Menghitung dan memproyeksikan CLV

Machine Learning untuk Pemasaran dengan Python

Karolis Urbonas

Head of Analytics & Science, Amazon

Tujuan CLV

Ukur nilai pelanggan dalam pendapatan/laba
Bandingkan pelanggan (benchmark)
Tentukan investasi maksimum untuk akuisisi pelanggan
Dalam kasus ini, kita abaikan margin laba demi sederhana dan gunakan formula CLV berbasis pendapatan

Formula CLV tradisional berbasis pendapatan

Perhitungan CLV dasar

# 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

Perhitungan CLV granular

# 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

Perhitungan CLV tradisional

# 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

Metode mana yang digunakan?

Bergantung pada model bisnis.
Model CLV tradisional mengasumsikan churn bersifat final = pelanggan "berhenti".
Model tradisional tidak robust pada retensi rendah — akan meremehkan CLV.
Yang paling sulit diprediksi: frekuensi di masa depan.

Ayo hitung customer lifetime value!

Machine Learning untuk Pemasaran dengan Python