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.
- 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.
- Power BI: For data visualization and interactive dashboard creation.
- Excel: Used for initial data cleaning and manipulation.
- Data Source: ONYX Data - DataDNA Dataset.
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.
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.
- Download the
.pbixfile and open it in Power BI. - View the Dashboard: Explore the interactive visualizations to get insights into the campaign performance.
- Adjust filters: Use Power BI filters to dive deeper into specific segments of the data.
- 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.
This project is licensed under the MIT License - see the LICENSE file for details.
- LinkedIn Post: Project Overview
- Contact: Sunny Kumar