Marketing Analytics Project

Project Demo & Key Insights

Overview:

Key Insights at a Glance:

• Conversion Rate Fluctuations: Conversion rates varied throughout the year, with peaks in January (18.5%) and lows in May (4.3%). Seasonal trends influence purchase behavior, indicating that marketing efforts should be aligned with demand cycles.

• Customer Engagement Challenges: Views and interactions with marketing content declined from August onwards, suggesting a need for stronger engagement strategies such as interactive content and optimized call-to-actions.

• Customer Sentiment Analysis: While the majority of customer reviews are positive (4-5 stars), the average rating of 3.7 indicates room for improvement. Addressing negative feedback (from 1-2 star reviews) could improve overall satisfaction.

• Click-Through Rate Efficiency: Despite declining engagement, the click-through rate remains at 15.37%, showing that engaged users are still interacting effectively. Enhancing content strategy could boost total engagement.

• Product-Specific Trends: Some products, like Ski Boots, had exceptional conversion rates (150%), highlighting the impact of seasonality and targeted marketing.

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Overview

This project combines robust data processing, sentiment analysis, and interactive data visualizations to provide actionable insights for marketing strategies. By integrating multiple data sources, the project supports:

• Interactive Reporting: A Power BI dashboard (Dashboard.pbix) that visualizes key marketing metrics.
• Data Preparation: SQL scripts and Python code to process and analyze raw data.
• Strategic Insights: Comprehensive analysis of customer sentiment and campaign performance to drive informed decision-making.

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How to Use

Prerequisites

• Power BI Desktop: To open and interact with Dashboard.pbix.
• SQL Server: To restore and explore the MarketingAnalyticsData.bak database.
• Python 3.x: To run the sentiment analysis script. Ensure you have the necessary libraries installed (e.g., pandas, nltk, etc.).

Installation & Setup

1. Clone the Repository:

   git clone https://github.com/your-username/your-repo.git

2. Database Setup:
   • Restore the database from Data/MarketingAnalyticsData.bak using SQL Server.
3. Dashboard:
   • Open Dashboard.pbix in Power BI Desktop to view interactive visualizations.
4. Run Sentiment Analysis:
   • Execute the Python script:

     python Scripts/CustomerReviewSentimentAnalysis.py

   • This script processes Data/CustomerReviewSentiment.csv and outputs sentiment scores for further analysis.

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Project Snippets & Detailed Insights

Project Snippets

# Example snippet from CustomerReviewSentimentAnalysis.py
import pandas as pd
import nltk

# Load customer reviews
df = pd.read_csv('Data/fact_customer_reviews_with_sentiment.csv')
# Perform sentiment analysis...

Detailed Insights from Data Analysis

• Seasonal Impact on Conversion Rates: Conversion rates peaked in January at 18.5%, largely driven by winter-related products like Ski Boots, which had an impressive 150% conversion rate. However, May had the lowest conversion rate at 4.3%, indicating the need for strategic promotions or seasonal marketing adjustments during weaker months.

• Customer Sentiment and Its Impact on Sales: The majority of customer reviews were positive (4-5 stars), but the overall average rating remained at 3.7, below the target of 4.0. Addressing recurring negative feedback (e.g., low-rated products) could improve sales and customer retention. Products rated below 3.5 saw lower engagement, showing a direct link between sentiment and conversion rates.

• Engagement Drop in Late Year: Engagement metrics declined steadily after July, with views and interactions dropping from August onward. Click-through rates remained at 15.37%, meaning engaged users were still interacting, but overall traffic was down. This suggests the need for more engaging content formats, optimized call-to-actions, and targeted campaigns in Q4.

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Folder Structure

Project_MarketingAnalytics/
├── Dashboard.pbix                     # Power BI dashboard 
├── Data/
│   ├── fact_customer_reviews_with_sentiment.csv  # Processed Customer review
│   └── MarketingAnalyticsData.bak     # Database backup
├── Docs/
│   ├── Marketing Analytics Business Case.pptx  # Business case presentation
│   └── Presentation.pptx                   # Additional presentation slides
└── Scripts/
    ├── Calendar DAX Script.txt             # DAX script for time intelligence calculations
    ├── CustomerReviewSentimentAnalysis.py  # Sentiment analysis script
    └── SQL/                                # SQL Queries    

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Additional Information

• Future Enhancements: Planned improvements include integrating real-time data feeds and expanding sentiment analysis capabilities.
• Contributing: Contributions are welcome! Please open an issue or submit a pull request with your suggestions.
• References:
  • Power BI Documentation
  • Python Sentiment Analysis Libraries

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Contact

For questions, suggestions, or further discussion, please contact:
Your Name – your-email@example.com