Data Analytics Project: Customer Shopping Behavior Analysis

📊 I analyzed 3,900+ customer transactions… and found some surprising insights 👇 As part of my Data Analytics journey, I worked on a Customer Shopping Behavior Analysis project using Python, PostgreSQL (SQL), Power BI, and Jupyter Notebook. 🔍 What I did: • Cleaned and processed raw data using Python (Pandas, NumPy) • Performed Exploratory Data Analysis (EDA) in Jupyter Notebook • Wrote advanced SQL queries in PostgreSQL to extract business insights • Built an interactive Power BI dashboard for data visualization 💡 Key Insights: • Repeat customers contributed significantly higher revenue • Certain products were highly dependent on discounts • Subscribers had higher average spending 📈 This project helped me understand how data analytics can drive real business decisions, improve customer retention, and optimize revenue strategies. 🔗 Project Link in Comments 👇 #DataAnalytics #DataAnalyst #Python #SQL #PostgreSQL #PowerBI #JupyterNotebook #EDA #DataVisualization #OpenToWork #FresherJobs

✅ Project Completed: Customer Shopping Behavior Analysis Successfully analyzed 3,900+ customer transactions to extract valuable business insights using Python, SQL, and Power BI. 📊 Built an interactive dashboard & identified key trends in customer behavior, revenue, and product performance. 🛠 Tech Stack: Python | SQL | Power BI Looking forward to applying these skills in real-world data analytics roles 🚀 #DataAnalytics #PowerBI #SQL #Python #Projects #OpenToWork

4 tools I use daily as a Data Analyst: 1️⃣ SQL — to extract exactly what the business needs 2️⃣ Power BI & Looker Studio — to turn data into clear visual insights 3️⃣ Python — to automate repetitive tasks and save time 4️⃣ Excel & Google Sheets — to clean, organize, and present data efficiently These tools cover 90% of real-world data analyst work. Which tool do you find most valuable? 👇 #DataAnalyst #SQL #PowerBI #Python #Excel #DataAnalytics #Analytics #OpenToWork

From raw customer data to meaningful business insights! I’m excited to share my latest Customer Behavior Data Analytics Project, where I followed the complete analytics workflow from data cleaning to dashboard storytelling. Tools Used: Python(Pandas) | SQL (MySQL) | Power BI Here’s what I worked on: - Loaded and explored the dataset using Python; - Performed EDA and handled missing values; - Used SQL / MySQL to answer real business questions; - Designed an interactive Power BI dashboard for decision-making; What made this project exciting was not just creating visuals, but understanding the story behind customer purchases: - Which product categories perform best; - Which customers spend the most; - How buying behavior changes over time; - Trends that can improve marketing decisions; This project improved my practical skills in Python, SQL, and Power BI, while also helping me think more like a business-focused data analyst. Thanks to Amlan Mohanty for providing this amazing Data Analytics project Github Link:https://lnkd.in/db3K6hGy I’d love to hear your feedback and suggestions! #DataAnalytics #Python #SQL #MySQL #PowerBI #EDA #DashboardDesign #BusinessIntelligence #CustomerAnalytics #DataAnalyst #OpenToWork

Most beginner data analysts focus on tools. SQL. Tableau. Python. But businesses don’t care about tools. They care about answers that make or save money. While working on a dataset recently, I realized something interesting: 👉 The biggest problem isn’t always what everyone tracks. For example: • Companies obsess over customer churn • But often ignore failed payments, drop-offs, or hidden leaks And those can quietly cost more than churn itself. That’s when it clicked for me: Data analysis isn’t about dashboards. It’s about finding what’s being missed. I’m now focusing my projects on: • Revenue leaks • Customer behavior • Decision-making insights Not just visuals. If you’re learning data analytics, try this shift: ➡️ Don’t just ask “what can I show?” ➡️ Ask “what is this data hiding?” #DataAnalytics #SQL #Tableau #BusinessIntelligence #OpenToWork

Hi LinkedIn, I’ll be honest, I was lowkey skeptical to start my first end-to-end business data analytics project. It felt overwhelming… Python, SQL, Power BI dashboards, Business Reporting - all in one project? But I finally pushed myself to do it. And now? I feel way more confident. I built a Customer Shopping Behavior Analysis project where I: Cleaned and analyzed 3,900+ transactions Wrote SQL queries to extract real business insights Built an interactive Power BI dashboard Turned raw data into actual recommendations Some insights I found: Subscribers spend significantly more (📈 +68%) Express shipping customers spend more per order A small % of loyal customers drive high value This project taught me that data isn’t just numbers - it’s decisions. Still learning, but proud of this step. Would love feedback or advice from the data community 🙌 Special thanks to Amlan Mohanty for the guidance during this project. #DataAnalytics #SQL #Python #PowerBI #OpenToWork

🚀 Starting a New Data Analytics Project: Delivery & Delay Analysis 📌 Problem: Delivery delays can impact customer satisfaction and business performance. I wanted to understand what factors contribute to delays and how they can be reduced. 📊 What I plan to do: * Analyze delivery dataset to identify delay patterns * Clean and prepare data for analysis * Use SQL/Python to explore key trends * Build a dashboard to visualize insights 🎯 Objective: Identify key reasons for delivery delays and provide actionable insights to improve efficiency. 🛠 Tools I’ll be using: SQL | Python | Power BI / Excel I’ll be sharing my progress and insights as I move forward. #DataAnalytics #SQL #Python #Learning #OpenToWork

📊 Customer Churn Analysis Project 🚀 I recently completed an end-to-end data analysis project to understand customer churn behavior and identify key factors affecting retention. 🔍 Using Python for data cleaning and exploratory analysis, and Tableau for visualization, I uncovered several important insights: • 📉 26.54% of customers churned  • ⚡ Customers with month-to-month contracts showed the highest churn  • 💳 Electronic check users had higher churn rates  • ⏳ Customers in early tenure (0–10 months) were most likely to leave 👉 Key takeaway: Customer churn is highest in the early lifecycle stage, making onboarding and early engagement critical for retention. 📈 I also built an interactive Tableau dashboard to visualize these insights and make them actionable. 🔗 GitHub Repository:  https://lnkd.in/dtGMs6Gz 🔗 Tableau Dashboard:  https://lnkd.in/dR4XnfzM I would love to hear your feedback! #DataAnalytics #Tableau #Python #DataScience #EDA #BusinessAnalytics #OpenToWork

Excited to share my latest Data Analytics Project: Customer Shopping Behavior Analysis 📊 In this project, I worked on: ✔️ Data cleaning & EDA using Python ✔️ SQL queries for business insights ✔️ Interactive Power BI dashboard ✔️ End-to-end analytics workflow Key insights: 🔹 Identified high-value customer segments 🔹 Discovered top-performing product categories 🔹 Analyzed purchasing trends This project helped me strengthen my skills in Python, SQL, and Power BI. Looking forward to feedback and opportunities in Data Analytics 🚀 #DataAnalytics #Python #SQL #PowerBI #DataScience #Projects #OpenToWork@

I gave myself a goal: Go from raw, messy customer data to a business-ready dashboard. No shortcuts, just the full workflow. So I built an end-to-end *Customer Behavior Analysis Project* using Python, SQL, and Power BI. Started with unstructured data, cleaned it using Pandas, explored patterns through EDA, and used SQL to answer business questions before finally visualizing everything in Power BI. 📊 What stood out: * ~73% customers are non-subscribers → Huge opportunity for conversion strategies * Clothing category drives the highest revenue → Clear focus area for inventory and marketing * Younger and middle-aged customers contribute the most revenue → Contrary to the assumption that older, loyal customers dominate * Avg purchase value is ~$59.76 → Useful baseline for pricing and upselling decisions The biggest surprise? Younger customers were driving more revenue than expected. This project made one thing clear: clean data + the right questions = real business insights. 🔗 Project: https://lnkd.in/dg7pnhSi #DataAnalytics #Python #SQL #PowerBI #LearningInPublic #OpenToWork