Extreme Personalization and Markets of 1

Slide 2: About This Session  This session will… Explain and justify the concepts of extreme personalization  Discuss the relevant technologies  Give you an overview of an approach that works   This session will not… Dive deep into the technologies  Review individual products   About me… Blogger, writer and speaker on decision-making technologies  Background in development, product management, product marketing  Familiar with the technologies we will discuss  © 2007 Fair Isaac Corporation. 2

Slide 4: Some “Extreme Personalization” Stories  A Banking Story  A Retail Story  Characteristics of Extreme Personalization  Why Worry about Extreme Personalization Now? © 2007 Fair Isaac Corporation. Confidential. 4

Slide 5: A Banking Story  So, what if your bank... always identified you when you put your card in the ATM, called the call  center, handed over a check at the teller remembered your preferences  remembered your regular activities and prioritized them  accurately predicted your likely behavior/needs  applied constraints and circumstances (ATM wait time, call center wait time,  teller vs. personal banker) to its approach used the information you gave them, no matter how you gave it to them  © 2007 Fair Isaac Corporation. 5

Slide 7: 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 © 2007 Fair Isaac Corporation. 7

Slide 8: A Retail Story  So, what if an online retailer... always identified you when you visited the website  presented buying options/deals based on your purchasing history  provided recommendations based on what similar shoppers reviewed and  purchased provided customer service based on past shopping and browsing history  learned from your browsing history to provide a better, more efficient process  the next time you visit © 2007 Fair Isaac Corporation. 8

Slide 10: 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  Information available to improve my experience is used © 2007 Fair Isaac Corporation. 10

Slide 11: Characteristics of Extreme Personalization  Personalization  Channel Consistency Rewards Loyalty Stronger customer relationships   Analytic targeting Customers preferred channels   Rules for policies, preferences Customer value drives   interaction Predictions of responses  http://www.f  Pricing  Empowerment Variable pricing Fewer approvals, faster   decisions Multiple pricing mechanisms  More response-oriented  Shared value established  Third parties act like you  Customers can self-serve  © 2007 Fair Isaac Corporation. 11

Slide 12: Personalization and Targeting  More than just scripting responses  The best response changes in each situation  Providing personalization and targeting requires: Analytically derived segments for targeting  Rules to implement policies, regulations  Customer’s own rules and preferences  Predictions of responses  Correlation of data inputs  Rapid response  © 2007 Fair Isaac Corporation. 12

Slide 13: Consistency Across Channels  Stronger company-level customer relationships  More effective use of multiple http://www.f channels Web  Interaction context is intelligently incorporated Call Center  Customers choose preferred channels Email  Customer value, not channel, Customers Decisions drives interaction Mobile © 2007 Fair Isaac Corporation. 13

Slide 14: Empowerment  Call Center staff can handle customers better Fewer approvals needed  Faster decisions, while the customer waits  More response-oriented, less batch-oriented  Call Center focus on the human interaction, not the decision  Complex dialogs handled effectively   Agent or third party assistance Third parties can act like you and not just for you   Customers can self-serve more effectively © 2007 Fair Isaac Corporation. 14

Slide 15: Rewarding Loyalty  Align treatments with behavior  Treat consistently with needs  Self-service or faster service for a specific customer’s common activities  Consistent treatment across channels  Better targeting of loyalty offers  Show customers that sharing information is valuable © 2007 Fair Isaac Corporation. 15

Slide 16: Why Extreme Personalization Now?  There is growing acceptance of web-based self-service, of social networking and of highly interactive, rich-media websites  “The era of one size fits all is ending.” Chris Anderson of The Long Tail  Information about customers is a precious asset as it is hard, if not impossible, for competitors to replicate  In a recent survey some key findings included: In earning their loyalty, customers rate their quality of interactions as equally  important to the quality of the goods or services “Well-trained and helpful employees” is the top attribute of companies that  provide “consistently excellent” experiences  The survey identified characteristics of top companies Personal attention, reward for loyalty  Friendly and caring employees  High-quality goods or services  Excellent customer service  Well-trained and helpful employees  © 2007 Fair Isaac Corporation. 16

Slide 18: Web 2.0  Much of the focus on how to achieve personalization has been on technologies for improving the interaction itself AJAX, rich media, Mashups, Wikis, Blogs, Search…   Translating these technical buzzwords into platforms/tools that deliver personalized online experiences is the key  Behind all these interfaces, is a set of operational information systems that run the business with whom the consumer is interacting  If these systems are making “one size fits all” decisions the interaction will not feel personalized, no matter how much energy is invested in the front-end © 2007 Fair Isaac Corporation. 18

Slide 19: SOA, EDA  Service Oriented Architecture and Event-Driven Architecture  Applications as components assembled into composite applications  More functionality available in bite-sized chunks  Companies can assemble new processes and applications from components  More ability to respond to potentially complex events  But the applications being service-enabled or linked to events are not personalized or even very customer-centric  They also rely on “peopleware” for most of their smarts © 2007 Fair Isaac Corporation. 19

Slide 20: Analytics  Analytics can be applied to someone’s behavior on your site or to data you have about their behavior  Web analytics are often used to help personalize for both registered and anonymous visitors  Collaborative Filtering is used to utilize customers’ behavior to improve recommendations for a new customer  Different kinds of analytics are used separately to improve pieces of the customer experience  The different kinds of analytics are not integrated nor used to improve the behavior of systems at a fundamental level © 2007 Fair Isaac Corporation. 20

Slide 21: Mobile  There is a proliferation of channels and devices  These channels have many differences between them  Companies tend to develop different applications for different channels and devices to take advantage of their characteristics  Customers expect to be treated consistently across channels – Gold customers always want to be treated like a Gold customer no matter what device they use  Customers also want to choose their channel more and more and companies need to use the behavior on every channel to improve each channel © 2007 Fair Isaac Corporation. 21

Slide 22: The Missing Approach  What’s Missing?  Enterprise Decision Management  Technologies for Enterprise Decision Management  Using EDM to deliver the Best Next Action © 2007 Fair Isaac Corporation. Confidential. 22

Slide 23: The problem “ In any moment of decision, the best thing you can do is the right thing, the next best thing is the wrong thing, and the ” worst thing you can do is nothing. Theodore Roosevelt © 2007 Fair Isaac Corporation. 23

Slide 24: What’s Missing?  Delivering the right content, at the right time, through the right channel But what is content?  What about decisions? Information products?   How many kinds of customers do you have? How many segments or micro-segments?  Are you managing anonymous customers?   How much control does your customer really have  Self-service - what can your customers really do for themselves? © 2007 Fair Isaac Corporation. 24

Slide 25: Enterprise Decision Management  Management of decisions, especially front-line decisions, is key  Success requires precision, consistency and agility in making customer- facing decisions in real time  You must be able to link business execution to business strategy  Increasingly your ability to make the right decision quickly is a competitive advantage  Automating decisions frees up resources to focus on other issues Enterprise Decision Management is a systematic approach to managing and improving operational decisions across the enterprise. © 2007 Fair Isaac Corporation. 25

Slide 27: 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 On Time Reliability is less than 75% then flight’s value To Me 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” © 2007 Fair Isaac Corporation. 27

Slide 28: 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, Descriptive models can be used to but the results can be “categorize” customers – which can used in automated be useful in setting strategies and decisions – such as targeting treatment. offering a given product to a specific customer © 2007 Fair Isaac Corporation. 28

Slide 29: 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 Predictive models often rank-order called by a business individuals. For example, credit scores rank- rules engine to “score” an order borrowers by their credit risk – the individual or transaction, higher the score, the more “good” borrowers often in real time for every “bad” one. © 2007 Fair Isaac Corporation. 29

Slide 30: Collaborative Filtering  Automatic predictions for an individual’s needs, wants, desires based on information collected from many people  Information is specific to one user, though it’s gleaned from many users  Item-based collaborative filtering popularized by Amazon.com © 2007 Fair Isaac Corporation. 30

Slide 31: Advanced “Collaborative Filtering”  Generate accurate cross-sell and up-  Identify bridge products to entice sell recommendations purchases in higher value categories © 2007 Fair Isaac Corporation. 31

Slide 32: Segmentation and other issues  Segmentation can improve targeting  Easier to make predictions about segments  But how many segments?  What is the most predictive way to summarize purchase transactions?  What about unstructured information?                                     © 2007 Fair Isaac Corporation. 32

Slide 33: Bringing this all to bear Call Center Web http://ww Business Email ERP Rules Request Decision Service Telemarketing for CRM Decision OPERATIONAL Rules CHANNELS SYSTEMS Rule & Decision Direct Mail Model Billing Models Repository Store / Branch SCM Kiosk / ATM Decision Field Analysis Analytic Data Models Customer Behavior and Strategy 0 Performance 01 © 2007 Fair Isaac Corporation. 33

Slide 34: Using EDM to Deliver Personalization  Bring back the “mom and pop” approach to doing business…  … but scale it up  Build loyal customers across channels and over time  Don’t just collect information about customers, use it to improve their experience © 2007 Fair Isaac Corporation. 34

Slide 35: Key dimensions Extreme Personalization means directing the right action to the right individual through the right channel at the right point in time. Segment Direct Segment mail A Kiosk Plus numerous Plus multiple B Segment other possibilities other possibilities POS C Individual E-Mail w/ P-score >2830 Individual Right Right Phone w/ P-score <129 channel individual All In-store prospects Message Now A Right Right time action Message In 5 days B Offer In A response to Plus numerous Plus numerous Trigger A Offer In response to B other possibilities other possibilities No In Trigger B action Treatment response to Trigger C A © 2007 Fair Isaac Corporation. 35

Slide 37: More Data  As more human behaviors emit trails of digital residue, the more opportunities reside for algorithms to harness human-induced data and become information intermediaries  Reporting on data is not helpful; organizations must put their data to use in improving their business and customer interaction  Predictive analytics help organizations make predictions of customer behavior based on their history; the more fine-grained the data, the better the predictions  Decision latency is key - companies who “know” first do not necessarily win - companies must be able to act on what they know and do so faster than their competition © 2007 Fair Isaac Corporation. 37

Slide 38: More Self-Service  People want to do more for themselves  In general, nothing frustrates a customer more then not being able to get things done - no matter how nice your staff are or how helpful they try to be. If they can't actually do what the customer wants the customer will be frustrated  People expect every interaction, regardless of how or where it takes place, to be personalized to them  By focusing on the automation and improvement of customer-facing decisions, organizations can make self-service more probable, more rewarding and more extensive  Customers who want to self-serve will appreciate being able to do more if you replace the need to seek approval from an employee with an automated decision process © 2007 Fair Isaac Corporation. 38

Slide 39: More Complexity  Fewer experts, more machines  All touch points keep upgrading (cell phones, ATMs, kiosks)  As soon as one website does something, everyone needs to  Massive complexity requires automation - the complexity is increasingly in decisions and so a focus on automating decisions is required  The use of predictive analytics to predict appropriate actions of business rules to deliver on these actions is key © 2007 Fair Isaac Corporation. 39

Slide 40: More Devices  More and more devices are being created, each a potential channel  Form factors and user interaction are different  These devices are increasingly converged and crossing over traditional boundaries  The use of each device creates a unique data stream of a customer’s behavior  More devices mean less willingness to be forced to a particular channel  These devices are increasingly global © 2007 Fair Isaac Corporation. 40

Slide 41: More Mashing  Mashups: combining content from multiple sources into one, integrated experience Google, Yahoo! maps and social networking  YouTube  eBay and Amazon   If customers are building multi-company websites how can you personalize it for them? How can you make them sticky?  If your customer controls the context then how can you target them? © 2007 Fair Isaac Corporation. 41

Slide 43: Where to Apply EDM  Identify opportunities to automate decisions  The best customer decisions to automate are those that: Are regulated  Need to change frequently for competitive or product—mix reasons  Can be delivered across many channels  Should be controlled by your business people  Deliver strategic differentiation  Leverage the customer data you have or can get  Require more complex business solutions  © 2007 Fair Isaac Corporation. 43

Slide 44: The ROI from EDM  Can’t focus solely on cost savings; must also evaluate opportunity costs  There may be both subjective and objective considerations  Consider the value of: Precision or targeting  Consistency across channels and time  Agility in responding to competitors and market changes  Speed of making a decision to help a customer  Cost in reduced waste, fewer staff  © 2007 Fair Isaac Corporation. 44

Slide 45: Decision Small customer base Few channels Few brands Few SKUs Few locations Few pricing variations Few interactions per time period Long time period High value purchase High volume purchase Weak EDM candidate © 2007 Fair Isaac Corporation. Intensity Low High Large customer base Many channels Many brands Many SKUs Many locations Many pricing variations Many interactions per time period Short time period Low value purchase Low volume purchase 9 10 3 5 0 1 4 6 7 8 2 Strong EDM candidate 9 10 3 5 0 1 4 6 7 8 2 45

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