# Calculate Customer Lifetime Value with Python

CLV is a crucial measure for firms to comprehend in order to maximize their marketing and sales activities. CLV is a forecast of the net profit attributable to a customer’s entire future relationship. This article will explain how to compute CLV in Python. Let’s take a look at the formula to get your mind in the right place.

CLTV = ((Average Order Value x Purchase Frequency)/Churn Rate) x Profit Margin

First, we will need to collect information about our clients, such as their purchase history, and any other pertinent data. Let’s take a lot at our data set.

The data will next be cleaned and organized using the Python Pandas package. Pandas is an indispensable tool for manipulating and analyzing massive datasets, as it is a potent data manipulation and analysis tool. Using the Pandas library, we will create a data frame containing all of our customer data, and group it according to the customer identifier.

After cleaning and organizing the data, we may begin the CLV calculation process. RFM analysis is a well-known technique for computing CLV. This strategy involves classifying clients depending on the time period in which they made their initial purchase and then monitoring their purchasing behavior over time. Using the Pandas library in Python, we can group consumers by the period in which they first made a purchase, and then determine the average purchase value for each group using the group by function.

## Watch the Video Presentation on CLTV with Python

After calculating the average purchase price for each group, the following formula can be used to determine CLV:

CLV = (average purchase price x frequency of purchases) / customer churn rate

The buy frequency is the number of purchases per year, whereas the customer churn rate is the percentage of consumers who cease making purchases annually.

Finally, we can use the Matplotlib module in Python to visualize our CLV data. Matplotlib is a potent tool for producing a vast array of static, animated, and interactive displays. Matplotlib can be used to construct a graph of CLV over time or a scatter plot of CLV against other customer data points, such as demographic information.

```
#load in the datasets and packages
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv("https://raw.githubusercontent.com/Gaelim/google-data-stuido/main/Ecommerce_Data.csv")
#some small data wrangling task
df['total_revenue'] = df['Quantity']*df['UnitPrice']
df.drop('Unnamed: 0',axis=1,inplace=True)
df['Date']=pd.to_datetime(df['Date'])
df = df[df['Quantity'] >0]
#lets put this all together in a function
def customer_model(data):
max_date = data['Date'].max()
data = data.groupby('CustomerID').agg(
{'Date':lambda x: (max_date-x.min()).days,
'InvoiceNo': lambda x: len(x),
'Quantity': lambda x: x.sum(),
'total_revenue': lambda x: x.sum()})
return data
# run the function
data = customer_model(df)
#change the name of the columns and insure that we dont have 0 quantity
data.columns = ['age','num_transactions','quantity','total_revenue']
#create the average order value
data['AOV'] =data['total_revenue']/data['num_transactions']
# lets calcualate the purchase frequency
purchase_freq = sum(data['num_transactions'])/len(data)
# lets calcuate the repeate rate
repeat_rate = data[data['num_transactions']>1].shape[0]/data.shape[0]
#lets add the profit margin
data['profit_margin'] = data['total_revenue']*.10
# lets put the final equation
data['CLTV'] =((data['AOV']*purchase_freq)/churn_rate)*.10
```

Calculating CLV in Python is a potent resource for firms seeking to comprehend and optimize their marketing and sales activities. By collecting client data, cleaning and organizing it, and utilizing Python packages such as Pandas and Matplotlib, we can simply compute CLV and obtain significant insight into our customers’ behavior.