用大数据重塑客户关系管理：聪明企业的秘密武器

import pandas as pd

# 假设我们有客户访问记录数据
data = {
    'customer_id': [1, 2, 3, 1, 3],
    'page_visited': ['Home', 'Product A', 'Product B', 'Cart', 'Checkout'],
    'time_spent': [5, 10, 7, 15, 3],
}
df = pd.DataFrame(data)
print(df)

# 统计客户访问页面次数
page_analysis = df.groupby('page_visited')['time_spent'].sum()
print(page_analysis)

# 找出客户在'Cart'页面后的流失率
cart_data = df[df['page_visited'] == 'Cart']
conversion_rate = len(cart_data) / len(df)
print(f"转化率：{conversion_rate}")

from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics.pairwise import cosine_similarity

# 假设有客户购买行为数据
purchase_data = pd.DataFrame({
    'customer_id': [1, 2, 3],
    'Product A': [1, 0, 1],
    'Product B': [0, 1, 1],
})

# 基于余弦相似度生成推荐
similarity = cosine_similarity(purchase_data.drop('customer_id', axis=1))
recommendations = purchase_data.loc[similarity.argmax()]
print(f"推荐产品：{recommendations}")