mass personalization

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20-751 ECOMMERCE TECHNOLOGY SUMMER 2003 COPYRIGHT © 2003 MICHAEL I. SHAMOS Mass Personalization

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Mass Personalization. Outline. What is personalization? Personalization is based on data Acquiring data about people From people themselves From their clickstream From outside data sources Using the data in the relationship (CRM) Improve the customer’s experience Help the company - PowerPoint PPT Presentation

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Page 1: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Mass Personalization

Page 2: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Outline

• What is personalization?• Personalization is based on data• Acquiring data about people

– From people themselves– From their clickstream– From outside data sources

• Using the data in the relationship (CRM)– Improve the customer’s experience– Help the company

• Data mining

Page 3: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Need For Personalization

• In the real-world– Customer relationship is mediated by people– Personalization is critical: PEOPLE are PEOPLE

• On the Web– Too many customers; too few employees– Orders are entered by machine; follow-up is by machine– Customer relationship is mediated by machines– Personalization is critical

• Uniqueness (everyone is different)• Efficiency (everyone has limited time)

Page 4: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Store Visitors in the Real World

• Casual store visitor:– no intention of buying

• Prospecting store visitor:– wants to buy, maybe not here

• Add, marketing target:– in store because of ad or promotion

• Customer:– buys something– pays cash– uses a credit card– uses a store charge card

DATA COLLECTEDONLY IF VISITORBUYS SOMETHING

IDENTITY KNOWN

IDENTITY UNKNOWN PRODUCT/TIME KNOWN

IDENTITY, JOB, INCOME KNOWN

Page 5: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Store Visitors in Cyberspace

• Casual site visitor:– no intention of buying

• Prospecting site visitor:– wants to buy, maybe not here

• Add, marketing target:– in store because of ad or promotion

• Customer:– buys something– pays cash– uses a credit card– uses a store charge card

CAN EASILY DETECTTHE DIFFERENCE

WE KNOW HOW HEGOT HERE AND WHATHE WANTS TO BUY

WE HAVE HIS WHOLE FILE

WE KNOW WHAT OTHER PEOPLELIKE HIM ARE BUYING

Page 6: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Click Behavior

STOREHOME PAGE

OFFICEPRODUCTS

PRESENTATIONITEMS

LASERPOINTERS

LASER 1

LASER 2

LASER 3

HOUSEWARESSPORTING

GOODS

HUNTING GOLF

CLUBS

CALLAWAY

RIFLES

KITCHEN

TOASTERS

CASUAL VISITOR

Page 7: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Click Behavior

STOREHOME PAGE

OFFICEPRODUCTS

PRESENTATIONITEMS

LASERPOINTERS

LASER 1

LASER 2

LASER 3

HOUSEWARESSPORTING

GOODS

HUNTING GOLF

CLUBS

CALLAWAY

RIFLES

KITCHEN

TOASTERS

PROSPECTING VISITOR

Page 8: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

• Addressing customers by name and remembering their preferences

• Showing customers specific content based on who they are and their past behavior

• Empowering the customer. Examples: Land’s End, llbean• Product tailoring. Example: dell.com

• Connecting to a human being when necessary. We Call You, Adeptra

•Allowing visitors to customize a site for their specific purposes

• Users are 20%-25% more likely to return to a site that they tailored (Jupiter Communications, Inc.)

What is Personalization?

Page 9: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Adeptra Response Solutions

SOURCE: ADEPTRA

Page 10: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

The Secret: Know the User

• IP address, e.g. 192.151.11.40. Look it up.

– Anonymous, but I might know your employer

• Domain name, e.g. hp.com

– I probably know your employer

• Name, address, phone no.

– A good start

• Social security number

– I know everything

Page 11: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Know Your Customer

• Insider trades (search AMZN)• Inmate release (search Jones with photos)• Marriage records (look up Snelling in Berks Co.)• Land records (look up “shamos”)• Home sale prices (search zip 10471, $2.2-$5 million, 1997-2001)• Name by address (look up 5026 Arlington Bronx)• Phone number by name (Bram, Jonathan, Bronx, NY)• Census data (look up 5026 Arlington 10463)• Altavista (search “jonathan bram”, “susan bram”)• Death index• Index of over 16,500 public databases

Page 12: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Customer Profiling

Geographic (How are customers distributed?)

Cultural and Ethnic (What languages do customers prefer? Does ethnicity affect their tastes or buying behavior?)

Economic conditions, income and/or purchasing power (What is the purchasing power of your customer?

Power (What is title and the decision-making power of the customer?)

Size of company (How big is the customer?)

Age (How old is the customer? Family? Children?)

SOURCE: K. GARVIE BROWN

Page 13: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Customer Profiling

Values, attitudes, beliefs (Predominant values your customers have in common; their attitude toward your kind of product

Knowledge and awareness (How much do customers know about your product or service, about your industry?)

Lifestyle (How many lifestyle characteristics can you name about your purchasers? UpMyStreet)

Buying patterns (How consumers of different ages and demographic groups shop on the Web.)

Media Used (How do your targeted customers learn? What do they read? What magazines do they subscribe to? What are their favorite websites ...?)

SOURCE: K. GARVIE BROWN

Page 14: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Cookies• Post-it notes for the web (typically 4KB)• Small files maintained on user’s hard disk, readable

only by the site that created them (up to 20 per site)• Used for

– website tracking, online ordering, targeted adverts

• Can be disabled• To learn about cookies, see Cookie Central• Internet Explorer keeps cookies in \windows\Cookies

• Netscape keeps them in cookies.txt in the Netscape directory

Page 15: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

How DoubleClick Works

DoubleClickServer

MerchantServer

e.g. Altavista

Client

Web Page

1. Client requests a page

2. Server sends a page witha DoubleClick URL

4. Client requests the DoubleClick page

3. Text is displayed

5. DoubleClick reads its cookie

6. DoubleClick decides which ads to send

If you choose to give u personal information via the Internet that we or our business partners may need -- to correspond with you, process an order or provide you with a subscription, for example -- it is our intent to let you know how we will use such information. If you tell us that you do not wish to have this information used as a basis for further contact with you, we will respect your wishes. We do keep track of the domains from which people visit us. We analyze this data for trends and statistics, and then we discard it.

Merchant Cookie

DoubleClickCookie

Page 16: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Filtering Techniques

• Rule-based filtering– Ask user questions to elicit preferences, adaptive sequencing

– Phone Wizard (uses Active Product Spex from ActiveDecisions)

– Credit card finder

• Learning agents (nonintrusive personalization)– implicit profiling

– webgroove.com

• Collaborative filtering– base decisions on preferences of like-minded users

– movielens

– amazon.com

Page 17: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Active Decisions 7

SOURCE: ACTIVE DECISIONS

Page 18: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Recommend™Back-end Server

MatchingAgent

MatchingAgent

PersonalizationDatabase

PersonalizationDatabase

Real TimeRecorderReal TimeRecorder

AnalyzerAnalyzer

Web Servers

RecorderRecorder

NavigationalData

Request Recommend

Synchronization

Recommend™ Front-end Server

OperationalDatabase

OperationalDatabase

Real TimePredictorReal TimePredictor

PredictorPredictor

CacheDatabase

CacheDatabase

Real-Time CRM

SOURCE: PIONSOFT

Page 19: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Companies with:• Many products/services

• Complex products/services

• Many customers

• Competitive environment

Industries:• Newspapers/Magazines/Research

• Catalogs/Retail

• High Tech

• Financial Services

Prime Personalization Candidates

Page 20: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Personalization Roadblocks

SOURCE: FORRESTER RESEARCH (12/98)

Page 21: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Portals

• Universal entry points for corporate information– Employees– Customers– Potential employees– Press– Investors

• Must allow some personalization– Too much information– CMU portal:

Page 22: Mass Personalization

Enterprise Portals - “Context is King”Characteristics Focused membership

targeting projects, teams, and “communities”

Hub for interactions (both structured & unstructured)

Includes unique & “guided” content & content/app linking and/or integration

Used to capture & access knowledge

Rich BCM services behind the portal with varying degrees of integration

MemosMemos

PlansPlans

ExternaExternall

PeoplePeople

StatusStatus

Project Project XX

You have a meeting in You have a meeting in ......

Interest Group Sites(Internet, Extranet, Intranet)

Real-Time Chat& Net Meetings

Document SharingBI Report Viewer Knowledge Mgmt.

CommunityGroupware Apps

Real-Time Info. Feed

Discussion Database

Related Links(Sites & Apps)

Search In:Search In:

Search For:Search For:

Fubar Corp. New productsRe: Fubar Corp New

productsNot a big deal in my client

baseSeeing interest out west.

Help!Help from engineering

Thanks. How about …Try the attached slides

Marketing will prepare a paperCustomer Satisfaction survey

Looking for more responses

All Sources

Bixbie Intl.

Search

Options

Search & Retrieval

InfomasterGuides Access

BuddyList

Who’s Online?Who’s Online?

Matt CainMatt Cain

David CearleyDavid Cearley

Mike GottaMike Gotta

Steve KleynhansSteve Kleynhans

Dale KutnickDale Kutnick

SOURCE: META GROUP

Page 23: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Anonymizers• Server that “launders” IP addresses to allow

anonymous browsing – List of Web anonymizers– The Cloak– JAP

• Issues– Blocking by administrators– Subpoenas

• Anonymous email• Escrow agents

– anonymous purchases and payments

Page 24: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Server Log Analysis

• Servers maintain logs of all resource requestsremotehost name authuser [date] "request" status bytes

gateway.iso.com - - [10/MAY/1999:00:10:30] "GET /class.html HTTP/1.1" 200 10000

• Referrer logs

08/02/99, 12:02:35,

http://ink.yahoo.com/bin/query?p="sample+log+file"&b=21&hc=0&hs=0,

130.132.232.48, biomed.med.yale.edu

• Analog

DATE REFERRING QUERY REQUESTING IP ADDRESS

REQUESTING DOMAIN

Page 25: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Analysis

SOURCE: WEBTRENDS CORP.

Page 26: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Hits Number of Successful Hits for Entire Site 184,558

Average Number of Hits Per Day 15,379

Number of Hits for Home Page 2,248

Page Views Number of Page Views (Impressions) 46,438

Average Number of Page Views Per Day 3,952

Document Views 43,829

Visitor Sessions Number of User Sessions 13,564

Average Number of User Sessions Per Day 1,130

Average User Session Length 00:03:09

International User Sessions 26.13%

User Sessions of Unknown Origin 31.01%

User Sessions from United States 42.81%

Visitors Number of Unique Visitors 11,685

Number of Visitors Who Visited Once 10,720

Number of Visitors Who Visited More Than Once 959

Analysis

SOURCE: WEBTRENDS

Page 27: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

Key Takeaways

• People want to be treated as individuals• There’s nothing wrong with entertaining the user• Everyone has a frustration limit• We can learn who a user is and what he wants to buy• Use data to alter the web experience in real-time• Users have high privacy sensitivity

Page 28: Mass Personalization

20-751 ECOMMERCE TECHNOLOGY

SUMMER 2003

COPYRIGHT © 2003 MICHAEL I. SHAMOS

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