Customer Centricity Master Data Management and Customer Golden Record. Sava Vladov, VP Business Development, Adastra BG

Transcription

1 Customer Centricity Master Data Management and Customer Golden Record Sava Vladov, VP Business Development, Adastra BG 23 April 2015

2 What is this presentation about? Customer-centricity for Banking and Insurance business The Challenge of building Long-term Profitable Customer relationship Customer-centricity in practice Master Data Management & Customer Golden Record fundamentals Banking industry - typical MDM/ Customer Golden record benefits Insurance: Policy-centricity vs. Customer-centricity Does it work? Sample business case calculations of MDM Adastra Fast Facts 2

3 The Challenge Long-term Profitable Customer relationship Business improvement priority: Customer data consolidation and Single Customer view for all Business Units Internet-globalized business reality with competition just one click away; Customers expect to be understood and treated differently based on their individual preferences; Customers insist on receiving consistent and logical customer service. They do not tolerate having one and the same question asked once again or being offered the promotion which they have already declined; Ocean of fragmented data of all types and formats, i.e. transforming data to useful information and business insight is more challenging than ever. Customer-centric business objectives for the Financial institutions: Take advantage of consolidated data for better customer understanding not all customers, not the average customer, but each customer individually; Extend Customer relationship on every level by treating the individual customer according to his/ her preferences; Predict behaviour and anticipate next customer need - purchase or request; Extract, analyze and use customer data to: make better business decisions; introduce more effective communication programs; deliver better products and services. 3

4 Invalid or artificial Personal dentification Number (Passport ID) Standard issues within Client data...what can be the impact? Missing Personal Identification number (Passport ID) Massive impact on CRM, sales, risk management and customer processes Wrong classification of subject Missing Name, Surname Duplicated clients Missing title, or written in wrong field Missing address information on client instance Missing residency status Missing subject categorization Address written in one field, resp. not standardized Missing Street, LRN/SN, City, ZIP Multiple contact information, not clear contact type Not valid DoB, Gender Invalid ZIP for given City/Address Missing or nonstandard company name Company name in different field e.g. surname Wrong customer contact data (phones, addresses) Wrong customer identification data (names, surnames, Personal ID) Wrong customer descriptive data (gender, DoB) No standards for data entry (pensioner, address), incomplete input checks on entry gate Manual flags for customers (unofficial death info, opt out from campaign per channel) Assignment of clients to branches, active/non active flag 1. Not possible to determine numbers of customers 2. Showing multiple records of same person in CRM 3. Not possible to determine campaign efficiency 4. Not possible to set campaign targets 5. Cost of returned mails more contacts, different data, etc 6. Lost opportunity ineffective campaigns 7. Client evidence /duplicated clients 8. Unavailability to send personalized offers to the clients 9. Non-recoverable costs (e.g. fraud) 10. Wrong decisions/actions/projection/ 11. Fines/legal/liability exposure costs 12. Data verification /cleanup /correction costs 13. Wrong decision about product offers 14. And many others 4

5 The Master Data problem G Pamukov Georgy Pamukov Finance Loans Georgi Pamukov Joro Pamukov Core Banking CRM George Pamukov Fragmented Inconsistent Duplicated Incomplete Multiple data entry - Inefficient Data replication - Information loss Restrictions - Business process interruptions Increased process complexity 5

6 What is Master Data? Core Business Entity Data describing the most important business entities of the enterprise Shared Data used (shared) by multiple business processes and systems Uniquely Identified Data having/forming Unique IDs, which are referenced by other data Rarely Changed Data that is relatively stable and rarely changes Changes need to be propagated 6

7 Master Data Examples The most typical examples of Master Data are Party (360 degree view) Examples include customer, person, company, vendor, supplier, partner, employee, etc. Location data with its address identifiers, territorial hierarchies Product data together with its hierarchies Code tables and other common reference data. Party Other Product 7

8 Master Data Management Adastra methodology Data enrichment from reference sources Address registers (postal code, street name and number, city, etc.) Geo-codes add geo-coding information to facilitate display of map locations Unification and de-duplication within and across data holdings Match identify a set of records about the same party Merge create a golden record best representation of a party Master data hub (repository) - Company, Person, Product, etc. Master data repository and/or pointers for unique identification of all customer records in the transactional systems 8

9 Master Data Matching and Unification Original Data Cleansed Data Party Records Unique Party Records Customer, Employee, Vendor, Cleanse, Standardize Enrich Match Unify 9

10 Data Integration Solution Architecture with MDM The framework of processes and technologies focused on creating and maintaining an authoritative, reliable, sustainable, accurate and secure data environment that represents a single version of the truth an accepted system of record. Transaction Systems Finance Core Banking MDM Solution Data Quality Cleansing Standardization Identification Unification Knowledge Bases Metadata Front End Master Data Repository Data Integration (ETL, EAI, SOA) CRM Data Marts Loans Operational Data Store Enterprise Data Warehouse Data Integration 10

11 MDM Architectural Styles Consolidation MDM Data is acquired from the source system into the MDM repository, but not updated back. Centralized MDM Data in the source systems and the MDM repository and updated synchronously. AKA Transaction style Coexistence MDM An offline mechanism is implemented to update the source system data from the MDM repository. Registry MDM The MDM repository only keeps pointers of where the data is. On request the data is federated and consolidated. - Original Data - Master Data 11

12 Typical MDM benefits for Banking Provide subsidiaries, business departments and partners with timely, correct and accurate client information while complying with legislation Consolidate all communication to and from a particular client regarding all products, service requests, offers, approval process, collections, etc. Manage client relationship, communication and service levels according to the CLTV Identify and prevent client s attrition Comply with regulatory requirements e.g. Basel II, III Sarbanes-Oxley, anti-money laundering reporting and national regulators reports. 12

13 Household Policy-Centricity vs. Customer-Centricity Insured Life Insured Owner Beneficiary Insured Mary-Ann Spouse Customer-centricity information assets: Single and complete view of the customer Complete product portfolio All customer assets Insured Lifetime value Owner All interaction Life Auto Beneficiary Owner Driver Owner Child Jason Preferences Life stage, life events Household Roles and relationships Segments - improved direct marketing and promotion decisions Beneficiary Home Peter 13

14 Does it work? Sample business case calculations of DQ / MDM Area Description Impact CRM Loan underwriting Fraud management Collections Campaign management accuracy improvement based on better targeting Better client contactability, communication savings (IVR efficiency, no unreturned mail, no duplicities in contacting customers) Scorecard accuracy improvement Prevention of wrong approvals based on blacklisted clients/employers/cell phones/fraud suspicious cases/client delinquency Identification of fraudulent and suspicious cases Proper quality used for Statistical fraud scorecard Early detection based on concentrations and velocities Early collections - Higher recovery due to better contact rate, Higher efficiency of skip tracing, Better performing early warning system (e.g. considering employer business problems lay-offs) Late collections + recovery - Lower provisions due to better collateral handling P/L impact of improved conversion rate of CRM campaigns is 5 13% EBIT 22% higher contact rate Cost of risk reduced & better portfolio quality, Risk loss improved from 4,4Mio EUR to 3,7Mio EUR Business case of 110 Mio RUR (FSTPD90 rate improvement) Lower losses from fraud due to accurate model for identification of fraud suspicions 7.1M EUR Decreased fraud losses due to faster detection and stopping the fraud spread 0.8M EUR Better client contactiblity improves collection effectiveness by 10-15%. Problematic clients are identified earlier and harder collection strategy is applied. Probability to collect is significantly improved. Improved contact rate at early stages =>Compound flow rate improvement => 7.5% (vs. originally 10.1%) of portfolio reaches 180+ and are written off 14

15 15 Adastra Fast Facts

16 Adastra solutions for Banking and Insurance Adastra Bulgaria Ltd. 38B Cherni vrah Blvd. Sofia 1407, Bulgaria 16