計算與推估 CLV

Python 的行銷機器學習

Karolis Urbonas

Head of Analytics & Science, Amazon

CLV 的目標

  • 以營收／利潤衡量顧客價值
  • 為顧客建立基準比較
  • 找出可投入的獲客上限
  • 本例為簡化起見，略過利潤率，改用基於營收的 CLV 公式

基於營收的傳統 CLV 公式

Python 的行銷機器學習

基本 CLV 計算

# 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
Python 的行銷機器學習

細緻 CLV 計算

# 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
Python 的行銷機器學習

傳統 CLV 計算

# 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
Python 的行銷機器學習

該用哪一種方法？

  • 取決於商業模式。
  • 傳統 CLV 模型假設流失是最終狀態＝顧客「流失」。
  • 留存率低時，傳統模型不穩健，會低估 CLV。
  • 最難預測的是未來的消費頻率。
Python 的行銷機器學習

一起來計算顧客終身價值！

Python 的行銷機器學習

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