Rulex Bank Presentation 20152701 Ef2

Predicting & Preventing BankingCustomer Churn By Unlocking Big Data. Case Study on a Bank. Light Years Ahead. All Rigths Reserved © Rulex, Inc. 2014 CUSTOMERS CHURN. 2014 . with unprecedented access to competing banks and to new types of financial services providers. win more business and create genuine loyalty. All Rigths Reserved © Rulex. and trust in customers’ own financial services providers is high. A key performance Indicator for Banks. CHURN IS ONE OF THE BIGGEST DESTRUCTORS OF ENTERPRISE VALUE FOR BANKS AND OTHER CONSUMER INTENSIVE COMPANIES. But customers are on the move. 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. Inc. Confidence in the banking industry is on the rise. • It costs far more to re-acquire defected customers. Banks must earn the highest levels of trust in order to retain customers. The Key issue: to know customers and predict churn with Rulex. Pool of customers ACTIVE 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.CUSTOMERS CHURN. 2014 . Inc. 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. RULEX IS THE NATIVE TECHNOLOGY ABLE TO SOLVE DATA ANALYTICS CHALLENGES POSED BY TRADITIONAL TECHNOLOGY CHURNED Who. When and Why is going to churn. All Rigths Reserved © Rulex. YOU CAN TAKE SPECIFIC ACTIONS TO PREVENT CHURN. 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. All Rigths Reserved © Rulex.CUSTOMERS CHURN. Why Rulex is LIGHT YEARS AHEAD? With RULEX. banks can store. 2014 . Rulex will give you the business rules needed to take action to reduce churn. Inc. 2014 .HISTORICAL DATA Who did / didn’t Churn Bank Dataset: 112984 in the training set 48421 in the testing set 161405 past customers 75 attributes per each customers Customer State? is the output variable. 99961 customers did not churn: 61444 customers churned: Integer Nominal Customer State = Actual Customer Stare = Former Continue Date All Rigths Reserved © Rulex. It can be “Actual” or “Former”. Inc. RULEX OUTCOME: THE CHURN MODEL 52 rules explaining the phenomenon AUTOMATI -CALLY INFERRED ! RULES COVERING ERROR CONDITION RELEVANCES All Rigths Reserved © Rulex. Inc. 2014 . 135) THEN (The Customer Churns) All Rigths Reserved © Rulex. 2014 .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. Inc. 5% of 43083 churning cases CONDITION RELEVANCES Removing Cond. Inc.1 from rule#41 increases the error by 41. Cond.1 is extremely relevant! ERROR Rule#41 gets wrong (false positive) in the 4.5% of the 69900 non-churning cases All Rigths Reserved © Rulex.RULEX OUTCOME: THE CHURN MODEL Exploring the Rules Interface COVERING Rule#41 is satisfied by 35.5%. 2014 AUTOMATI -CALLY INFERRED ! . 2014 .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 All Rigths Reserved © Rulex. Inc. Inc. Rulex does. automatically. almost all customers are actual … but cannot find multi-condition rules. All Rigths Reserved © Rulex. almost all customers are Actual Above 1 Free Saving Deposit.BI tools can confirm the simplest conditions … Above 10000 Account Balance SML. 2014 . 2014 . UNBALANCE IMMUNITY: Rulex is immune to intrinsic unbalances (churning is less frequent than staying). All Rigths Reserved © Rulex.CONFUSION MATRIX How good is the churn model? Customers with a churning behavior still active HIGH ACCURACY: the Rulex model fits about 78. Inc. and 84.2% of the churning ones.5% of not churning customers. Inc. Forecast • who is going to churn? • why? • what are their drivers? All Rigths Reserved © Rulex.THE RULEX APPROACH Understand. 2014 . Forecast. Decide. Inc. 2014 Prevision .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. WHO WHEN WHY List of customers prevision confidence main applied rule Current state This customer has already churned (and Rulex recognized it) This customer has not churned yet but has a “churn-like” behavior. Automatic alarm / start actions All Rigths Reserved © Rulex. With the knowledge provided by Rulex. Decide. now you can make effective decisions to solve the problem of churn. Inc.THE RULEX APPROACH Understand. Forecast. All Rigths Reserved © Rulex. Decide You are the experts in your field. 2014 . Churn=yes/no. Decide. Application of the Churn Model to all customers. DESCRIBED BY RULES (IF-THEN conditions) Churn Model List of rules and drivers describing who churns Churn Reduction Using the rules and attribute relevancies. transactions. Inc. Forecast EXPLICIT MODEL. Bank Historical Data Customer info. Forecast. the bank defined marketing and sale actions focused to reduce the phenomenon at the origin. contract.THE RULEX APPROACH Understand. contract. 2014 . to test if they will churn or not Churn Candidate List Understand AUTOMATIC ALARM Decide Churn Prevention The bank created a portfolio of actions to be automatically activated when an alarm is received. Creation of the model from the past Application of the model for the future Bank Actual Data Customer info. transactions. All Rigths Reserved © Rulex. precise and clear: • Data pre-processing: 1 minute • Automatic model extraction: 20 seconds • Clear view of:  Conditions of churning (rules)  Relevance. for each attribute • High accuracy • Confidence of prevision for each customer All Rigths Reserved © Rulex. for each attribute  Critical thresholds. Inc.CONCLUSIONS Rulex makes Churn Analytics quick. automatic. 2014 . MA.02110 Boston.THANK YOU Light Years Ahead. Suite 920 . 16th Floor .Via De Marini 16. Inc. 2014 USA .16149 Genova (Italy) T: +39 010 6475218 F: +39 010 6475200 . All Rights Reserved © Rulex. 02110 T: +1 617 263 0080 F: +1 617 263 0450 EUROPE .75 Federal Street. 02110 T: +1 617 263 0080 F: +1 617 263 0450 EUROPE .16149 Genova (Italy) T: +39 010 6475218 F: +39 010 6475200 .com or follow us on USA - or Contact me: Linda Treiman Linda. white papers and further information please go to www.75 Federal Street.02110 [email protected] Linda. 16th Floor . Suite 920 . MA.Via De Marini 16.Treiman1 USA .Contacts For more case studies.