Digital Marketing Campaign Conversion Prediction.

Unlock the power of predictive analytics in digital marketing with our in-depth exploration of conversion prediction. This presentation provides a comprehensive approach to forecasting the effectiveness of digital marketing campaigns by analyzing historical data, user behavior, and campaign metrics. Discover how to use data-driven insights to optimize your marketing strategies, improve conversion rates, and maximize return on investment (ROI). for more information visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

1. Digital Marketing Campaign Conversion Prediction - Boston Institute Of Analytics

2. Introduction Data Preprocessing Exploratory Data Analysis (EDA) Feature Engineering Model Selection Model Evaluation

3. This project aims to enhance campaign effectiveness in the digital marketing sector by accurately predicting customer conversions. By leveraging machine learning, the project seeks to identify potential converters and optimize marketing strategies. The objective is to develop a robust machine learning model that predicts customer conversions based on various demographic and engagement factors, enabling improved campaign targeting, increased conversion rates, and maximized return on advertising spend (ROAS).

4. Based on the first bar chart equal amount money was spend on different Champaign channels Likewise based on the second chart there is also equal amount of money was spend on different Campaign Type

5. The first chart suggests that Social Media is the largest campaign channel by a small margin followed by other Campaign channel. The second chart shows that how many percentage of people are converted or not converted.

6. In this process we convert or drop different feature that have significant or not have significant impact on the Dependent feature. Like we convert categorical Data type into numerical so that Algorithm can understand the data . In this project we have Campaign Channel and Campaign Type as categorical Data type so we have to convert this to numerical using different encoding technique like One Hot Encoding. Here is the code that convert this to numerical and drop the feature that not have significant impact on target variable.

7. In this process we try different type of model or algorithm that give better result on the data and give good accuracy. There are different type of algorithm, that perform better on categorical problem in this project I use Logistic Regression, Random Forest and SVM. After applying this algorithm I got best accuracy using Logistic regression. Here is the code that how I implement Logistic Regression.

8. In this process we asses the model that whether the model is overfit or underfit. We use different technique like accuracy score, classification report and confusion metrics. Here is the code that help to asses the model.

9. Thank You