Predictive Modeling for Conversion Optimization

2.1 Definitions

• Predictive Modeling: A process used to predict an outcome based on historical data.
• Conversion Rate Optimization (CRO): The practice of increasing the percentage of users who complete a desired action on a website.
• User Behavior: Actions taken by users on a website, which can be analyzed to improve user experience and conversion.

3.1 Data Collection

Gather data from various sources such as website analytics, CRM systems, and user feedback.

3.2 Data Preprocessing

Clean the data by handling missing values, normalizing data, and encoding categorical variables.

3.3 Model Selection

Choose appropriate predictive modeling techniques such as logistic regression, decision trees, or machine learning algorithms.

3.4 Model Training

Use historical data to train the selected model. Below is an example using Python's scikit-learn:

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Sample dataset
X = [[...], [...]]  # Features
y = [...]  # Target variable (conversion: yes/no)

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Create and train the model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, predictions)
print(f'Accuracy: {accuracy:.2f}')
                

3.5 Model Evaluation

Assess the model's performance using metrics like accuracy, precision, and recall.

3.6 Implementation

Implement the model to predict user actions and optimize conversion strategies accordingly.