How to Perform Churn Analysis for your Mobile Application?

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A GACP and GTMCP company

How to perform Churn Analysis for your Mobile Apps

March 19th, 2014 Free Webinar by

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Your Speakers

• @parikh_shachi• Technical Analyst @Tatvic• Interested in jQuery, JavaScript and Data

Analysis

• @Mrugesh_soni• Web Analyst @Tatvic• Interested in Statistical Modelling of

data

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Overview

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Overview

What is Churn Analysis?

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Overview


Why we are carrying out Churn Analysis?

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Overview



What are the Benefits Of Churn Analysis?

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Overview




What data will back-up my churn analysis?

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Overview





How to extract the desired data?

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Overview






Why did we select this data abstraction

method?

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Overview







method?

Why Predictive Model?

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Overview







method?


Which machine learning Algorithm

will fuel our model?

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Overview







method?

How to build predictive model?




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• The term “Churn” refers to customer attrition

• The process of identifying those customers who are most likely to discontinue the use of your service or product is known as Churn Analysis

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Why Churn Analysis?

• Customer Acquistion Cost > Customer Retention Cost

• Customer might Churn before you recover Acquisition Cost

• Signals that indicate whether a Customer is about to Churn

– Cancel Account/Service

– Uninstall Mobile App

• Difficult to Retain them with standard Retention Measures

• Calls for a Pro-active Approach

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Business Objective for Churn Analysis

Reduce Customer Churn

• Predict whether a customer will Churn and when it will

happen

• Understand why customers churn and act on these reasons

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Revenue as a function of Churn Rate

If you cut your churn in half, then in few years your revenue will be increase by 50 %

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Impact of Churn on your business in terms of revenue

0

5,000

10,000

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YearsTotal Revenue… Total Revenue…

2X $

X $

After 5 years, a company with 3% churn rate has 50% higher recurring revenue than the company with 6% churn rate

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Churn: Implications on Mobile Apps

Most of the Apps Lose Half of their Peak Users within 3 Months

15001,700

1,9001,720

1,4601,272

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1000120014001600180020002200

1 2 3 4 5 6

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Total User

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1,700

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Total…

Before Churn Analysis

After Churn Analysis

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1500

1,700

1,900

1,720

1,460

1,272

500 500 500 500 500 500

300 300

680760

688 650

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Effect on Total Users : Before and After Churn Analysis

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1500

1,700

1,900

1,720

1,460

1,272

500 500 500 500 500 500

300 300

680760

688 650

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1500

1,700

1,9002,020

2,1162,193

500 500 500 500 500 500

300 300380 404 423 439

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After Churn Analysis

Effect on Total Users : Before and After Churn Analysis

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How to begin with Churn Analysis?

• Identify the type of App (eg. Gaming/Social/Productivity/Travel)

• Identify the activities that users perform on your app

• Identify the frequency of usage of core features across your user-base

• Identify the average life-time for your user base

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Churn Criteria w.r.t. App

• Cut-off date defines the date difference between Current Date and the Date when user last engaged with your app

• E.g. User A last interacted with app on 9th Feb 2014 and has been inactive ever since

• Cut-off Date for User A is 40 days

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Data Points for defining Churn

Other parameters can be:

• # of activities performed since the app was downloaded

• # of times the user visited your app

• Date of App install

• Whether the user interacted with Core Features on a regular basis

• And so on…depending upon the nature of your app.

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Overview







method?





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3 Steps prior to Model building

1• Variable Selection

2• Data Extraction

3• Data Preprocessing

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Variable(Feature) Selection

Category Variable

Location Country

Device SpecificOperating System

Device category

In App behaviorCount of sessions

Days since last visit

User action

User specific action1




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Extracting Google Analytics Data into R

User performing data extraction

Google OAuth2Authorization

Server

Google Analytics API

Access Token Response

Call API for list of

profiles

Call API for query

Access Token Request

Image adapted from: Google Analytics Core Reporting API Dev Guide

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Overview







method?





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Data Preparation Steps

• Exclude new user who have downloaded the app recently

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Overview







method?





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Overview







method?





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Classification Problem

• In our case, we are trying to predict whether user will churn or not

• Based on data points, we are trying to classify between two outcomes

• This is a Classification Problem

Churn

“YES”1

“NO”0

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Logistic Regression

• Logistic Regression is the technique when you are trying to predict the binary output.

• In our case Predictor(dependent) variable will be unique key(Visitor ID) for each visitors

• Predicted label would be1 : Visitor will churn0 : Visitor would not churn

• Using the Logistic Regression algorithm we predict the probability of a user getting Churn or not

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Model building Process

• Split Data Randomly into Train and Test Sets

• Build the Model on the Train Data-set• Apply the Model on the Test Data-set (un-seen data)• Calculate the Accuracy Metric for the model on the Test Data

70% 30%

Train Data Set Test Data Set

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Model Accuracy

Accuracy = (Number of Correctly Predicted Labels) / Total Number of Labels= (620 + 1024)/ (620 + 4 + 7 + 1024)~ 99.34 %

Predicted Labels

(Predicted by running Model

on Test data set)

Actual labels (From Test data set)

Churn Not Churn

Churn 620 4

Not Churn 7 1024

Confusion Matrix

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User Segmentation

Fencers Activators Engaged Loyalists

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User types

Fencers

Tried App just once in last x

days

Awareness, Benefit, cost of not using App

Activators

Tried App more than once, used more than one

feature

Increase Knowledge , Motivation

Engaged

Tried App more than once, more

than 2 features/levels of

app, more at infrequent time

Learning, practice,

improving skills

Loyalists

Regular usage of app with most of features, shares

app, recommends,

provide feedback

Practice, improve,

influence others

Characteristics

Messaging

0.87 0.56 0.45 0.35Churn chance

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Overview







method?



Which machine learning fuel our

model?

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Webinar Video

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Thank you!

Mrugesh Soni

[email protected]

Data & Analytics

How to Perform Churn Analysis for your Mobile Application?