Customer lifetime value analysis is used to estimate the total value of customers to the business over the lifetime of their relationship. It helps businesses make data-driven decisions on how to allocate their resources and improve their customer relationships. So, if you want to learn how to analyze the lifetime value of customers, this article is for you. In this article, I will take you through the task of Customer Lifetime Value Analysis using Python.
Customer Lifetime Value Analysis
Customer lifetime value analysis is used to estimate the total value of customers to the business over the lifetime of their relationship. It helps companies determine how much to invest in customer acquisition and retention, as well as identify the most valuable customers to prioritize for retention efforts.
By analyzing customer lifetime value, companies can identify the most effective marketing channels and campaigns for acquiring high-value customers, as well as develop targeted retention strategies to keep those customers engaged and loyal.
For the Customer Lifetime Value analysis task, we need a dataset based on customers’ relationships with the business. I found an ideal dataset for this task. You can download the dataset from here.
In the section below, I will take you through the task of analyzing Customer Lifetime Value using Python.
Customer Lifetime Value Analysis using Python
I’ll start this task by importing the necessary Python libraries and the dataset:
import pandas as pd
import plotly.graph_objs as go
import plotly.express as px
import plotly.io as pio
pio.templates.default = "plotly_white"
data = pd.read_csv("customer_acquisition_data.csv")
print(data.head())customer_id channel cost conversion_rate revenue 0 1 referral 8.320327 0.123145 4199 1 2 paid advertising 30.450327 0.016341 3410 2 3 email marketing 5.246263 0.043822 3164 3 4 social media 9.546326 0.167592 1520 4 5 referral 8.320327 0.123145 2419
Let’s start by visualizing the distribution of acquisition cost and revenue generated by the customer using histograms:
fig = px.histogram(data,
x="cost",
nbins=20,
title='Distribution of Acquisition Cost')
fig.show()
fig = px.histogram(data,
x="revenue",
nbins=20,
title='Distribution of Revenue')
fig.show()
Now let’s compare the cost of acquisition across different channels and identify the most and least profitable channels:
cost_by_channel = data.groupby('channel')['cost'].mean().reset_index()
fig = px.bar(cost_by_channel,
x='channel',
y='cost',
title='Customer Acquisition Cost by Channel')
fig.show()
So paid advertisement is the most expensive channel, and email marketing is the least expensive channel. Now let’s see which channels are most and least effective at converting customers:
conversion_by_channel = data.groupby('channel')['conversion_rate'].mean().reset_index()
fig = px.bar(conversion_by_channel, x='channel',
y='conversion_rate',
title='Conversion Rate by Channel')
fig.show()
Social media is the most effective channel for converting customers, while paid advertising is the least effective. Now let’s calculate the total revenue by channel and have a look at the most and least profitable channels in terms of generating revenue:
revenue_by_channel = data.groupby('channel')['revenue'].sum().reset_index()
fig = px.pie(revenue_by_channel,
values='revenue',
names='channel',
title='Total Revenue by Channel',
hole=0.6, color_discrete_sequence=px.colors.qualitative.Pastel)
fig.show()
So email marketing is the most profitable channel in terms of generating revenue. But there’s not a huge difference between the percentages of revenue generation from all the channels to call any channel the least profitable channel.
Now let’s calculate the return on investment (ROI) for each channel:
data['roi'] = data['revenue'] / data['cost']
roi_by_channel = data.groupby('channel')['roi'].mean().reset_index()
fig = px.bar(roi_by_channel,
x='channel',
y='roi', title='Return on Investment (ROI) by Channel')
fig.show()
The ROI from email marketing is way higher than all other channels, while the ROI from paid advertising is the lowest. Now let’s calculate the customer lifetime value from each channel. Based on the data we have, we can use the formula mentioned below to calculate CLTV:
- CLTV = (revenue – cost) * conversion_rate / cost
data['cltv'] = (data['revenue'] - data['cost']) * data['conversion_rate'] / data['cost']
channel_cltv = data.groupby('channel')['cltv'].mean().reset_index()
fig = px.bar(channel_cltv, x='channel', y='cltv', color='channel',
title='Customer Lifetime Value by Channel')
fig.update_xaxes(title='Channel')
fig.update_yaxes(title='CLTV')
fig.show()
So the customer lifetime value from Social Media and the referral channels is the highest.
Now let’s compare the CLTV distributions of the social media and referral channels:
subset = data.loc[data['channel'].isin(['social media', 'referral'])] fig = px.box(subset, x='channel', y='cltv', title='CLTV Distribution by Channel') fig.update_xaxes(title='Channel') fig.update_yaxes(title='CLTV') fig.update_layout(legend_title='Channel') fig.show()
There’s not much difference, but the Customer Lifetime Value from the Social Media channel is slightly better than the referral channel.
So this is how you can analyze and compare the customer lifetime value from various channels.
Summary
Customer lifetime value analysis is used to estimate the total value of customers to the business over the lifetime of their relationship. It helps companies determine how much to invest in customer acquisition and retention, as well as identify the most valuable customers to prioritize for retention efforts. I hope you liked this article on Customer Lifetime Value Analysis using Python. Feel free to ask valuable questions in the comments section below.
