Www.decideo.fr/bruleyChurn Extract from various presentations: Owens, Telecom Lab, Aster Data …...

www.decideo.fr/bruley

ChurnChurn

Extract from various presentations: Owens, Telecom Lab, Aster Data …

[email protected]

January 2013January 2013


Churn in Comm. Industry: a bottom line issue

Attracting thousands of new subscribers is worthless if an equal number are leaving

Minimizing customer churn provides a number of benefits, such as:

– Minor investment in acquiring a new customer

– Higher efficiency in network usage

– Increase of added-value sales to long term customers

– Decrease of expenditure on help desk

– Decrease of exposure to frauds and bad debts

– Higher confidence of investors


Poor Service

45%

Lack of Attention

20%

Price15%

Quality15%

Other5%

How can I effectively manage customer churn?Why are my customers churning?

How do I identify key churn drivers across the customer lifecycle?How can I predict when my customers will churn?

What kind of initiatives can I run to anticipate customer churn and address drivers of churn?How do I report on churn and retention initiatives?

Churn: Why Customers Leave


Churn management: scoping the problem (1)

Churn can be defined and measured in different ways

– “Absolute” Churn. number of subscribers disconnected, as a percentage of the subscriber base over a given period

– “Line” or “Service” Churn. number of lines or services disconnected, as a percentage of the total amount of lines or services subscribed by the customers

– “Primary Churn”. number of defections

– “Secondary Churn”. drop in traffic volume, with respect to different typology of calls


Churn management: scoping the problem (2)

Measuring churn is getting more and more difficult

– Growing tendency for Business users to split their business between several competing fixed network operators

– Carrier selection enables Residential customers to make different kind of calls with different operators

– Carrier pre-selection and Unbundling of the Local Loop makes it very difficult to profile customers according to their “telecommunication needs”

Other frequent questions for Fixed Network Services

– What if a customer changes his type of subscription, but remains in the same telco? What if the name of a subscriber changes? What if he relocates?


The case: Churn Analysis for wireless services

The framework

– A major network operator willing to establish a more effective process for implementing and measuring the performance of loyalty schemes

Objectives of the “churn management” project

– Building a new corporate Customer Data Warehouse aimed to support Marketing and Customer Care areas in their initiatives

– Developing a Churn Analysis system based upon data mining technology to analyze the customer database and predict churn


Business understanding

Sponsors

– Marketing dept., IT applications, IT operations

Analysis target

– Residential Customers, subscriptions

Churn measurement

– Absolute, primary churn

Goal:

– Predict churn/no churn situation of any particular customer given 5 months of historical data


Solution scope

millions of residential customers

millions of customers

millions of business customers

Usage patterns analysis of Voice Services by

single subscriber line

Usage patterns analysis of Voice Services by

subscriber line, contract, company, etc.

Usage patterns analysis of VAS by single subscriber line


Application framework

ContractsTariff plansBilling data

Accounts dataFraud / Bad debts data

Customer dataMarket dataSales data

Customer service contacts

Front-officeSystems

Marketing automation

Service automation

Salesautomation

Marketing

ListenerLoader

Loader

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ETL

Data Collection &Transformation

Data Preprocessing

Data Server

Data Warehouse

Analytical Applications

Reporting OLAP

Data Mining

Decision Engine

Back-officeSystems

•Campaign Targets•New product /

services•Loyalty schemes

•Performance analysis


Data understanding

CustomerData Warehouse

Input Data• Customer demographics

Basic customer information• Service Profile

Products/services purchased by each customer.

• Tariff plansDetails of the tariff scheme in use• Extra service information

Special plans / ratesService bundles

• Call data aggregated by month• Billing data aggregated by month

• Complaint information• Fraud and bad debts data• Customer service contacts

• Sales force contacts• Market data

xx operational systems

•More than 500 indicators per customer•Loading: on a monthly basis

•Size: xTB


Modeling with Data Mining tool

Main steps

– Define Concepts, Attributes, Relationships …

– Select Operators

– Build the execution workflow


Concepts, Attributes, Relationships

Demographic attributes

Call data records

Data about subscribed

services

Revenue data


Construction stage output

16 Raw attributes

45 Derived attributes

Data Construction Feature Selection


Churn modeling chain

Medium value customers are selected

training set

decision tree operator applied to fit predict the

likelihood of a customer to become a churner in the

month M6

Save output

4 Predictive models, one for each

customer segment


Predictive performance

PRED_ACTPRED_CHN

ACTIVE

CHURNER

11

8986

140

20

40

60

80

100

MEDIUM customer model performance

PRED_ACTPRED_CHN

ACTIVE

CHURNER

19

8194

60

20

40

60

80

100

HIGH customer model performance

PRED_ACTPRED_CHN

ACTIVE

CHURNER

5

95

67

33

0

20

40

60

80

100

VERY LOW customer model performance

PRED_ACTPRED_CHN

ACTIVE

CHURNER

25

7582

180

20

40

60

80

100

LOW customer model performance


What Is Graph Analysis?

Aster: MapReduce implementations for graph analysis

Operates on Any Transaction or Interaction Data

•Identifies the individuals or nodes in a network•Identifies the relationships or edges in a network

In-Memory Graph Structure Allows for Graph Analytics

•MapReduce creates a graph object that can then be traversed for analysis

•Traversal of the graph is non-trivial even for simple graph analysis

Output of Graph Analysis Is Flexible

•MapReduce used to dynamically bind structure to data on execution


Teradata Aster Graph Analysis

Why this belongs on Aster

• Limitations in SQL Relational DBMSs- Set-based SQL is a poor programming construct

for Graph problems

- Every connection between 2 people is a self-join in SQL

• Aster Advantages- Read & transform data into in-memory graph

structure

- Perform standard SQL logic or MapReduce on the in-memory graph structure

- Influencer Analytics: traverse the graph for single shortest path – six degrees of Kevin Bacon

Aster Metrics: Social Graph Analysis

Environment:

1 billion rows, 700GB, 11 workers

Aster Response:

180 Seconds


Graph Analysis & Churn Prediction

Graph Analysis constructs an influence propagation model

- Given persons who churned (initial churners)- Diffuse their influence into their social environment- Thus, their friends are at a larger churn risk.. (The two C1s churn; N does not) - And this propagates to some of their friends’ friends (C2 affected due to indirect, cumulative influence)

Output

- List of predicted churners

Business Value

- Captures higher order social effects - Capture the effect of multiple churners on a subscriber- Does not require profile information- Once the model is created, it can be run quickly & often - Complements traditional churn models

I

C1

*

N

*

*

I

C1

C2

I Initial churners (known)

C Predicted churners

Influence spreading

Indirect influence


Identify most frequent paths to early termination of service

Analyze specific patterns of customer behavior

Across multiple channels of customer engagement – web,

retail, customer service

Multi-Channel Path Analysis

Understand customers paths to service Understand customers paths to service cancellationcancellation


*nPath analysis

Propensity-to-churn model

call drop outs

data drop outs in web (PDP) sessions

level of call quality (voice and data speed)

3G to 2G drop down and length of time on 2G.

sentiment analysis from call center records

Customer experience score

Propensity to churn

Path to churn

Good Score = Upsell opportunity

Bad Score = Retention Activities

Variation Score = Explanatory Message/Action

*nPath - pre-packaged SQL-MapReduce function for finding sequences of events

Customer Journey across Multiple Customer Journey across Multiple ChannelsChannels


Analysis across Diverse Sources & Data Analysis across Diverse Sources & Data Types Types


Discover Specific Service Cancellation Discover Specific Service Cancellation PathsPaths


Big Data & Churn Prevention

•Enrich Traditional Churn Model•Graph Analysis

•Multi-Channel Path Analysis

Business impact•With significantly less effort, know when customers are in the

last mile of considering leaving•Higher customer retention leading to lower costs and higher

profitability•Higher customer satisfaction

Detect & Prevent Customer Detect & Prevent Customer ChurnChurn

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