How To Understand Your Business Value From Big Data

Transcription

1 Stefan Ruhland Industry Consultant Banking Teradata Austria Teradata Big Data Analytics Zagreb Nov 5 th

2 AGENDA Teradata Big Data approach Banking Use Cases Conclusion 2

3 2014 Highlights: - Focus on Advanced Analytics and Big Data - Revenue 2.7B$; - Employees growth: from 6000 (2008) to more than (2014) - International: 77 New Accounts; +8% Revenue vs New Big Data Projects in EMEA - We bolstered our portfolio through acquisitions of 3 SW companies specialized on Big Data Analytics - We broadened our ecosystem of technology partners, with new and strengthened relationships with Cloudera, Hortonworks, MapR, and MongoDB

4 Teradata Big Data References 4 DISC F I N A N C I A L S E R V I C E S a n k q. MACHI NIMA COM C A S T e G I LT G R O U P E *. t l l f " T C H I N A E V E R B RIG HT B A N K BARNES ' NOBLE W I N D in s i g h t e x p r e s s : H S B C 'II:]ove rstoc k.com.. ed m 0 d 0 r d iscove r te>morrow. t o d a y A u r o r a H e a l t h Care Mobile. '\. C A N A L + ver1 z o n wireless mv / I L L I A l \ IS -S O TOJ.V A B a n k o f A m e r i c a M z inqa Aurora Health Care o t t o g r o u p L i n k e d ( m.. v o d o t o n e swisscom TERADATA.

5 AGENDA Teradata Big Data approach Banking Use Cases Conclusion 5

6 6 Is not about Volume, Velocity and Variety anymore. It is about how you use the data and analytics

7 Big Data Discovery Process: a complex iterative process Typical Challenges Data Acquisition Data Preparation Analysis LONG PROCESS Production 7

8 8 Big Data Reference Architecture Business Conceptual View

9 Agile Discovery Process Solving Typical Challenges Discovery Platform integrated with Hadoop, Teradata, Oracle data sources Data Acquisition Data Preparation Analysis FASTER PROCESS Production 9

10 EDW Model vs Big Data Discovery EDW Model Highly Planned & Controlled Slow Release Schedule 3x releases 2 years High Central funding cost $3m Release 1 3 Releases Over 2 years $3m Release 2 $3m Release 3 Low Risk / High Success March May July Sept Nov Jan March May July Sept Nov Discovery Model 40+ Projects Over 2 years Small Iterative Projects 40+ Discoveries / 2 years Low cost per project $50k-$70k per project BAU funded initiatives High Risk / High Fail Iterates to a new project March May July Sept Nov Jan March May July Sept Nov 10

11 EDW Model vs Big Data Discovery (cont d) EDW Model EDW projects must succeed Successful Discoveries productionised as part of release schedule $3m Release 1 3 Releases Over 2 years $3m Release March May July Sept Nov Jan March May July Sept Nov $3m Release 3 Discovery Model 40+ Projects Over 2 years Many projects fail Failure is accepted as part of the process and leads to new innovations and iterative projects Successful projects are often productionised on the EDW for execution March May July Sept Nov Jan March May July Sept Nov Successful Project 11 Failed Project

12 AGENDA Teradata Big Data approach Banking Use Cases Conclusion 12

13 Big Data Business Value Framework Overview of Key Areas Real & live POC/POV Idea Marketing Abandon online purchase Insight and action to drive follow up Sales Process Improvement ID and Improve sales process effectiveness Mktg Attribution ID the contribution of each contact to a sale Path to Churn ID the path leading to attrition Customer Experience Predict Complaint ID root cause and identify opportunity to intervene and fix People Like Me Affinity groupings refine people like me recommendations Customer Sat/NPS Understand the cause of dissatisfaction and loyalty Identify broken processes based on multi channel engagement Fraud Path to Fraud ID the detailed multichannel steps that precede fraud Fraud Networks Find connections between related parties Claims Fraud Identification of valid v fraudulent customer claims Online fraud Unusual usage of authenticated website based on context Risk Collections analytics Identify path to repay via collections Connection risk High risk associates via social or txn networks Pre default risk Path to default via golden path analysis Real Estate Pricing Using new data and techniques to enhance risk-based price Operational (Banking) Sales Compliance & Miss-Selling Detect key words that mislead client Service Efficiency ID the paths leading to high cost service calls and rectify cause Online T&C s follow up from optout or rapid T&C completion Call Centre Analytics Adherence to core processes and service standards at busy times Operational (Insurance) Reduction in manual Claims review Increased productivity Automate Claims notification Optimise handling and client satisfaction Advanced Risk & Pricing insights Minimise adverse selection Behavioral-based Pricing with Telematics data 13

14 14 Customer Journey

15 Understand Online Cross-Session Behaviour Using log data could help understand the customer journey The data looks like The web data provides you with a lot of facts: This visitor is interested in home loans & came from a competitor site They spent 6 minutes looking at 8 re-mortgage pages They spent 2 minutes on the 3 rd page reading the detail They run 3 re-mortgage calculator scenarios, then abandon then they called the call centre They want to borrow more than they currently owe We can see pressure on the level of hardcore borrowing 2 weeks ago they visited the getting married financial planning web pages for 30 minutes over 2 sessions 15

16 Remember What the Customer Tells You Capturing data from forms adds more insight Quote #1: Home Loan Term 12 Years Quote #2: Home Loan Term 20 Years The data tells you. This visitor is happy with the Purpose, Type & Value of loan. They are undecided over the term is this about affordability of monthly payments? Knowing the sticking point helps: Gives you a reason to contact the customer Gives you the hook to open the conversation The data looks like. Term then changed to 15 Years before session abandoned 16

17 Real-time capture of events and actions you might take Endless opportunities for customer contacts These financial service examples shows the strength of an integrated inbound real-time and outbound solution. When a special event occurs online, you can let your branch network, or personal advisor make a follow-up call. For for less urgent matters you can use a cheaper channel like SMS, E- mail or place a banner ad on the customers next visit. 17

18 Analysing Digital Journeys Sales Funnels Analysed Sales Funnels: - Personal Loan Quote - Savings Account - Current Account - New Credit Card - Mortgage Application 3. Affordability Details Leads can be delivered in session (via RTIM) and/or offline via CIM into the branc6h.oprecrasoll nal & Financial centre. Details Displayed 5. Personal & Financial Details Update 4. Personal & Financial Details Displayed 7. Review Application 2. Loan Quote Request 1. Application Data is also actionable - opportunity for personalised triggers based upon where customers abandon Teradata

19 Analysing Digital Journeys Sales Funnels Analysed Sales Funnels: - Personal Loan Quote - Savings Account - Current Account - New Credit Card - Mortgage Application 4. Personal & Financial Details Displayed 5. Personal & Financial Details Update 5. Review Application Teradata

20 Analysing Digital Journeys Sales Funnels Analysed Sales Funnels: - Personal Loan Quote - Savings Account - Current Account - New Credit Card - Mortgage Application 4. Personal & Financial Details Displayed Teradata H o m e p a g e Home p a g e Trial a process with forced update step, at least for certain fields, e.g. salary. H o m e p a g e H o m e p a g e H o m e p a g e Personal & Financial Details Update Accept Rate: 36% H o m e p a g e Review Application Accept Rate: 27% Closing the gap in accept rates is worth roughly 5-6k sales per year (worth $3m profit p.a).

21 21 Churn Prevention

22 Customer Retention Improvement Opportunity Churn Analysis Statistical & Pattern Matching techiques Space of all possible customers at risk of churn Churn Statistical customers that can be identified through Classic Statistics, e.g., SAS models Pattern Matching customers that can be identified through pattern matching via path analytics. 22

23 23 Events Preceding Account Closure Discovery Process First step

24 Events Preceding Account Closure Iterative Process Reducing the Noise to find the Signal Commission Reduction Request and Service Complaint seem to be Signals 24

25 25 Events Preceding Account Closure Insight Identification Most common event Sequences (aka golden path )

26 Path to Churn Outputs Three possible output Business Rules New input variables for current models New predictive models Triggers that need to be analyzed to determine whether the bank should add customer names to a list of potential defectors Identification of new statistically reliable input variables: Single Events Paths Frequent Sub-paths Building news predictive model from scratch: Event path based model Markov Chains 26

27 AGENDA Teradata Big Data approach Banking Use Case Conclusion 27

28 How can we help? Services on all layers of the Stack Strategic Consulting Implementation Analytics as a Service Strategic Consulting Strategic Consulting Service: address organizational changes Roadmap Service: What Big Data & Analytic Projects generate most revenue, where to start? Support Big Data Governance and Models Aster Analytics Platform All major Hadoop Distributors are Teradata Partners Platform Implementation DataLake Architecture Implementation From strategy to production: supporting the organization in making data become a first-class citizen Implementing Big Data & Analytic Projects in your organization Analytics as a Service Far-reaching between you r organization and Teradata Shared risk/benefit: for projects: only pay for what brings you value! Teradata Support

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30 30 Credit Risk

31 Consumer Credit Risk Models «Traditional» Machine-Learning Algorithms Transactions data Exploratory data analysis Model input variables (ADS) Forecast model (Decision Tree) Credit Bureaus data Account Balance data Transaction Data Number of Transactions Total inflow Total outflow Total expenses at discount stores Total clothing stores expenses Total restaurant expenses Total vehicle related expenses Total education related expenses... Credit Bureau Data Total Number of Trade Lines Number and balance of home loans Balance of all auto loans to total debt Total credit-card balance to limits... Deposit Data Savings account balance Checkingaccount balance CD account balance Brokerage account balance 31 Source: MIT - Massachusetts Institute of Technology - Khandani, Amir E., Adlar J. Kim, and Andrew W. Lo. Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance 34 (2010)

32 Consumer Credit Risk Models «Traditional» Machine-Learning Algorithms Transactions data Exploratory data analysis Model input variables (ADS) Forecast model (Decision Tree) Credit Bureaus data Account Balance data current crednit-ebuwreacuoamnaplylteicms suecnhtaasrcyreadint sacolyressis[e.g. FICO score] sed on slowly varying consumer characteristics are ba Path & Graph Analytics techiques machine-learning forecasts are considerably more adaptive, and are able to pick up the dynamics of changing credit cycles as well as the absolute levels of default rates In-database analyses, modeling and scoring of the entire dataset Improving current model (higher lift) Building new predictive models 32 Source: MIT - Massachusetts Institute of Technology - Khandani, Amir E., Adlar J. Kim, and Andrew W. Lo. Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance 34 (2010)

33 Credit Risk Business Improvement Opportunity Pre-Default Risk Reuse Data Preparation Same events used for Fraud Detection + some specific events The probability indicators of Default include: Missed payments Shift in spend from discretionary items to essentials; shift in location of spend from higher-end brands to lower-end brands Card balances generally increasing Canceling recurring subscription payments, e.g.magazines Shift in spend of debit to credit Changes in amount of direct deposit Changes in pattern of spending activity, e.g. someone fills up their car consistently at 8am prior to arriving and work and all of a sudden starts filling up in the middle of the day (potential indicator of a lost job) Starting to pull cash down off of a credit card, particularly telling when it is pulling cash down at particular locations like a Casino Increasing debt levels across all debt mechanisms... 33

34 Path Analysis of Account Balance Find Correlation between Account Balance and Default Risk 1.5 Account Balance A Path Analysis Statistical Analysis B C SAX (Symbolic Aggregate approximation) Time [day] BCCBAB 34

35 Banking Analytics Use cases Marketing Path to Churn. Enable you to study your customers omni-channel behavior, in order to discover Abandonment Paths, that are sequences of events/behaviors that frequently lead to customers churn. These insights allow to improve churn prediction models and are complementary to traditional approaches. Pre-built Path and Predictive analytics functions Multi-channel Attribution. Help you quantifying channels effectiveness to drive revenue, in order to identify which channels/ads perform the best, calculate true ROI on a per ad basis and/or run time-sensitive promotions by knowing which ads convert the fastest. Pre-built Attribution and Path analytics functions Location Based Offers. Allow you to analyze the locations most frequented by customers and identify the types of spend for each customers and the brand share of that spend. The goal is to improve customer loyalty by providing usage based offers for Credit and Debit Cards and select Merchants to partner with for location based offers. Pre-built Path, Graph and Statistical analytics functions 35

36 Banking Analytics Use cases Marketing Abandoned online Purchase. Enable you to understand in detail how customers progress through the online sales process, in order to identity, understand and fix broken processes where customers exit, get stuck or cycle back. Outcomes are higher conversion and efficiency at each step and more revenue from sales, at a lower cost per sale because re-work is reduced. Pre-built Path and Graph analytics functions Personalized Recommendation Analysis. Allows you to make product recommendations when you knows very little about the customer (e.g. customer is inactive or holds only 1 product), using individual customer browsing combined with people like you purchase behaviors. The goal is to improve product penetration amongst segments that are either inactive or only holding one. Pre-built Path, Association and Cluster Analytics functions 36

37 Banking Analytics Use cases Fraud Path to Fraud. Enable you to analyze cross-channel customer activities to identify common sequence of events leading up to a fraudulent transaction. This new crosschannel fraud prediction path analysis is a substantial improvement vs. the current fraud models and it's complementary to them. Pre-built Path and Clustering analytics functions Fraud Networks. Enable you to use graph-based approaches to uncovering anomalies in customers' graph, where the anomalies consist of unexpected entity/relationship and patterns that are often related with fraud behavior. As with path to fraud, these insights allow to significantly improve fraud prediction models. Pre-built Statistical, Graph and Path analytics functions 37

38 Banking Analytics Use cases Credit Risk Pre-Default Risk. Enable you to identify genuine signs of default pressure, through the analysis and comparison of events, transactions, interactions and changes over time. The key objective is to discover customer behaviors that frequently lead to customers default and translate them in Business Rules. Pre-built Association and Path analytics functions Connections Risk Consumer & B2B Networks. Allow you to build and analyze customers network, in order to identify explicit and implicit associations and actionable insights that can be vitally important for credit risk detection. Pre-built Path, Graph and Predictive analytics functions 38