https://github.com/omarzain27/marketing-analysis-project
ShopEasy Marketing Analysis 📊: A job simulation analyzing customer engagement and conversion rates for an online retailer. Uses MS SQL Server for EDA, Python (NLTK) for sentiment analysis, and Power BI for interactive KPI dashboards. Delivers actionable insights to boost marketing strategies. 🚀
https://github.com/omarzain27/marketing-analysis-project
data-analytics kpis powerbi python3 sqlserver
Last synced: about 1 month ago
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ShopEasy Marketing Analysis 📊: A job simulation analyzing customer engagement and conversion rates for an online retailer. Uses MS SQL Server for EDA, Python (NLTK) for sentiment analysis, and Power BI for interactive KPI dashboards. Delivers actionable insights to boost marketing strategies. 🚀
- Host: GitHub
- URL: https://github.com/omarzain27/marketing-analysis-project
- Owner: omarzain27
- Created: 2025-05-27T13:00:13.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-27T14:03:16.000Z (about 1 year ago)
- Last Synced: 2025-06-25T07:42:53.466Z (about 1 year ago)
- Topics: data-analytics, kpis, powerbi, python3, sqlserver
- Language: Python
- Homepage:
- Size: 2.92 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📊 ShopEasy Marketing Analysis Project 🚀
Welcome to the **ShopEasy Marketing Analysis Project**, a job simulation designed to tackle real-world business challenges through data-driven insights! This project aims to analyze and enhance ShopEasy's marketing strategies to boost customer engagement and conversion rates. 🛍️
## 📋 Project Overview
**ShopEasy**, an online retail business, is facing challenges with declining customer engagement and conversion rates despite heavy investments in marketing campaigns. This project conducts a comprehensive analysis to identify areas for improvement and provide actionable recommendations to the Marketing Manager and Customer Experience Manager. 🎯
### Business Problem 🛠️
ShopEasy is grappling with:
- **Reduced Customer Engagement**: Fewer interactions with the website and marketing content. 😕
- **Decreased Conversion Rates**: Lower percentage of visitors converting to paying customers. 📉
- **High Marketing Expenses**: Significant campaign costs with suboptimal returns. 💸
- **Need for Customer Feedback Analysis**: Understanding customer sentiments to improve engagement and conversions. 🗣️
## 🛠️ Methodology
This project follows a structured, data-driven approach to uncover insights and drive impactful recommendations:
1. **Exploratory Data Analysis (EDA) & Data Cleaning with MS SQL Server** 🗄️
- Connected to the ShopEasy database using MS SQL Server.
- Performed EDA to identify trends, patterns, and anomalies in customer and campaign data.
- Cleaned data by removing duplicates, handling missing values, and standardizing formats to ensure data integrity.
2. **Sentiment Analysis of Customer Reviews with Python (NLTK)** 🧠
- Utilized Python and the NLTK library to perform sentiment analysis on customer reviews.
- Classified reviews as positive, negative, or neutral to gauge customer opinions.
- Identified key themes in feedback to inform marketing improvements.
3. **Interactive Power BI Dashboard with DAX Measures** 📈
- Built an interactive Power BI dashboard connected to the SQL Server database.
- Created DAX measures to calculate key performance indicators (KPIs) such as:
- Conversion Rate
- Customer Engagement Score
- Campaign ROI
- Visualized trends and insights for stakeholder decision-making.
4. **Stakeholder Presentation with Goals & Recommendations** 📽️
- Developed a compelling presentation for the Marketing Manager and Customer Experience Manager.
- Shared clear goals, data-driven insights, and actionable recommendations to enhance engagement and conversions.
- Highlighted key findings from EDA, sentiment analysis, and KPIs with visually engaging charts.
## 📽️ Presentation
Explore the full analysis in our [Marketing Analysis Presentation](https://github.com/omarzain27/Marketing-Analysis-Project/blob/main/Marketing%20Analysis%20Presentation.pdf), designed for stakeholders to review key findings and recommendations. Below is a preview of the title slide:
[View Persentation](https://github.com/omarzain27/Marketing-Analysis-Project/blob/main/Marketing%20Analysis%20Presentation.pdf)
## 🖼️ Dashboard Screenshots
Below are key visuals from the Power BI dashboard, showcasing critical KPIs and insights for ShopEasy’s marketing strategy:
- **Conversion Rate Trends** 📉
Displays monthly conversion rates, highlighting peaks (e.g., 18.5% in January) and lows (e.g., 4.3% in May) to guide targeted campaigns.
- **Engagement Metrics** 📊
Visualizes views, clicks, and likes by content type, with blog content leading in April and July, revealing engagement trends.
- **Sentiment Analysis** 🧠
Shows distribution of customer review sentiments (275 positive vs. 82 negative), supporting feedback-driven improvements.
## 🎯 Goals & Deliverables
- **Understand Customer Behavior**: Identify factors driving low engagement and conversions.
- **Optimize Marketing Strategies**: Provide recommendations to improve campaign effectiveness and ROI.
- **Enhance Customer Experience**: Use sentiment analysis to address customer pain points.
- **Interactive Insights**: Deliver a Power BI dashboard for stakeholders to explore KPIs in real time.
- **Actionable Recommendations**: Present clear, data-backed strategies to boost ShopEasy’s performance.
## 🛠️ Tools & Technologies
- **MS SQL Server**: For EDA and data cleaning.
- **Python (NLTK, Pandas)**: For sentiment analysis and data processing.
- **Power BI (DAX)**: For interactive dashboards and KPI visualization.
- **Presentation Tools**: For stakeholder communication (e.g., PowerPoint). 📊
## 📝 Recommendations
Based on the analysis, key recommendations include:
- **Refine Campaign Targeting**: Focus on high-engagement customer segments identified in EDA.
- **Address Negative Sentiments**: Improve product features or customer service based on review themes.
- **Optimize Budget Allocation**: Shift marketing spend to channels with higher ROI, as shown in the Power BI dashboard.
- **Enhance User Experience**: Streamline website navigation to boost conversion rates.
## 🚀 Next Steps
- Share the Power BI dashboard and presentation with stakeholders.
- Implement recommended changes and monitor KPIs for improvement.
- Iterate on sentiment analysis to track changes in customer feedback over time.
## 🙌 About Me
I’m Omar Zain, a passionate Data Analytics Engineer with expertise in Python, SQL, and Power BI. I’m ready to bring data-driven solutions to real-world challenges. Connect with me on [LinkedIn](https://linkedin.com/in/omar-zain-802341168) or check out my projects on [GitHub](https://github.com/omarzain27)! 🌟
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*This project is a simulation created to demonstrate data analysis skills for a marketing analytics role.* 🧑💼