Challenges and opportunities in customer-led services James Taylor Fair Isaac Corporation

2 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Enterprise Decision Management Enterprise Decision Management (EDM) is a systematic approach to automate and improve decisions across the enterprise. It allows businesses to: Make more profitable and targeted decisions PRECISION In the same way, over and over again CONSISTENCY While being able to adapt “on-the-fly” AGILITY

3 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Agenda  Customer-led services  What are they  Why are they going to happen  Where are they going to happen  Some examples  Present  Future  Challenges with customer-led services  Organizational  Technological  Ethical

4 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Characteristics of Customer-led Services  Personalization  Rewards Loyalty  Analytic targeting  Rules for policies, preferences  Predictions of responses  Channel Consistency  Stronger customer relationships  Customers preferred channels  Customer value drives interaction  Pricing  Variable pricing  Multiple pricing mechanisms  Shared value established  Empowerment  Fewer approvals, faster decisions  More response-oriented  Third parties act like you  Customers can self-serve

5 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Why Customer-led Services?  Growing important of information in products  Response to threats to traditional business from the explosion and prevalence of the Internet  Price transparency  Customer mobility and a lack of loyalty  The Long Tail  An opportunity to create competitive advantage from customer data

6 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Adverse selection and micro-segmentation Price Risk Ideal Pricing Model Over/Under-priced segments

7 Copyright © 2003 Fair Isaac Corporation. All rights reserved. For what products will you see them?  Information  Insurance  Banking  Credit  Mass-Customizable  Clothing  Electronics  Long Tail  Books  Music  Content Complexity Value Automated Decisions Expert Decisions Manual Decisions Manual Decisions

8 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Current Example - Pay as you drive insurance  Logical extension of micro-segmentation  Use of a far broader range of variables and predictive analytics  Precisely rate the risk presented by individual consumers.  Static measures of risk  Driver's age  Driving history  Commuting distance  Dynamic measures  Speed  Time of day  Location  A pricing band for every single policyholder they serve

9 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Current Example – Amazon.com

10 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Getting closer with My amazon.com

11 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Future Example - Personal online shopping  Site reconfigures itself to suit me  Explicitly through instructions (rules)  Implicitly though analysis (analytics)  Channels are integrated  , IM, Mobile, Phone, Store(s), Mashups  Choices and actions (or comments) in one affect the others  Offers, pricing, shipment are dynamic  Based on the specific purchase consideration  Loyalty is rewarded  If information is available that could improve my experience, it is used

12 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Current Example – Online Banking

13 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Future Example - Personal banking  The website does more than show my accounts  It stops asking me to open accounts I have  It stops asking for information for new accounts that it already has  It makes recommendations on credit cards it does not just list them  It feeds information about what I look at into offer models  Pricing and offers are made in real time to suit me  It makes it easy for me to do the things I always do  And so on…  Meanwhile…  The ATM remembers you and reconfigures itself  The IVR reconfigures based on wait times, status, past behavior …  The monthly statement highlights out of pattern activities  Branch staff make intelligent suggestions based on your recent behavior and the behavior of successful customers with a similar profile

14 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Challenges in developing customer-led services  Organizational  Design, deployment, lifecycle, innovation...  Some banks now release hundreds of new products a month  Price transparency and intra-P&L pricing  Channel consistency  Ending the separation between back and front office  Ethical  Data privacy  Business mashups and privacy  Cross-border regulations

15 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Technology Challenges TECHNOLOGYDESCRIPTIONIssues Data Access & Management Acquire, access, deliver and manage data from internal and external sources Privacy Real-time Descriptive Analytics Analytics for analyzing and understanding individual and group behavior Demographics Data Sources Predictive Analytics Analytics for predicting individual behavior and for identifying best actions to meet objective Legality Balance v choices Business Rules Software for defining, testing and executing rules, processes and strategies Ownership Change management Auditing DeploymentIntegrating services into delivery processes and systems Process integration Third-party integration

16 Copyright © 2003 Fair Isaac Corporation. All rights reserved. What rules look like If (vehicle’s age is between 0 years and 8 years) and (policyholder’s age is between 21 years and 60 years) and (policyholder’s number_of_claims does not exceed 3) Then set policyholder’s case to “STANDARD” If flight’s onTimeReliability is less than 75% then flight’s valueToMe is “Low”. If customer's debt exceeds customer’s assets then set the approval_status of customer’s application to Declined

17 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Descriptive Models Identify Relations Use: Find the relationships between customers Example: Sort customers into groups with different buying profiles. Operation: Analysis is generally done offline, but the results can be used in automated decisions – such as offering a given product to a specific customer Descriptive models can be used to categorize customers into different categories – which can be useful in setting strategies and targeting treatment.

18 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Predictive Models Calculate Risk Or Opportunity Use: Identify the odds that a customer will take a specified action Example: Will the customer pay me back on time? Will the customer respond to this offer? Operation: Models are called by a business rules engine to “score” an individual or transaction, often in real time Predictive models often rank-order individuals. For example, credit scores rank- order borrowers by their credit risk – the higher the score, the more “good” borrowers for every “bad” one.

19 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Models Bringing this all to bear Rules Analytic Models Business Rules Decision Service Rule & Model Repository Data Request for Decision Decision Call Center Web Telemarketing CHANNELS Direct Mail Store / Branch Kiosk / ATM Field ERP CRM OPERATIONAL SYSTEMS Billing SCM Decision Analysis Customer Behavior and Strategy Performance

20 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Fair Isaac Corporation – Automating decisions for 50 years  Founded in 1956  NYSE symbol: FIC  Annual revenues over $800 million  Market cap: Over $3 billion  3,000 employees  Software engineers, PhDs, data analysts, consultants…  Background in analyzing data, predicting outcomes, making decisions  Credit scoring  Customer acquisition / origination / management  Risk assessment  Fraud detection

21 Copyright © 2003 Fair Isaac Corporation. All rights reserved. Closing Thoughts  Consider customer-led service design  Think about micro-segmentation  Think about automation of decisions  Read my Blogs  Read my blog at  Read my (other) blog at  Subscribe to the blog(s) with RSS or  me   Ask me questions now!