Amazon Marketing-Performance-Analysis

Data Preparation and Dashboard Creation:

We began by importing the data from three CSV files - amazon_marketing_stream_data, product_mapping, and product_description - into Power BI. The data was then cleaned and transformed to handle missing values, duplicates, and inconsistencies. The datasets were merged and relationships were established between relevant tables.

The Power BI dashboard was thoughtfully designed to provide insights into various aspects of the marketing stream data. Key sections include Campaign Performance, Sales Trends, Conversion Rates, Click-through Rates (CTR), and Returns of Spends. The dashboard features interactive visualizations, such as bar charts, line charts, and tables, to present the data in a meaningful and actionable way.

Analysis and Findings:

1. Marketing Campaigns with the Highest ROI (RoAS):

   • Visualizations: Bar chart displaying RoAS for each campaign.
   • Finding: Campaign X had the highest RoAS, indicating it was the most profitable campaign.

2. Sales Performance Across Different Product Categories:

   • Visualization: Bar chart showing total sales for each product category.
   • Finding: Category Y and Category Z had the highest and lowest sales, respectively.

3. Hourly Behavior of Campaigns and Products:

   • Visualizations: Line chart showing campaign metrics (e.g., clicks, conversions) hourly.
   • Finding: Campaign A had the highest clicks during the afternoon hours, while Campaign B showed consistent conversions throughout the day.

4. Correlation between Marketing Campaign Timing and Product Sales:

   • Visualization: Scatter plot of campaign start/end times against product sales.
   • Finding: Some campaigns showed a positive correlation with sales, especially during their active periods.

5. Overall Sales Trend Before, During, and After Campaigns:

   • Visualization: Area chart displaying daily sales and marking campaign periods.
   • Finding: Campaign C resulted in a significant sales spike during the campaign period.

6. Optimizing Marketing Strategies for Improved Sales and Customer Engagement:

   • Explanation: Based on the analysis, allocate more resources to high-performing campaigns (e.g., Campaign X) and products (e.g., top-selling products in Category Y). Refine targeting strategies using CTR and conversion rate insights to enhance customer engagement.

7. Products with the Highest Growth in Sales During Campaigns:

   • Visualization: Bar chart comparing product sales before and during campaigns.
   • Finding: Product P experienced the highest growth in sales during the campaign period, while Product Q underperformed during the campaigns.

Conclusion and Recommendations:

The analysis provided valuable insights into the marketing performance data. To improve ROI, focus on Campaign X and consider optimizing ad spend allocation based on historical data. Invest in high-performing products and categories while addressing underperforming ones. Additionally, leverage the hourly behavior insights to fine-tune campaign scheduling for optimal customer engagement.