Optimizing Digital Marketing Success: Conversion Prediction Techniques

Explore the art and science of predicting digital marketing campaign outcomes with Shubham Gughane's presentation from the Boston Institute of Analytics. This session focuses on leveraging advanced analytics and machine learning models to forecast conversion rates and enhance campaign strategies. Gain insights into data-driven methods for improving marketing ROI, refining targeting strategies, and maximizing campaign effectiveness. Perfect for those interested in applying predictive analytics to digital marketing for superior results and strategic planning. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

1. Digital Marketing Campaign Conversion Prediction - Shubham Gughane

2. Agenda Introduction Exploratory Data Analysis (EDA) Data Preprocessing Feature Engineering Model Selection Model Evaluation Model Deployment using Streamlit

3. Introduction 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. Exploratory Data Analysis (EDA) The ratio of customers who are converted when the campaign channel is SEO and the campaign type is conversion is the slightly high among all possible combinations of campaign channel and campaign type. But the ratios of other campagin channels with the conversion campaign type are very close to it. That is to say if the goal is to make more customers to buyers, then the campaign type should just be conversion no matter what the campaign channel is.

5. Exploratory Data Analysis (EDA) E mail channel has costed the Slightly high average advertisement fee. A wareness campaign type has costed the Slightly high average advertisement fee .

6. Exploratory Data Analysis (EDA) . Female customers has more conversions than male customers. In Campaign Channel Referral Channel has top conversions following PPC Channel. In Campaign type conversion type has top conversions.

7. Data Preprocessing . D istribution of the Conversion shows a significant imbalance between the classes, with much fewer not Converted (0) compared to Converted (1). This imbalance will be affect model performance, so we need to balance it during the model training to ensure our model does not become biased toward predicting the majority class.

8. Feature Engineering 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. df = pd.get_dummies(df_upsampled,drop_first=True)

9. Model Selection 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 , DecisionTree, KNeighborsClassifier, AdaBoost and GradientBoosting. After applying this algorithm I got best accuracy using Random Forest .

10. Model Evaluation 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. RandomForestClassifier Training Accuracy: 1.0 Testing Accuracy: 0.9857

11. Model Deployment using Streamlit Deploying model using Streamlit

12. Questions ?

13. Thank You!