Date post: | 16-Sep-2015 |
Category: |
Documents |
Upload: | kevindsiza |
View: | 14 times |
Download: | 5 times |
Light Years Ahead.
Predicting & Preventing Banking Customer Churn By Unlocking Big Data. Case Study on a Bank.
All Rigths Reserved Rulex, Inc. 2014
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.
All Rigths Reserved Rulex, Inc. 2014
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.
All Rigths Reserved Rulex, Inc. 2014
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.
All Rigths Reserved Rulex, Inc. 2014
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
All Rigths Reserved Rulex, Inc. 2014
RULEX OUTCOME: THE CHURN MODEL 52 rules explaining the phenomenon
RULES
COVERING
ERROR CONDITION RELEVANCES
AUTOMATI
-CALLY INFERRED
!
All Rigths Reserved Rulex, Inc. 2014
RULEX OUTCOME: THE CHURN MODEL Details from the GUI
Rule # 41 IF (Customer Type is in a given subset) AND
IF (Account Balance SML
All Rigths Reserved Rulex, Inc. 2014
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
!
All Rigths Reserved Rulex, Inc. 2014
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
All Rigths Reserved Rulex, Inc. 2014
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?
All Rigths Reserved Rulex, Inc. 2014
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
All Rigths Reserved Rulex, Inc. 2014
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
All Rigths Reserved Rulex, Inc. 2014
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
All Rigths Reserved Rulex, Inc. 2014
Decide
THE RULEX APPROACH Understand. Forecast. 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.
All Rigths Reserved Rulex, Inc. 2014
THE RULEX APPROACH Understand. Forecast. Decide.
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
All Rigths Reserved Rulex, Inc. 2014
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
Linda.Treiman1