard, where I combined Python, SQL, and Power BI to analyze customer purchasing behavior and generate actionable business insights. This project demonstrates how raw data can be cleaned, analyzed, and transformed into interactive dashboards to support data-driven decision-making. 🔍 Key Insights from the Dashboard: • 👥 3.9K Customers analyzed • 💰 $59.76 Average Purchase Amount • ⭐ 3.75 Average Review Rating • 🛍️ Clothing category generated the highest revenue • 📈 Young Adults contributed the highest sales among age groups • 📦 Interactive filters for Subscription Status, Gender, Category, and Shipping Type 🛠️ Tools & Technologies Used: • 🐍 Python (Data Cleaning, EDA using Pandas & Matplotlib) • 🗄️ SQL (Data querying & business insights extraction) • 📊 Power BI (Dashboard development & visualization) • ⚡ DAX (KPIs & calculated measures) • 🔄 Power Query (Data transformation) 📌 Key Skills Demonstrated: • Data Cleaning & Preprocessing • SQL-based Data Analysis • Exploratory Data Analysis (EDA) • Dashboard Design & Data Storytelling • Business Insights Generation 🔗 GitHub Repository: https://lnkd.in/gNaTHYba I’m actively building end-to-end data analytics projects to strengthen my portfolio. #PowerBI #Python #SQL #DataAnalytics #DataAnalyst #BusinesAnalysis #DataVisualization #learningjourney #PowerQuery #AnalyticsPortfolio #DataScience

I recently worked on a Customer Behavior Dashboard to analyze sales performance, customer trends, and product insights. Tools Used: Excel | Python | SQL | Power BI Key Insights: • A few top product categories contribute to the majority of overall revenue • Customers purchasing discounted items showed higher buying frequency • Certain categories generated high sales but lower profit margins • Revenue trends highlighted peak purchasing periods and seasonal demand What I Did: • Cleaned and transformed raw data using Python & SQL • Built Pivot Tables & Charts in Excel for initial analysis • Created KPIs like Total Sales, Revenue, and Category Performance • Designed an interactive Power BI dashboard with slicers for dynamic filtering • Visualized customer behavior and top-performing products. What I Learned: • End-to-end data analysis workflow (cleaning → analysis → visualization) • How to extract meaningful business insights from raw datasets • Building interactive dashboards for decision-making 📷 Dashboard preview attached below 🔗 GitHub: https://lnkd.in/g7VN3GeS #DataAnalytics #PowerBI #SQL #Python #Excel #DataAnalyst #PortfolioProject

New Project: Customer Shopping Behavior Analysis -- Analyzed 3,900 customer transactions using Python, SQL, and Power BI to identify patterns in: ✔ Customer segmentation ✔ Subscription behavior ✔ Product preferences ✔ Discount dependency ✔ Revenue drivers Key work completed: -- Cleaned and transformed raw data using Python -- Loaded data into PostgreSQL for business analysis -- Wrote SQL queries to solve business questions -- Built an interactive Power BI dashboard -- Provided strategic recommendations for retention and revenue growth -- One interesting finding: Subscribers showed stronger spending behavior than non-subscribers, indicating potential growth through loyalty programs. Tools Used: Python | PostgreSQL | Power BI Feedback from data professionals is welcome. #DataAnalytics #Python #SQL #PowerBI #BusinessIntelligence #DataAnalyst #AnalyticsPortfolio #Hiring #Data #DataAnalysis #Business

I’m excited to share my latest project – Retail Sales Analytics Dashboard 📊 Built an end-to-end analytics solution using SQL, Python, and Power BI to analyze sales performance and identify key business insights. 🔍 What I worked on: • Wrote SQL queries (joins, aggregations) to analyze retail sales data • Performed data validation and cleaning using Python • Built an interactive Power BI dashboard to track KPIs like revenue, profit, and category performance 💡 Key Insights: • Technology drives the highest revenue • Phones and Chairs are top-performing sub-categories • West region shows the highest profitability • Tables are consistently loss-making This project helped me strengthen my skills in data analysis, visualization, and business problem-solving. 🔗 View Project: (https://lnkd.in/gShc6umW) #DataAnalytics #SQL #PowerBI #Python #BusinessAnalytics #DataScience #grow #learn #Skills

🚀 Turning Data into Decisions & Impact Data isn’t just numbers—it’s the story behind every smart decision. As a Data Analyst, I focus on transforming raw data into clear, actionable insights that drive real business growth. 📊 Skilled in: • Excel for data cleaning & reporting • SQL for extracting meaningful insights • Power BI for interactive dashboards & visualization • Python for data analysis & automation I believe the true power of data lies in how effectively we can interpret and use it to solve problems, improve strategies, and create value. 💡 From data analysis to visualization and automation—my goal is simple: 👉 Turn complex data into smart decisions #DataAnalytics #Excel #SQL #PowerBI #Python #DataDriven #BusinessGrowth #Analytics

🚀 Customer Behaviour Analysis | Data Analytics Project I’m excited to share my latest end-to-end Data Analytics project, where I analyzed customer shopping data to uncover meaningful business insights. 🔍 Project Overview: This project focuses on understanding customer purchasing behavior, identifying trends, and helping businesses make data-driven decisions. 🛠️ Tools & Technologies: Python (Pandas, NumPy, Matplotlib, Seaborn) SQL Power BI 📊 Key Business Questions: Which product categories generate the highest revenue? What are the customer spending patterns? Are there any seasonal purchase trends? How can customer retention be improved? 📂 Project Highlights: Cleaned and analyzed raw data using Python to uncover meaningful patterns Used SQL to answer key business questions and derive insights Built an interactive Power BI dashboard to visualize trends and support decision-making 📈 Key Insights: Identified top-performing product categories driving maximum revenue Observed patterns in customer spending behavior Discovered trends across different customer segments Highlighted opportunities to improve customer retention Link :- https://lnkd.in/gaq4wmGj I would love to hear your feedback! #DataAnalytics #Python #SQL #PowerBI #DataScience #EDA #AnalyticsProject #BusinessInsights

Customer Shopping Behavior Dashboard Excited to share my latest Data Analytics project where I built an interactive Customer Shopping Behavior Dashboard using Python, SQL, and Power BI. 🔹Project : • Data Cleaning & Preprocessing using Python (handled missing values, formatting, transformations) • Data stored and queried using SQL Database • Solved business questions using SQL queries • Connected SQL to Power BI for visualization • Built an interactive dashboard with filters & insights 🔹Key Insights: • Total Customers: 3.9K+ • Average Purchase Amount: $59.76 • Subscription Analysis shows majority users are non-subscribers • Young Adults contribute highest revenue & sales • Category-wise performance analysis for better decision making 🔹Dashboard Features: • Dynamic filters (Gender, Category, Subscription, Shipping Type) • Revenue & Sales breakdown • Customer segmentation by Age Group • Interactive and user-friendly design 🔹Tools Used: Python | SQL | Power BI This project improved my data analysis, SQL, and visualization skills. #DataAnalytics #PowerBI #SQL #Python #DataVisualization #Dashboard

Hello Connections 👋 In the journey of a Data Analyst, tools like SQL, Power BI, Python, Tableau, and Excel play a crucial role in solving business problems and deriving insights. But beyond analysis, one of the most critical steps is data cleaning and transformation — and that’s where Power Query (Mashup Language) becomes a game changer. 1)It allows us to handle messy, real-world data efficiently 2)Helps standardize inconsistent formats (like phone numbers, emails, etc.) 3)Enables automation of repetitive data cleaning tasks 4)Improves data quality before it reaches dashboards and reports 5) Saves time and ensures reliable decision-making In this post, I’ve shared a simple yet powerful scenario where we clean and validate contact numbers using Mashup Language (M). Key takeaway: Strong data analysis starts with clean, structured, and reliable data — and mastering Power Query is a must-have skill for every data professional. #DataAnalytics #PowerQuery #DataCleaning #BusinessIntelligence #PowerBI #Excel #DataTransformation #AnalyticsJourney