E-commerce Automation and Optimization - Customer Retention Prediction Model

Notes:

• Problem: Predicting customer retention in e-commerce is challenging.

• Benefit: Helps businesses focus on retaining high-value customers, saving on marketing costs.

• Adoption: Can be integrated with CRM systems to notify teams about retention efforts.

Code:

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import accuracy_score

# Example data (Customer ID, Age, Purchase History, Product Types, etc.)

data = pd.read_csv('customer_data.csv')

# Train a retention prediction model

X = data.drop('retention', axis=1)

y = data['retention']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

model = RandomForestClassifier()

model.fit(X_train, y_train)

predictions = model.predict(X_test)

print("Accuracy: ", accuracy_score(y_test, predictions))