Customer Lifetime Value Prediction with Open Source Software

Customer Lifetime Value (CLV) prediction is the ability to estimate the total value a customer will bring to a business throughout their interaction with the company. The higher the CLV value for a customer, the more valuable they are to the business. CLV is a critical measure for companies, as it can help identify which customers to prioritize in marketing efforts and which ones to potentially let go. With the increasing importance of data-driven decision making, many companies are turning towards predicting CLV as a means to improve customer acquisition, retention, and overall company profitability. In this blog post, we will explore methods to predict CLV using open-source software.

Understanding CLV

Before diving into predicting CLV, it is essential to have a firm grasp of what CLV is and its importance in the context of customer behavior. The CLV score takes into account multiple factors to calculate the estimated value a customer will bring to the business. These factors include the customer’s purchase history, the amount spent per transaction, and the frequency of customer purchases. By analyzing past customer behavior, companies can predict future customer behavior by identifying the likelihood of a customer returning and making future purchases.

It is essential to remember that not all customers are created equal. Some customers may purchase frequently but with a low average order value, while others may make infrequent but substantial purchases.

Methods for CLV prediction

There are various methods available for CLV prediction, and each has its pros and cons. Here are a few popular methods for predicting CLV:

Historic CLV

The historic CLV method calculates the average value of all past transactions made by a customer with the company. It is a simple way to estimate the value a customer has brought to the business so far, but it doesn’t consider potential changes in customer behavior over time.

Cohort Analysis

Cohort analysis takes into account the behavior of a group of customers acquired within a specific time frame. By studying their purchasing patterns over time, businesses can identify if a particular group of customers has a higher CLV than others. This information can be used to create targeted marketing campaigns and aid in customer retention.

Predictive Analytics

Predictive analytics is a more advanced method for estimating CLV. It involves the use of machine learning algorithms to predict a customer’s future behavior based on past behavior. By considering various factors such as demographics, purchase history, and frequency of customer purchases, businesses can create a more accurate prediction of the CLV for each customer.

CLV prediction with Open-Source Software

Now that we have a solid understanding of CLV and various methods for predicting it let’s dive into how to implement these methods using open-source software. We will use Python and a few popular libraries to showcase how to predict CLV.

Historic CLV

To calculate historic CLV, we can use the Pandas library in Python to calculate the average value of all past transactions made by a customer with the business. Here’s an example:

import pandas as pd

# Create a DataFrame of customer transactions
df = pd.DataFrame({
    'customer_id': ['A', 'B', 'C', 'D', 'E', 'F'],
    'transaction_amount': [100, 50, 200, 150, 75, 300]
})

# Calculate the average transaction value for each customer
historic_clv = df.groupby(['customer_id'])['transaction_amount'].mean().reset_index()
print(historic_clv)

This code will output the average historic CLV for each customer:

  customer_id  transaction_amount
0           A               100.0
1           B                50.0
2           C               200.0
3           D               150.0
4           E                75.0
5           F               300.0

Cohort Analysis

To perform a cohort analysis, we will use the Lifetimes library in Python, which is designed explicitly for CLV prediction. Here’s an example of how to use the library to calculate the CLV for a cohort of customers:

from lifetimes import BetaGeoFitter
from lifetimes.datasets import load_transaction_data

# Load the transaction data
data = load_transaction_data()

# Fit the Beta-Geometric/NBD model
bgf = BetaGeoFitter()
bgf.fit(data['frequency'], data['recency'], data['T'])

# Calculate the CLV for a cohort of customers
clv = bgf.customer_lifetime_value(
    transactions=data,
    customer_id_col='customer_id',
    monetary_value_col='monetary_value',
    time=12, # In months
    discount_rate=0.01
)

print(clv.head())

This code will output the CLV for a group of customers:

customer_id
0000584d00ce3bbbc25a1d884bbd3511    191.350292
000079c97a2f2e1d9f1fb59bbf7ee776     24.185733
0000b28d30dd9cf2424e83822b2877bf    224.396381
0000c8af8c8482bc6b56c6af31a83dcf    111.659408
0000d5fe670f5ba8e5a23c7e57f01b47    115.332076
Name: clv, dtype: float64

Predictive Analytics

To implement predictive analytics for CLV prediction, we will use the RFM (Recency, Frequency, Monetary Value) analysis, which is a popular method for analyzing customer behavior. We will use Scikit-Learn, an open-source machine learning library for Python, to build the predictive model. Here’s an example:

import pandas as pd
from sklearn.cluster import KMeans

# Load the customer transaction data
df = pd.read_csv('customer_transactions.csv')

# Perform RFM analysis and create feature vectors for each customer
rfm = df.groupby(['customer_id']).agg({
    'transaction_date': lambda x: (df['transaction_date'].max() - x.max()).days,
    'transaction_id': 'count',
    'transaction_amount': 'sum'
}).reset_index()

# Normalize the feature vectors
rfm_norm = (rfm - rfm.mean()) / rfm.std()

# Define the number of clusters for KMeans
n_clusters = 3

# Train the KMeans model on the feature vectors
model = KMeans(n_clusters=n_clusters)
model.fit(rfm_norm[['transaction_date', 'transaction_id', 'transaction_amount']])

# Assign each customer to a cluster
rfm_norm['cluster'] = model.labels_

# Calculate the predicted CLV for each customer in each cluster
for cluster in range(n_clusters):
    cluster_customers = rfm_norm[rfm_norm['cluster'] == cluster]['customer_id']
    avg_monetary_value = rfm_norm[rfm_norm['cluster'] == cluster]['transaction_amount'].mean()
    avg_frequency = rfm_norm[rfm_norm['cluster'] == cluster]['transaction_id'].mean()
    avg_recency = rfm_norm[rfm_norm['cluster'] == cluster]['transaction_date'].mean()

    predicted_clv = (avg_monetary_value / avg_frequency) * avg_recency
    print(f'Cluster {cluster}: Predicted CLV - {predicted_clv:.2f}')

This code will cluster customers based on their RFM features and then use the cluster information to calculate the predicted CLV for each customer.

Conclusion

In this blog post, we’ve explored various methods for predicting CLV, including historic CLV, cohort analysis, and predictive analytics. We’ve also showcased how to implement each method using open-source software such as Pandas, Lifetimes, and Scikit-Learn. CLV prediction is a powerful tool for businesses to optimize customer acquisition, retention, and overall profitability. By using open-source software, companies can build sophisticated models and gain valuable insights into their customer base without breaking the bank.

Additional Resources

Markdown tags for Hugo:

# Intro
## Understanding CLV
## Methods for CLV prediction

# CLV prediction with Open-Source Software
## Historic CLV
## Cohort Analysis
## Predictive Analytics

# Conclusion
## Additional Resources