page 1 Copyrighted material John Tullis Personalization Overview John Tullis DePaul Instructor

page 2 Copyrighted material John Tullis Personalization Overview 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

page 3 Copyrighted material John Tullis Personalization Overview 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?

page 4 Copyrighted material John Tullis Competition 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.

page 5 Copyrighted material John Tullis Competition in the Interactive Age “As the Interactive Age arrives, every enterprise will have to learn how to treat customers differently…...instead of selling one product at a time to as many customers as possible in a particular sales period, the 1:1 marketer uses customer databases and interactive communications to sell one customer at a time as many products and services as possible, over the entire lifetime of that customer’s patronage. ‘Enterprise One to One’, Don Peppers and Martha Rogers, PhD

page 6 Copyrighted material John Tullis Customer-driven Marketing Model Customers Reached Needs Satisfied  Aggregate-marketing  Traditional mass- marketing  Customer-driven marketing

page 7 Copyrighted material John Tullis Economics 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.

page 8 Copyrighted material John Tullis Law of Supply and Demand The main commodity in short supply on the Web today is the attention of the people who use it. To win the Web marketing game, companies must 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.

page 9 Copyrighted material John Tullis 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

page 10 Copyrighted material John Tullis The ‘Learning Relationship’ 3. Customer is satisfied and returns to site. 2. Enterprise meets specifications and remembers them. 1. Customer tells enterprise what he wants.

page 11 Copyrighted material John Tullis The ‘Learning Relationship’ 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 go through the teaching process all over again. It becomes much easier to stay with original firm.

page 12 Copyrighted material John Tullis The ‘Learning Relationship’

page 13 Copyrighted material John Tullis The ‘Learning Relationship’ 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!)

page 14 Copyrighted material John Tullis 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 absorbLooking to interact with companies, not just absorb Expecting rewards for information sharedExpecting rewards for information shared Shopping for ‘information rich’ products, not commoditiesShopping for ‘information rich’ products, not commodities 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:

page 15 Copyrighted material John Tullis How Do We Help Businesses Personalize? Businesses have significant challenges in becoming 1:1 marketers. As e-business architects, we can help by enabling technologies that assist in:  Identifying customers  Collecting information about customers and their needs  Storing and analyzing information collected  Delivering value-added services to customers

page 16 Copyrighted material John Tullis 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

page 17 Copyrighted material John Tullis Personalization Software Techniques Collaborative Filtering - builds a profile of likes and dislikes and look for patterns you share with others, replicating the ‘word of mouth’ experience. (Net.Perceptions is an example.) Case-based Systems - uses statistical modeling to turn a database into a set of cases, which users navigate by answering a series of questions. (PersonalLogic, but Net.Perceptions does some of this also.) Rules-based Filtering - generate databases of user profiles and/or content profiles. Patterns are transformed into assumptions, or rules, which are used to predict future likes and dislikes.

page 18 Copyrighted material John Tullis Personalization Software Techniques Customer profiling -. The (Firefly Passport is an example.) Customer profiling - Combines shopper history, traditional demographics and interest profile. The "We know who you are and what you told us before” model (Firefly Passport is an example.) Parallel track systems - Parallel 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 Agents - Neural Nets & Learning Agents - tracking users' movements around the site and altering what is presented based on their click trails. (Example: Learn Sesame.)

page 19 Copyrighted material John Tullis Personalization Software Collaborative Filtering NetPerceptions WiseWire LikeMinds Rules-based Filtering BroadVision WebSphere Commerce (Blaze) Blue Martini (Blaze) Case-based Filtering Brightware MultiLogic PersonalLogic Business Evolutions Most current software choices fall into 3 of the 6 cases:

page 20 Copyrighted material John Tullis BroadVision - Rules-based Filtering

page 21 Copyrighted material John Tullis BroadVision - Rules-based Filtering 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.

page 22 Copyrighted material John Tullis BroadVision - Rules-based Filtering

page 23 Copyrighted material John Tullis WebSphere Commerce - Rules-based Filtering

page 24 Copyrighted material John Tullis WebSphere Commerce - Rules-based Filtering

page 25 Copyrighted material John Tullis NetPerceptions - Collaborative Filtering

page 26 Copyrighted material John Tullis Blaze - Rules Based Rules & Business Objects: Integrated & Separated

page 27 Copyrighted material John Tullis Brightware - Case-based Advice

page 28 Copyrighted material John Tullis Brightware - Case-based Advice Brightware uses an established set of cases to analyze requests for customer service. Product can use either 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.

page 29 Copyrighted material John Tullis Brightware - Case-based Advice Works through alone, or through Web interaction. Customer answers questions, and engine examines past cases to find resolution. Customer gets personalized answer in real time.