In this project, I undertook the task of evaluating and optimizing a mobile marketing campaign through the lens of online advertising. The dataset, aptly named campaign.csv, encapsulates a full day's worth of impressions generated by a particular mobile campaign. Each row within the dataset represents a unique impression, with various characteristics outlined in columns. The primary focus is on understanding the outcomes of these impressions—specifically, whether they resulted in a click or led to a conversion following a click. Please find the entire project by clicking here.

Dataset Overview:

The dataset encompasses a diverse range of information, including timestamps, user interests from external partners, historic user interactions, device specifications, geographical data, and details about the advertisement itself. The richness of the dataset presents an opportunity to derive valuable insights and enhance the effectiveness of the marketing campaign.

Key Tasks Accomplished:

Data Cleaning:
- Removed missing values to ensure the dataset's integrity.
- Selected relevant columns for analysis.
- Converted column types for better compatibility and understanding.
Exploratory Data Analysis (EDA):
- Explored the dataset to gain a comprehensive understanding of its structure.
- Utilized visualizations to uncover patterns and trends within the data.
- Investigated relationships between various features and the occurrence of clicks.
Statistical Analysis:
- Employed statistical methods to identify factors that appear to have the most significant influence on the occurrence of clicks.
- Focused on key columns, such as user interests, historical interactions, and device specifications.

Key Findings:

The analysis revealed compelling insights into the factors influencing click occurrences. Notably, specific user interests, historic interactions, and device-related parameters demonstrated a significant impact on user engagement.

Project Impact:

Understanding these influential factors provides actionable insights for optimizing the marketing campaign. This newfound knowledge can guide strategic decision-making, enabling the campaign to better target users based on their interests and preferences.

Conclusion:

This project successfully bridged theoretical knowledge with practical application, showcasing the ability to extract valuable insights from real-world data. The findings contribute to the ongoing enhancement of the mobile marketing campaign, emphasizing a data-driven approach for effective online advertising.

Mobile Marketing Campaign Analysis by Python