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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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20-751 ECOMMERCE TECHNOLOGY
SUMMER 2003
COPYRIGHT © 2003 MICHAEL I. SHAMOS
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
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)
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
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
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
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
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?
20-751 ECOMMERCE TECHNOLOGY
SUMMER 2003
COPYRIGHT © 2003 MICHAEL I. SHAMOS
Adeptra Response Solutions
SOURCE: ADEPTRA
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
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
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
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
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
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
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
20-751 ECOMMERCE TECHNOLOGY
SUMMER 2003
COPYRIGHT © 2003 MICHAEL I. SHAMOS
Active Decisions 7
SOURCE: ACTIVE DECISIONS
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
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
20-751 ECOMMERCE TECHNOLOGY
SUMMER 2003
COPYRIGHT © 2003 MICHAEL I. SHAMOS
Personalization Roadblocks
SOURCE: FORRESTER RESEARCH (12/98)
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:
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
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
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
20-751 ECOMMERCE TECHNOLOGY
SUMMER 2003
COPYRIGHT © 2003 MICHAEL I. SHAMOS
Analysis
SOURCE: WEBTRENDS CORP.
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
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
20-751 ECOMMERCE TECHNOLOGY
SUMMER 2003
COPYRIGHT © 2003 MICHAEL I. SHAMOS
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