Extreme Personalization and Markets of 1

Extreme Personalization for Markets of 1 James Taylor VP Enterprise Decision Management Fair Isaac February, 2007

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

Some “Extreme Personalization” Stories

Buzzwords in Extreme Personalization

The Missing Approach

Future Trends

Action Plan

A Banking Story

A Retail Story

Characteristics of Extreme Personalization

Why Worry about Extreme Personalization Now?

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

Current Example – Online Banking

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

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

Getting Closer with My amazon.com

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

Characteristics of Extreme Personalization

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

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

Consistency Across Channels

Stronger company-level customer relationships

More effective use of multiple channels

Interaction context is intelligently incorporated

Customers choose preferred channels

Customer value, not channel, drives interaction

Web Call Center Email Mobile Decisions Customers http://www.f

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

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

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

Web 2.0

SOA, EDA

Analytics

Mobile

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

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

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

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

What’s Missing?

Enterprise Decision Management

Technologies for Enterprise Decision Management

Using EDM to deliver the Best Next Action

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

“ ”

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?

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.

Key Concepts

Business Rules

Descriptive models

Predictive analytics

Collaborative Filtering

Segmentation and other issues

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 On Time Reliability is less than 75% then flight’s value To Me is “Low” If customer's debt exceeds customer’s assets then set the approval status of customer’s application to declined

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 – which can be useful in setting strategies and targeting treatment.

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.

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

Advanced “Collaborative Filtering”

Generate accurate cross-sell and up-sell recommendations

Identify bridge products to entice purchases in higher value categories

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?

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

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

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

More Data

More Self-Service

More Complexity

More Devices

More Mashing

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

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

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

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

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?

Where to apply EDM

The ROI from EDM

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

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

Decision Intensity Low 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 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 High 0 1 2 3 4 5 6 7 8 9 10 Strong EDM candidate Weak EDM candidate 0 1 2 3 4 5 6 7 8 9 10

Questions? Thank You James Taylor [email_address] http://www.edmblog.com