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Email Marketing Analysis

Overview

This project focuses on analyzing customer engagement through email marketing campaigns. Using data on sign-ups, activity levels, and demographic attributes, I created an interactive Power BI dashboard to uncover trends and identify opportunities for targeted marketing strategies. This analysis demonstrates my ability to work with customer data, identify actionable insights, and optimize marketing efforts.

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Datasets Used

  1. Email Marketing Analysis.xlsx:

    • Includes details of email marketing campaign responses such as:

      • Sign-up dates, activity levels, and demographic details (gender, marital status, living status).

      • Geographic attributes like state and city.

      • Time-based patterns of enrollment.

  2. Supplementary Data:

    • Additional segmentation metrics to identify behavioral patterns and campaign effectiveness.

What I Did?

  1. Data Cleaning and Transformation:

    • Standardized demographic data to ensure consistency (e.g., uniform state and city names).

    • Segmented users based on attributes like marital status, activity levels, and enrollment timing.

    • Calculated metrics for active and inactive users to assess engagement rates.

  2. Analysis and Insights:

    • Analyzed sign-up trends by year, time, and location to identify peak enrollment periods.

    • Segmented users by demographic attributes:

      • Gender: Majority of sign-ups were male (6764).

      • Marital Status: Married individuals dominated (9418 sign-ups).

      • Living Status: 48.02% of sign-ups were from households with children.

    • Explored geographic distribution:

      • Telangana had the highest sign-ups (46.63%).

      • Cities like Hyderabad and Secunderabad showed strong engagement.

  3. Dashboard Design in Power BI:

    • Created an interactive dashboard with the following features:

      • Demographic Analysis:

        • Visualized sign-ups by gender, marital status, and living conditions.

      • Geographic Insights:

        • Mapped sign-ups by state and city, highlighting hotspots for email engagement.

      • Time-Based Trends:

        • Bar charts showing yearly and daily enrollment patterns.

        • Peak activity was identified during specific times (e.g., 6:00 AM and 6:00 PM).

    • Added slicers to filter data dynamically by state, activity level, and year.

What I Learned?

  1. Technical Skills:

    • Gained expertise in data modeling, DAX calculations, and visualization techniques in Power BI.

    • Learned to optimize dashboards for usability and performance.

  2. Marketing Insights:

    • Improved understanding of customer segmentation and behavior analysis.

    • Recognized the importance of time and geographic factors in targeting email campaigns.

  3. Data Storytelling:

    • Enhanced ability to communicate key insights visually and interactively.

Key Highlights

  1. Demographic Insights:

    • Gender: 70% of sign-ups were male, followed by 30% female.

    • Marital Status: Married individuals dominated sign-ups, indicating strong engagement from this group.

    • Living Status: Nearly half of the sign-ups came from individuals with children.

  2. Geographic Insights:

    • Telangana accounted for nearly half (46.63%) of the total sign-ups.

    • Hyderabad and Secunderabad were top cities, with 2.6K and 2.1K sign-ups, respectively.

  3. Activity Trends:

    • A significant number of users were active (9654 vs. 15 inactive users).

    • Peak sign-ups occurred in 2018 (4.8K), indicating a successful campaign year.

  4. Visual Appeal:

    • Interactive charts and maps provide clear, actionable insights for marketing teams.

Next Steps

  1. Extend the analysis by incorporating campaign-specific performance metrics (e.g., open rates, click-through rates).

  2. Use predictive analytics to forecast future engagement trends.

  3. Explore deeper segmentation to refine targeting strategies for email campaigns.

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