ShopEasy, an online retail business, faced declining customer engagement and conversion rates despite significant marketing investments. The goal of this project was to analyze marketing performance using SQL, Python, and Power BI to provide data-driven recommendations for improving customer engagement, optimizing conversion rates, and enhancing customer satisfaction.
- 📈 Analyze Conversion Trends: Identify drop-off points in the customer journey and suggest improvements.
- 💡 Enhance Customer Engagement: Evaluate marketing content performance and recommend content strategies.
- 😊 Improve Customer Sentiment: Extract insights from customer reviews to enhance satisfaction.
- 🔍 Data-Driven Decision Making: Provide actionable insights through SQL, Python (NLTK), and Power BI dashboards.
- 🗄 SQL Server: Data extraction, cleaning, and transformation.
- 🐍 Python (pandas, NLTK, pyodbc): Sentiment analysis and customer segmentation.
- 📊 Power BI: Data visualization and interactive dashboard creation.
- ⚙️ SQL Queries: Used for data manipulation, joins, and aggregations.
- Extracted data from SQL Server using optimized queries.
- Removed duplicates and handled missing values to ensure data integrity.
- Standardized formats for consistency (date formats, text casing, and structured categories).
- Joined multiple tables (fact_customer_reviews, orders, customers) using
JOINsto create a unified dataset. - Applied CTEs and Window Functions to rank customers based on spending and review frequency.
- Used NLTK’s VADER sentiment analysis to categorize customer reviews as Positive, Neutral, or Negative.
- Cleaned text data by removing stopwords, special characters, and normalizing text.
- Performed customer segmentation based on sentiment scores and ratings.
- Analyzed correlations between customer reviews and conversion trends to identify improvement areas.
- Developed interactive dashboards to showcase key marketing trends.
- Created a Conversion Funnel chart to identify drop-off points in the customer journey.
- Visualized customer engagement trends using heatmaps and time-series graphs.
- Displayed sentiment score distributions to help understand customer satisfaction trends.
- Presented insights to stakeholders with clear, data-driven recommendations.
Why? Raw data often contains inconsistencies, missing values, and redundancies. Cleaning the data ensures accuracy and reliability for analysis.
✅ Extracted data from SQL Server using optimized queries.
✅ Removed duplicates and handled missing values to maintain integrity.
✅ Standardized formats like dates, text casing, and structured categories.
✅ Joined multiple tables (fact_customer_reviews, orders, customers) using JOINs to unify datasets.
✅ Used CTEs and Window Functions to rank customers based on spending and review frequency.
Why? Understanding customer sentiment helps in identifying key pain points and areas for improvement.
✅ Used NLTK’s VADER sentiment analysis to categorize customer reviews as Positive, Neutral, or Negative. ✅ Cleaned text data by removing stopwords, special characters, and normalizing text. ✅ Performed customer segmentation based on sentiment scores and ratings. ✅ Analyzed correlations between customer sentiment and conversion trends to pinpoint major issues.
Why? Clear, interactive dashboards help stakeholders quickly understand trends and make informed decisions.
✅ Developed interactive Power BI dashboards showcasing key marketing insights. ✅ Created a Conversion Funnel chart to identify drop-off points in the customer journey. ✅ Visualized customer engagement trends using heatmaps and time-series graphs. ✅ Displayed sentiment score distributions to highlight satisfaction trends. ✅ Provided actionable recommendations based on data-driven insights.
- 📉 Conversion Rate Trends: Identified that conversion rates were lowest in May (4.3%) and highest in January (18.5%), indicating seasonal influences.
- 🎯 Customer Engagement: Click-through rates (15.37%) suggested that while overall engagement declined, users who interacted were still highly engaged.
- 😊 Customer Sentiment: 275 positive reviews highlighted strengths, while 82 negative reviews indicated concerns around delivery times and product quality.
- 🔍 Content Performance: Blog content performed better than social media, suggesting a need to optimize social content strategies.
A detailed breakdown of overview using Power BI:
A detailed breakdown of Conversion Rate using Power BI:
A detailed breakdown of Customer Analysis using Power BI:
A detailed breakdown of SocialMedia Analysis using Power BI:
- Increase Conversion Rates: Introduce personalized promotions and discounts during historically low-performing months.
- Enhance Customer Engagement: Implement interactive content (videos, user-generated content, and quizzes) to increase engagement rates.
- Improve Customer Feedback Scores: Address frequent complaints regarding delivery times and product quality by refining logistics and product control measures.
- Optimize Marketing Strategies: Focus ad spend on high-performing content types and enhance call-to-action placements.
- 📌 Informed Decision-Making: Provided data-driven insights to optimize marketing strategies.
- 📌 Higher Engagement: Suggested interactive content strategies to boost user interactions.
- 📌 Increased Conversions: Recommended seasonal targeting and better checkout experiences to improve sales.
- 📌 Enhanced Customer Satisfaction: Proposed solutions for common complaints, leading to a potential increase in ratings and customer loyalty.
This project successfully leveraged SQL, Python, and Power BI to analyze marketing performance and derive actionable insights. By addressing key challenges like low conversion rates, declining engagement, and customer sentiment issues, we developed a data-driven approach to optimize marketing strategies. The insights and recommendations provided have the potential to drive higher customer satisfaction, better engagement, and increased revenue for ShopEasy. 🚀