Predicting & Preventing Banking Customer Churn By Unlocking Big Data.

Transcription

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

2 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.

3 CUSTOMERS CHURN. The Key issue: to know customers and predict churn with Rulex. Pool of customers ACTIVE CHURNED CHURNING 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 Who, When and Why is going to churn.

4 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.

5 HISTORICAL DATA Who did / didn t Churn Bank Dataset: past customers 75 attributes per each customers in the training set in the testing set Customer State? is the output variable. It can be Actual or Former customers did not churn: Customer State = Actual customers churned: Customer Stare = Former Integer Nominal Continue Date

6 RULEX OUTCOME: THE CHURN MODEL 52 rules explaining the phenomenon AUTOMATI -CALLY INFERRED! RULES COVERING ERROR CONDITION RELEVANCES

7 RULEX OUTCOME: THE CHURN MODEL Details from the GUI AUTOMATI -CALLY INFERRED! Rule # 41 IF (Customer Type is in a given subset) AND IF (Account Balance SML <= 0.135) THEN (The Customer Churns)

8 RULEX OUTCOME: THE CHURN MODEL Exploring the Rules Interface COVERING Rule#41 is satisfied by 35.5% of 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 non-churning cases AUTOMATI -CALLY INFERRED!

9 ATTRIBUTE RANKING How are churning customers characterized? Time since last transaction has a (positive) relevance of about 46% for churning customers (State=Former) Account balance has a (negative) relevance of about 37% for churning customers (State=Former) AUTOMATI -CALLY INFERRED! 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

10 BI tools can confirm the simplest conditions Above 1 Free Saving Deposit, almost all customers are actual Above Account Balance SML, almost all customers are Actual but cannot find multi-condition rules. Rulex does, automatically.

11 CONFUSION MATRIX How good is the churn model? Customers with a churning behavior still active 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).

12 THE RULEX APPROACH Understand. Forecast. Decide. Forecast who is going to churn? why? what are their drivers?

13 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) WHO List of customers Current state Prevision WHEN prevision confidence WHY main applied rule This customer has not churned yet but has a churn-like behavior. Automatic alarm / start actions

14 THE RULEX APPROACH Understand. Forecast. Decide. 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.

15 THE RULEX APPROACH Understand. Forecast. Decide. Bank Historical Data Customer info, contract, transactions. Churn=yes/no. Creation of the model from the past EXPLICIT MODEL, DESCRIBED BY RULES (IF-THEN conditions) Application of the model for the future Application of the Churn Model to all customers, to test if they will churn or not Bank Actual Data Customer info, contract, transactions. Forecast Churn Candidate List Churn Model List of rules and drivers describing who churns Churn Reduction Using the rules and attribute relevancies, the bank defined marketing and sale actions focused to reduce the phenomenon at the origin. Understand AUTOMATIC ALARM Decide Churn Prevention The bank created a portfolio of actions to be automatically activated when an alarm is received.

16 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

17 THANK YOU Light Years Ahead. All Rights Reserved Rulex, Inc USA - 75 Federal Street, Suite Boston, MA, T: F: EUROPE - Via De Marini 16, 16th Floor Genova (Italy) T: F:

18 Contacts For more case studies, white papers and further information please go to or follow us on USA - or Contact me: Linda Treiman [email protected] Linda.Treiman1 USA - 75 Federal Street, Suite Boston, MA, T: F: EUROPE - Via De Marini 16, 16th Floor Genova (Italy) T: F: