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https://github.com/sunnybibyan/marketing_campaign_analysis_power_bi_dashboard

Campaign Performance Analysis This project analyzes the performance of Spring, Summer, and Fall marketing campaigns, revealing key insights and actionable recommendations.
https://github.com/sunnybibyan/marketing_campaign_analysis_power_bi_dashboard

data-analysis data-visualization dax marketing-campaign powerbi

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Campaign Performance Analysis This project analyzes the performance of Spring, Summer, and Fall marketing campaigns, revealing key insights and actionable recommendations.

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README 

        
# Marketing Campaign Analysis Power BI Dashboard



## Overview

This project analyzes the performance of the **Spring**, **Summer**, and **Fall** marketing campaigns, revealing key insights and actionable recommendations to optimize future marketing efforts.



The analysis is done using Power BI, focusing on campaign performance, target audience engagement, and conversion rates.



![Campaign Analysis Dashboard](assets/Dashboard.png)



## Key Features

- **Data Sources**: This analysis uses a cleaned dataset from the ONYX Data - DataDNA Dataset Challenge.

- **Visualizations**: Includes interactive charts and graphs for campaign performance across different seasons.

- **Metrics**: Focuses on key marketing metrics such as conversion rates, cost per acquisition (CPA), and customer lifetime value (CLV).

- **Actionable Insights**: Provides recommendations based on campaign performance.



## Tools & Technologies

- **Power BI**: For data visualization and interactive dashboard creation.

- **Excel**: Used for initial data cleaning and manipulation.

- **Data Source**: ONYX Data - [DataDNA Dataset](https://www.linkedin.com/posts/sunny-bibyan_datadna-builtwithzoomcharts-datacleaning-activity-7209298860601401344-cIxq?utm_source=share&utm_medium=member_desktop).



## Dataset Information

The dataset contains the following key columns:

- **Campaign ID**: Unique identifier for each campaign.

- **Start Date / End Date**: Duration of the campaign.

- **Impressions**: The total number of times the campaign was viewed.

- **Clicks**: The number of times users clicked on the ad.

- **Conversions**: Actions taken by users (e.g., purchases, sign-ups).

- **Cost**: Total amount spent on the campaign.



The data was cleaned using Excel for outlier removal, missing values handling, and normalization.



## Visualizations

Here are some of the visualizations included in the dashboard:

- **Conversion Funnel**: Visualizes the journey from impressions to conversions.

- **Campaign Performance Comparison**: Bar charts comparing the success of each campaign (Spring, Summer, Fall).

- **Cost vs. Conversion**: Scatter plots analyzing how campaign costs relate to conversions.

- **Customer Segmentation**: Pie charts showing customer demographics across different campaigns.



## Project Structure



## How to Use the Project

1. **Download the `.pbix` file** and open it in Power BI.

2. **View the Dashboard**: Explore the interactive visualizations to get insights into the campaign performance.

3. **Adjust filters**: Use Power BI filters to dive deeper into specific segments of the data.



## Insights and Recommendations

- **High Engagement in Summer Campaign**: The summer campaign had the highest click-through rate (CTR) but also the highest cost per conversion.

- **Optimizing for ROI**: Based on conversion data, the Fall campaign had the lowest cost per conversion, making it the most cost-effective.

- **Audience Targeting**: Younger demographics responded better to the Spring campaign, suggesting more focus on this audience in future efforts.



## License

This project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.



## Connect with Me

- **LinkedIn Post**: [Project Overview](https://www.linkedin.com/posts/sunny-bibyan_datadna-builtwithzoomcharts-datacleaning-activity-7209298860601401344-cIxq?utm_source=share&utm_medium=member_desktop)

- **Contact**: [Sunny Kumar](mailto:[email protected])