Page 1 Copyrighted material John Tullis Personalization Overview John Tullis DePaul Instructor [email protected]

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Copyrighted materialJohn Tullis

Personalization OverviewPersonalization Overview

John TullisDePaul [email protected]

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Why do it?

• To enhance user loyalty by providing personalized service and a highly customized user interface• To provide competitive advantage• Increase opportunity to cross- sell/ upsell• Lower Marketing Costs• Target advertising• Identify the Most Profitable Relationships

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Are you ready to do it?•What is your objective?

•Increasing Sales•Driving Web Traffic•Creating a knowledge base

•Have you defined your business rules?•Marketing rules•Fulfillment rules•Access rules

•Have you defined the Information Architecture for your site?•How is your site structured ?•How will the data be accessed ?

•What type of personalization do you plan to offer on your site?

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Competition in the Interactive AgeCompetition in the Interactive Age

Peppers and Rogers came out with the concept of one to one marketing in their book ‘The One to One Future: Building Relationships One Customer at a Time”. (1993). While book describes how technology makes personalization possible again, it does not mention Web.

Interesting point is that back in early 1900’s it was common for retailers to provide a high degree of personalization. With the advent of mass media such as TV, radio and print, mass marketing took over. Now we have the technology needed to personalize on a global level, providing personal experiences for great numbers of individuals. Marketing has come full circle in the interactive age.

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Competition in the Interactive AgeCompetition in the Interactive Age

““As the Interactive Age arrives, every enterprise will have toAs the Interactive Age arrives, every enterprise will have tolearn how to treat customers differently…learn how to treat customers differently…

...instead of selling one product at a time to as many customers ...instead of selling one product at a time to as many customers as possible in a particular sales period, the 1:1 marketer uses as possible in a particular sales period, the 1:1 marketer uses customer databases and interactive communications to sellcustomer databases and interactive communications to sellone customer at a time as many products and services as one customer at a time as many products and services as possible, over the entire lifetime of that customer’s patronage.possible, over the entire lifetime of that customer’s patronage.

‘‘Enterprise One to One’Enterprise One to One’, Don Peppers and Martha Rogers, , Don Peppers and Martha Rogers, PhDPhD

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Customer-driven Marketing ModelCustomer-driven Marketing Model

Customers Reached

Customers Reached

Nee

ds

Sat

isfi

edN

eed

s S

atis

fied

Aggregate-marketingTraditional mass- marketing

Customer-driven marketing

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Economics and the WebEconomics and the Web

•Traditional economics based on notion of scarcity:

•Human desires will always exceed available resourcesHuman desires will always exceed available resources.

• On the Web, the supply of available information far exceeds human demand - people feel deluged with information on a daily basis.

• The Web prohibits the use of mass-marketing techniques by its very nature. People can ‘tune out’ information they don’t want to see, and with the vast numbers of web sites out there, it is impossible for a marketer to blanket the web with any particular message.

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Law of Supply and DemandLaw of Supply and Demand

The main commodity in short supply on the Web The main commodity in short supply on the Web today is the today is the attentionattention of the people of the people who use it.who use it. To To win the Web marketing game, companies must win the Web marketing game, companies must compete to capture and sustain that attention.compete to capture and sustain that attention.

Example: all the ‘portal’ sites available - Yahoo, MS, InfoSeek, etc. To grab and hold customers you have to make a great first impression or they are off to the competition.

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The ‘Learning Relationship’The ‘Learning Relationship’

In their book ‘Enterprise One to One’, Don Peppers and

Martha Rogers explain the ‘Learning Relationship’ that can create a barrier which makes it more difficult for

a customer to ‘shop around’ than to remain loyal.

‘Switching costs’ is the marketing term to use here. Idea is to make the switching cost high

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3. Customer is satisfiedand returns to site.

2. Enterprise meets specifications and remembers them.

1. Customer tellsenterprise what hewants.

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•As the cycle continues, the customer spends more time and energy teaching the enterprise about his particular needs.

•After a few iterations, to get an equivalent level of service from any other company, the customer will have to gothrough the teaching process all over again. It becomesmuch easier to stay with original firm.

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This is an example of learning relationship at Amazon. As I purchase from Amazon, future recommendations are based upon those past purchases. The more I purchase, the better the recommendations get. After a while, I’ve invested considerable time with Amazon and they know me well. Barnes and Noble may have the best web site out there, but I’ll stay with Amazon because it’s a pain to re-establish a relationship with Barnes and Noble.

(That’s the theory, anyway!)

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Quantity vs. Quality Quantity vs. Quality

•Returning again and again to sites that offer real qualityReturning again and again to sites that offer real quality

•Looking to interact with companies, not just absorb Looking to interact with companies, not just absorb

•Expecting rewards for information sharedExpecting rewards for information shared

•Shopping for ‘information rich’ products, not Shopping for ‘information rich’ products, not commoditiescommodities

•Demanding self-serviceDemanding self-service

•Aligning themselves with brand names that they trustAligning themselves with brand names that they trust

As the Web matures, consumers are:As the Web matures, consumers are:

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How Do We Help Businesses Personalize?How Do We Help Businesses Personalize?

Businesses have significant challenges in becoming 1:1Businesses have significant challenges in becoming 1:1marketers. As e-business architects, we can help by enablingmarketers. As e-business architects, we can help by enablingtechnologies that assist in:technologies that assist in:

Identifying customers

Collecting information about customers and their needs

Storing and analyzing information collected

Delivering value-added services to customers

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How Do We Help Businesses Personalize?How Do We Help Businesses Personalize?

•Identifying customers:• Who is logging on to my site?• We can provide tools to entice customers to share information

•Collecting information• Databases to collect info - this is all basically a data question

•Storing and analyzing information collected• Data mining• Information and document management

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Personalization Software TechniquesPersonalization Software Techniques

Collaborative FilteringCollaborative Filtering - builds a profile of likes and dislikes - builds a profile of likes and dislikesand look for patterns you share with others, replicating theand look for patterns you share with others, replicating the‘‘word of mouth’ experience. (Net.Perceptions is an example.)word of mouth’ experience. (Net.Perceptions is an example.)

Case-based SystemsCase-based Systems - uses statistical modeling to turn a - uses statistical modeling to turn a database into a set of cases, which users navigate by answeringdatabase into a set of cases, which users navigate by answeringa series of questions. (PersonalLogic, but Net.Perceptions does a series of questions. (PersonalLogic, but Net.Perceptions does some of this also.)some of this also.)

Rules-based FilteringRules-based Filtering - generate databases of user profiles - generate databases of user profiles and/or content profiles. Patterns are transformed into and/or content profiles. Patterns are transformed into assumptions, or rules, which are used to predict future likes and assumptions, or rules, which are used to predict future likes and dislikes.dislikes.

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Personalization Software TechniquesPersonalization Software Techniques

Customer profilingCustomer profiling - - Combines shopper history, traditional demographics and interest profile. The . The "We know who you are and what you told us before” model (Firefly Passport is an example.)(Firefly Passport is an example.)

Parallel track systemsParallel track systems - - Used to provide multiple navigation paths through a web site. Visitors go down a different path, depending on where they came from. Language & session specific. (Product Advisor in WebSphere Commerce, PersonalLogic are examples.)

Neural Nets & Learning AgentsNeural Nets & Learning Agents - - tracking users' movements around the site and altering what is presented based on their click trails. (Example: Learn Sesame.)

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Personalization Software Personalization Software

Collaborative FilteringCollaborative Filtering• NetPerceptions• WiseWire • LikeMinds

Rules-based FilteringRules-based Filtering• BroadVision• WebSphere Commerce (Blaze)• Blue Martini (Blaze)

Case-based FilteringCase-based Filtering• Brightware• MultiLogic• PersonalLogic• Business Evolutions

Most current software choices fall into 3 of the 6 cases:

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BroadVision - Rules-based FilteringBroadVision - Rules-based Filtering

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• BroadVision was the main vendor of rules-based filtering systems. They have many high-level customers such as American Airlines, and are probably the major competitor in this space. This example shows how they are helping American maintain AAdvantage information in customer profiles, and then providing recommendations based on those preferences.• However, they have competition today - WebSphere Commerce & Blue Martini now offer rules based personalization based on Blaze technology, which is “non-proprietary” in the sense that it is provided by a 3rd party vendor to many organizations including: Active Software, IMA, ClickAction, e-solutions Software, etc.

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WebSphere Commerce - Rules-based FilteringWebSphere Commerce - Rules-based Filtering

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WebSphere Commerce - Rules-based FilteringWebSphere Commerce - Rules-based Filtering

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NetPerceptions - Collaborative FilteringNetPerceptions - Collaborative Filtering

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Blaze - Rules BasedBlaze - Rules Based

Rules & Business Objects: Integrated & Separated

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Brightware - Case-based AdviceBrightware - Case-based Advice

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Brightware uses an established set of cases to analyze requests for customer service. Product can use either email queries or web information. The customer walks through a set of questions to narrow down parameters of problem, and then based on the set of cases, a recommendation is made.

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Works through email alone,or through Web interaction.

Customer answers questions,and engine examines pastcases to find resolution.

Customer gets personalizedanswer in real time.

Documents

Page 1 Copyrighted material John Tullis Personalization Overview John Tullis DePaul Instructor [email protected]