计算与预测 CLV

Python 营销中的机器学习

Karolis Urbonas

Head of Analytics & Science, Amazon

CLV 的目标

以营收/利润衡量客户价值
为客户做基准比较
确定获客可投入的上限
本例为简化起见忽略利润率，使用基于营收的 CLV 公式

基于营收的传统 CLV 公式

基础 CLV 计算

# Calculate monthly spend per customer
monthly_revenue = online.groupby(['CustomerID','InvoiceMonth'])['TotalSum'].sum().mean()

# Calculate average monthly spend
monthly_revenue = np.mean(monthly_revenue)

# Define lifespan to 36 months
lifespan_months = 36

# Calculate basic CLV
clv_basic = monthly_revenue * lifespan_months

# Print basic CLV value
print('Average basic CLV is {:.1f} USD'.format(clv_basic))

Average basic CLV is 4774.6 USD

细化 CLV 计算

# Calculate average revenue per invoice
revenue_per_purchase = online.groupby(['InvoiceNo'])['TotalSum'].mean().mean()

# Calculate average number of unique invoices per customer per month
freq = online.groupby(['CustomerID','InvoiceMonth'])['InvoiceNo'].nunique().mean()

# Define lifespan to 36 months
lifespan_months = 36

# Calculate granular CLV
clv_granular = revenue_per_purchase * freq * lifespan_months

# Print granular CLV value
print('Average granular CLV is {:.1f} USD'.format(clv_granular))

Average granular CLV is 1635.2 USD
Revenue per purchase: 34.8 USD
Frequency per month: 1.3

传统 CLV 计算

# Calculate monthly spend per customer
monthly_revenue = online.groupby(['CustomerID','InvoiceMonth'])['TotalSum'].sum().mean()

# Calculate average monthly retention rate
retention_rate = retention_rate = retention.iloc[:,1:].mean().mean()

# Calculate average monthly churn rate
churn_rate = 1 - retention_rate

# Calculate traditional CLV 
clv_traditional = monthly_revenue * (retention_rate / churn_rate)

# Print traditional CLV and the retention rate values
print('Average traditional CLV is {:.1f} USD at {:.1f} % retention_rate'.format(
                    clv_traditional, retention_rate*100))

Average traditional CLV is 49.9 USD at 27.3 % retention_rate
Monthly average revenue: 132.6 USD

应使用哪种方法？

取决于业务模型。
传统 CLV 模型假设流失是最终的，即客户"消亡"。
保留率低时，传统模型不稳健，往往低估 CLV。
最难预测的是未来的购买频率。

让我们来计算客户生命周期价值！

Python 营销中的机器学习