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Background

  • Operators are losing in today’s competitive market. Churn is a key driver and is an industry wide challenge. A churned customer provides less revenue or zero revenue and increases competitor market share. Increase acquisition cost for the service provide if the customer churned to competition. It costs up to 5 times as much for a Service provider to acquire a new subscriber as to retaining an existing one.

Goal

  • This analysis will asses the classification of subscribers, asses insights on churn behavior of subscribers and using the information to strategize new marketing initiatives.

Dataset info

  • Sample Dataset containing Telco customer data and showing Customers that left last month

Methodology

  1. Install and load Python libraries
  2. Explore the data
  3. Transform the data
  4. Perform Data visualisations
  5. Analyse correlation in between attributes

Tools

-Python, Jupyter notebooks

Importing and Loading necesary Libraries

#pip install pandas
#pip install seaborn
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.ticker as mtick
import matplotlib.pyplot as plt
%matplotlib inline
Load the datafile
telco_base_data = pd.read_csv('WA_Fn-UseC_-Telco-Customer-Churn.csv')