Churn-Project

Managing Customer Churn using Data Analytics

This book "Fighting Churn With Data: Science and strategy for keeping your customers" serves as a detailed guide to the code. You can get more information at:

The author's You Tube Channel, Fighting Churn With Data Science

---

Setup Instructions

Pre-requisite Setup

1. Python 3 and the required packages (requirements.txt)
2. PostgreSQL
3. PgAdmin - for PostgreSQL database setup

---

Links for Downloading

For Linux :

1. For installing Python: https://www.python.org/downloads/
2. For installing PostgreSQL: https://www.postgresql.org/download/linux/
3. For installing pgAdmin: https://www.pgadmin.org/download/pgadmin-4-apt/

---

Database Setup

Create a Database by clicking on the root of the Servers tree and selecting Create.

A dialog will open. Name your connection localhost, and on the second tab (Connection) enter the address 127.0.0.1. You should also enter your user name and password.

Next you need to create a new database to hold all of the churn data schemas you create. You will probably create multiple schemas as you work on the examples in the book and/or your own data so this will help keep these organized. An easy way to create a database is in PgAdmin - right click on the Databases node under localhost in the tree:

And enter the name of the new database:

---

Code Setup

Command Line Setup

Create a Virtual Environment

First, you should make a new virtual environment in which to install the Fight Churn code.

python3 -m venv churn

You can use whatever name you like for the python environment but i have used churn for relevance. Next, you have to activate your environment!

---

Active environment

source churn/bin/activate

The prompt will change to :

(churn) ~ user$ 

---

Install the fightchurn package

pip install fightchurn

---

Create a directory for output

You should make a local folder to store your output. On linux that would look as follows:

mkdir my_churn_output_folder

---

Start the Python virtual environment

Next you should start your Python environment, and enter a python shell:

source churn/bin/activate
python

All the commanda above are assuming you named your virtual environment churn). You should see something like the following...

(py_venv) :~ user$ python
Python 3.9.6 (default, Jun 29 2021, 05:25:02) 
[Clang 12.0.5 (clang-1205.0.22.9)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> 

---

Import the run_churn_listing_module

Next, you will import the fightchurn package that you will use to run everything:

from fightchurn import run_churn_listing

---

Set the churn environment variables

Now you need to set a few enviroment variables. These are:

1. The database name : 'churn' in the example below
2. The username for the database
3. The password for the database
4. A local folder where outputs can be written....

run_churn_listing.set_churn_environment('churn','user','password','path_to_my_churn_output_folder')

---

Run the data simulation

Next, you need to write some data to the database in order to run the code against. The example is for a standard simulation of 10,000 customers. Use the following command :

run_churn_listing.run_standard_simulation(init_customers=10000)

You will see the output as follows :

Creating schema socialnet7 (if not exists)...
Creating table event (if not exists)
Creating table subscription (if not exists)
Creating table event_type (if not exists)
Creating table metric (if not exists)
Creating table metric_name (if not exists)
Creating table active_period (if not exists)
Creating table observation (if not exists)
Creating table active_week (if not exists)
Creating table account (if not exists)

Creating 2000 initial customers for month of 2020-01-01
Simulated customer 0/2000: 2 subscriptions & 100 events
Simulated customer 100/2000: 448 subscriptions & 154,047 events
Simulated customer 200/2000: 872 subscriptions & 282,882 events
Simulated customer 300/2000: 1,324 subscriptions & 426,866 events
Simulated customer 400/2000: 1,767 subscriptions & 557,543 events
...

This will continue for a while - maybe 10-15 minutes if you ran the full 10,000 customer simulation.

---

Run code listings

Example for running the code

Now you are ready to run the code. To do that you use the run_listing function that you previously imported. For example, the following is chapter 2, listing 2:

run_churn_listing.run_listing(2,2)

Running multiple listings and versions

In some parts of the book you might want to run more than one listing at once. To do this, pass as a list for the listing argument. For example, to run all four chapter 2 churn calculation listings try:

run_churn_listing.run_listing(2,[1,2,3,4])

Later in the book, some of the listings have multiple versions with different arguments. The run_listing function also takes a version argument. For example, to run a query and plot the results of the events per day for the first event created by the simulation, try the following:

run_churn_listing.run_listing(chapter=3,listing=[9,10],version=[1,2,3])

That command should save the plots to your output directory.

Command Sheet & Listings' Description

You can follow and use the excel sheet to run the code listings and understand the manipulations that each listing does to the data.

Fighting Churn Commands

• Listings (Sheet 1) : This sheet contains all the code listings in each chapter from the book, along with a brief description of what each listing does. This is will help you understand the code listing that you want to run and what happens to the data after the code is executed.
• Command Sheet (Sheet 2) : This sheet contains the commands that need to be executed in order to get the expected output.