DataSet

Here is the dataset which I have used to find out the Customer Lifetime Value (CLTV).

customer_acquisition_data.csv

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 visualising 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()