In-Depth Digital Marketing Campaign Analysis

Conducted by Dharinesh, this analysis delves into the effectiveness of digital marketing campaigns. The presentation evaluates key performance metrics, strategies, and outcomes to provide actionable insights for optimizing future marketing efforts and achieving greater results. for more information visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

1. Digital Marketing Campaign Analysis

2. Agenda : Enhance campaign effectiveness in digital marketing by predicting customer conversions. Objectives: Enhance campaign effectiveness in digital marketing by accurately predicting customer conversions. Develop a machine learning model to identify potential converters. Improve targeting of marketing efforts to increase efficiency. Enable data-driven decision-making in marketing strategies. Understand key factors influencing conversions to design more effective marketing campaigns.

3. Data Preprocessing Loading Data : Loaded dataset from an Excel file. Handling Missing Values: Dropped rows with missing values. Encoding Categorical Variables: Used LabelEncoder to convert categorical variables to numerical values. Scaling Numerical Features: Applied StandardScaler to standardize numerical features. Splitting Data: Split data into training and testing sets (70/30 split).

4. Exploratory Data Analysis (EDA) Key Visualizations: Conversion Rate by Gender: Identified differences in conversion rates between males and females. Conversion Rate by Campaign Channel: Analyzed effectiveness of different campaign channels. Conversion Rate by Campaign Type: Examined which campaign types led to higher conversions Ad Spend vs. Conversion: Visualized relationship between ad spend and conversion rates.

5. Model Selection: Evaluated various classifiers, started with Logistic. Training the Model: Used training data to train the Logistic regression. Making Predictions: Predicted conversion outcomes on test data. Classification Report: Show precision, recall, F1-score for each class. Accuracy Score: Present overall accuracy of the model. Feature Importance: Visualized the importance of each feature in predicting conversions using a bar chart. Model Building, Evaluation & Interpretation

6. Findings and Predictions : Conversion Rate Insights: Gender, campaign channel, and campaign type influence conversion rates. Ad Spend Effect: Higher ad spend generally leads to higher conversion rates. Model Predictions: Successfully predicted customer conversions with an accuracy of 89% Conclusion : Improved Targeting: Focused marketing efforts on likely converters. Cost Optimization: Reduced wasted ad spend. Enhanced Campaign Performance: Designed more effective marketing campaigns.

7. Questions ?

8. Thank You!