Challenges and opportunities in customer-led services

1. Challenges and opportunities in customer-led services James TaylorFair Isaac Corporation

2. 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 decisionsPRECISION In the same way, over and over againCONSISTENCY While being able to adapt “on-the-fly”AGILITY

3. 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. 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 http://www.f

5. 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. Adverse selection and micro-segmentation IdealPricingModel Price Over/Under-priced segments Risk

7. For what products will you see them? Value • Information • Insurance • Banking • Credit • Mass-Customizable • Clothing • Electronics • Long Tail • Books • Music • Content Expert Decisions Manual Decisions Manual Decisions Automated Decisions Complexity

8. 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. Current Example – Amazon.com

10. Getting closer with My amazon.com

11. Future Example - Personal online shopping • Site reconfigures itself to suit me • Explicitly through instructions (rules) • Implicitly though analysis (analytics) • Channels are integrated • Email, 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. Current Example – Online Banking

13. 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. 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. Technology Challenges

16. What rules look like If customer's debt exceeds customer’s assets then set the approval_status of customer’s application to Declined If flight’s onTimeReliability is less than 75% then flight’s valueToMe is “Low”. 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”

17. 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. 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. Bringing this all to bear Call Center Web ERP Email CRM http://ww Telemarketing OPERATIONAL SYSTEMS CHANNELS Billing Direct Mail SCM Store / Branch Rule & ModelRepository Kiosk / ATM Field BusinessRules Request for Decision Decision Service Rules Decision Models Decision Analysis AnalyticModels Data Customer Behavior and Strategy Performance

20. 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. Closing Thoughts • Consider customer-led service design • Think about micro-segmentation • Think about automation of decisions • Read my Blogs • Read my blog at http://www.edmblog.com • Read my (other) blog at http://www.eizq.net/blogs/decision_management • Subscribe to the blog(s) with RSS or email • E-mail me • jamestaylor@fairisaac.com • Ask me questions now!