rulex bank presentation 20152701 ef2

Light Years Ahead.

Predicting & Preventing Banking Customer Churn By Unlocking Big Data. Case Study on a Bank.

All Rigths Reserved Rulex, Inc. 2014


CUSTOMERS CHURN. A key performance Indicator for Banks.

Confidence in the banking industry is on the rise, and trust in customers own financial services providers is high. But customers are on the move, with unprecedented access to competing banks and to new types of financial services providers. Banks must earn the highest levels of trust in order to retain customers, win more business and create genuine loyalty. Customer churn and engagement has become one of the top issues for most banks: It costs significantly more to acquire new customers than retain existing ones. It costs far more to re-acquire defected customers. CHURN IS ONE OF THE BIGGEST DESTRUCTORS OF ENTERPRISE VALUE FOR BANKS AND OTHER CONSUMER INTENSIVE COMPANIES.


CUSTOMERS CHURN. The Key issue: to know customers and predict churn with Rulex.

Pool of customers

ACTIVE

CHURNED

In order to identify early signs of potential churn you first need to start getting a holistic 360-degree view of your customers and their interactions across multiple channels. RULEX is able to aggregate the customer information across multiple channels and to focus on several key indicators that can flag propensity to churn. If you can easily detect these signs, YOU CAN TAKE SPECIFIC ACTIONS TO PREVENT CHURN. RULEX IS THE NATIVE TECHNOLOGY ABLE TO SOLVE DATA ANALYTICS CHALLENGES POSED BY TRADITIONAL TECHNOLOGY

CHURNING

Who, When and Why is going to churn.


CUSTOMERS CHURN. Why Rulex is LIGHT YEARS AHEAD?

With RULEX, banks can store, analyze and retrieve a massive volume and variety of data to aggregate the totality of information about the customer into a single platform

RULEX allows banks the economical advantage of storing data and scale it elastically to expand with the data volume growth

RULEX allows banks tap into a real-time data and customer interactions that provide clear insight into early warning signals to ensure timely retention offers and preservation of enterprise value

Rulex will build a model which will list the factors resulting in churn in order of importance in two weeks or less. Rulex will give you the business rules needed to take action to reduce churn.


HISTORICAL DATA Who did / didnt Churn

161405 past customers

75 attributes per each customers

Customer State? is the output variable.

It can be Actual or Former.

99961 customers did not churn: Customer State = Actual

61444 customers churned: Customer Stare = Former

112984 in the training set

48421 in the testing set

Bank Dataset:

Integer Nominal Continue Date


RULEX OUTCOME: THE CHURN MODEL 52 rules explaining the phenomenon

RULES

COVERING

ERROR CONDITION RELEVANCES

AUTOMATI

-CALLY INFERRED

!


RULEX OUTCOME: THE CHURN MODEL Details from the GUI

Rule # 41 IF (Customer Type is in a given subset) AND

IF (Account Balance SML


RULEX OUTCOME: THE CHURN MODEL Exploring the Rules Interface

COVERING Rule#41 is satisfied by 35.5% of 43083 churning cases

CONDITION RELEVANCES Removing Cond.1 from rule#41 increases the error by 41.5%. Cond.1 is extremely relevant!

ERROR Rule#41 gets wrong (false positive) in the 4.5% of the 69900 non-churning cases

AUTOMATI

-CALLY INFERRED

!


ATTRIBUTE RANKING How are churning customers characterized?

Account balance has a

(negative) relevance of about

37% for churning customers

(State=Former)

Customers who churn: Do not have deposits Has an old first purchase Belong to particular categories

(Customer type) Have a high Time since last

transaction

AUTOMATI

-CALLY INFERRED

!

Time since last transaction

has a (positive) relevance of

about 46% for churning

customers (State=Former)

BI tools can confirm the simplest conditions


Above 1 Free Saving Deposit, almost all customers are actual

Above 10000 Account Balance SML, almost all customers are Actual

but cannot find multi-condition rules. Rulex does, automatically.

CONFUSION MATRIX How good is the churn model?


HIGH ACCURACY: the Rulex model fits

about 78.5% of not churning customers,

and 84.2% of the churning ones.

UNBALANCE IMMUNITY: Rulex is

immune to intrinsic unbalances (churning is

less frequent than staying).

Customers with a churning behavior still active


Forecast

THE RULEX APPROACH Understand. Forecast. Decide.

who is going to churn? why? what are their drivers?

CHURN CANDIDATE LIST Who is going to churn & who is not


Previsions about new customers (are they churning?) are made quickly applying the rules to the available attributes.

This customer has already churned (and Rulex recognized it)

This customer has not churned yet but has a churn-like behavior.

WHO

List of customers

WHEN

prevision confidence

WHY

main applied

rule

Current state

Prevision

Automatic alarm / start

actions


Decide


You are the experts in your field.

With the knowledge provided by Rulex, now you can make effective decisions to

solve the problem of churn.



Churn Reduction

Using the rules and attribute relevancies,

the bank defined marketing and sale actions focused to reduce

the phenomenon at the origin.

EXPLICIT MODEL,

DESCRIBED BY RULES

(IF-THEN conditions)

Application of the Churn Model

to all customers, to test if they will

churn or not

Creation of the model from the past Application of the model for the future

Churn Candidate

List

Bank Historical Data Customer info, contract, transactions. Churn=yes/no.

Bank Actual Data Customer info, contract, transactions.

Churn Model List of rules and

drivers describing who churns

AUTOMATIC ALARM

Churn Prevention

The bank created a portfolio of actions

to be automatically activated when an alarm is received.

Understand

Forecast

Decide


CONCLUSIONS

Rulex makes Churn Analytics quick, automatic, precise and clear:

Data pre-processing: 1 minute Automatic model extraction: 20 seconds Clear view of:

Conditions of churning (rules) Relevance, for each attribute Critical thresholds, for each attribute

High accuracy Confidence of prevision for each customer

All Rights Reserved Rulex, Inc. 2014 All Rights Reserved Rulex, Inc. 2014

Light Years Ahead.

USA - 75 Federal Street, Suite 920 - 02110 Boston, MA, 02110 T: +1 617 263 0080 F: +1 617 263 0450 EUROPE - Via De Marini 16, 16th Floor - 16149 Genova (Italy) T: +39 010 6475218 F: +39 010 6475200

THANK YOU

USA -

Contacts

USA - 75 Federal Street, Suite 920 - 02110 Boston, MA, 02110 T: +1 617 263 0080 F: +1 617 263 0450 EUROPE - Via De Marini 16, 16th Floor - 16149 Genova (Italy) T: +39 010 6475218 F: +39 010 6475200

For more case studies, white papers and further information please go to

www.rulex-inc.com

or follow us on

or Contact me: Linda Treiman

[email protected]

Linda.Treiman1

rulex bank presentation 20152701 ef2

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