Calculando e projetando CLV

Machine Learning para Marketing em Python

Karolis Urbonas

Head of Analytics & Science, Amazon

O objetivo do CLV

Medir valor do cliente em receita/lucro
Fazer benchmark de clientes
Identificar o investimento máximo em aquisição
No nosso caso, vamos ignorar a margem de lucro e usar fórmulas de CLV baseadas em receita

Fórmula tradicional de CLV baseada em receita

Cálculo básico de CLV

# 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

Cálculo granular de CLV

# 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

Cálculo tradicional de CLV

# 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

Qual método usar?

Depende do modelo de negócio.
CLV tradicional: assume churn definitivo = cliente "morre".
O modelo tradicional não é robusto com baixa retenção — subestima o CLV.
O mais difícil de prever é a frequência futura.

Vamos calcular o valor do tempo de vida do cliente!

Machine Learning para Marketing em Python