Transcript Mass Personalization

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
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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:
DATA COLLECTED
ONLY IF VISITOR
BUYS SOMETHING
– wants to buy, maybe not here
• Add, marketing target:
– in store because of ad or promotion
• Customer:
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–
–
–
buys something
pays cash
uses a credit card
uses a store charge card
20-751 ECOMMERCE TECHNOLOGY
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IDENTITY UNKNOWN
PRODUCT/TIME KNOWN
IDENTITY KNOWN
IDENTITY, JOB, INCOME KNOWN
COPYRIGHT © 2003 MICHAEL I. SHAMOS
Store Visitors in Cyberspace
• Casual site visitor:
– no intention of buying
• Prospecting site visitor:
CAN EASILY DETECT
THE DIFFERENCE
– wants to buy, maybe not here
• Add, marketing target:
– in store because of ad or promotion
WE KNOW HOW HE
GOT HERE AND WHAT
HE WANTS TO BUY
• Customer:
–
–
–
–
buys something
pays cash
uses a credit card
uses a store charge card
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WE HAVE HIS WHOLE FILE
WE KNOW WHAT OTHER PEOPLE
LIKE HIM ARE BUYING
COPYRIGHT © 2003 MICHAEL I. SHAMOS
Click Behavior
CASUAL VISITOR
STORE
HOME PAGE
OFFICE
PRODUCTS
HOUSEWARES
PRESENTATION
ITEMS
LASER
POINTERS
LASER
1
LASER
2
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KITCHEN
TOASTERS
LASER
3
SUMMER 2003
SPORTING
GOODS
HUNTING
RIFLES
GOLF
CLUBS
CALLAWAY
COPYRIGHT © 2003 MICHAEL I. SHAMOS
Click Behavior
PROSPECTING VISITOR
STORE
HOME PAGE
OFFICE
PRODUCTS
HOUSEWARES
PRESENTATION
ITEMS
LASER
POINTERS
LASER
1
LASER
2
20-751 ECOMMERCE TECHNOLOGY
KITCHEN
TOASTERS
LASER
3
SUMMER 2003
SPORTING
GOODS
HUNTING
RIFLES
GOLF
CLUBS
CALLAWAY
COPYRIGHT © 2003 MICHAEL I. SHAMOS
What is Personalization?
• 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.)
20-751 ECOMMERCE TECHNOLOGY
SUMMER 2003
COPYRIGHT © 2003 MICHAEL I. SHAMOS
Adeptra Response Solutions
SOURCE: ADEPTRA
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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
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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
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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
Merchant Cookie
Client
DoubleClick
Cookie
1. Client requests a page
2. Server sends a page with
a DoubleClick URL
Merchant
Server
e.g. Altavista
3. Text is displayed
4. Client requests the DoubleClick page
Web Page
5. DoubleClick
reads its cookie
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.
20-751 ECOMMERCE TECHNOLOGY
DoubleClick
Server
6. DoubleClick decides
which ads to send
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
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SUMMER 2003
COPYRIGHT © 2003 MICHAEL I. SHAMOS
Real-Time CRM
Web Servers
Navigational
Data
Request
Operational
Database
Recommend
Recommend™
Front-end Server
Real Time
Recorder
Recommend™
Back-end Server
Matching
Agent
Recorder
Predictor
Cache
Database
Real Time
Predictor
Analyzer
Synchronization
Personalization
Database
SOURCE: PIONSOFT
20-751 ECOMMERCE TECHNOLOGY
SUMMER 2003
COPYRIGHT © 2003 MICHAEL I. SHAMOS
Prime Personalization Candidates
Companies with:
• Many products/services
• Complex products/services
• Many customers
• Competitive environment
Industries:
• Newspapers/Magazines/Research
• Catalogs/Retail
• High Tech
• Financial Services
20-751 ECOMMERCE TECHNOLOGY
SUMMER 2003
COPYRIGHT © 2003 MICHAEL I. SHAMOS
Personalization Roadblocks
SOURCE: FORRESTER RESEARCH (12/98)
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SUMMER 2003
COPYRIGHT © 2003 MICHAEL I. SHAMOS
Portals
• Universal entry points for corporate information
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Employees
Customers
Potential employees
Press
Investors
• Must allow some personalization
– Too much information
– CMU portal:
20-751 ECOMMERCE TECHNOLOGY
SUMMER 2003
COPYRIGHT © 2003 MICHAEL I. SHAMOS
Enterprise Portals - “Context is King”
Characteristics
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Focused membership
targeting projects, teams,
and “communities”
Hub for interactions (both
structured & unstructured)
Includes unique &
“guided” content &
content/app linking and/or
integration
Infomaster
Guides Access
Fubar Corp. New products
Re: Fubar Corp New
products
Not a big deal in my client
Memos
base
Seeing interest out west.
Help!
Help from engineering
Plans
Thanks. How about …
Try the attached slides
Marketing will prepare a
paper
Customer Satisfaction survey
Looking for more responses
Used to capture & access
knowledge
Rich BCM services behind
the portal with varying
degrees of integration
Community
Groupware Apps
People
Project
X
Externa
l
Status
Real-Time Info. Feed
You have a meeting in
...
Search In:
Search For:

Discussion Database
Real-Time Chat
& Net Meetings
All
Sources
Bixbie Intl.
Who’s Online?
Matt Cain
David Cearley
Mike Gotta
Steve Kleynhans
Dale Kutnick
Optio
ns
Sear
ch
Search & Retrieval
Buddy
List
Interest Group Sites
(Internet, Extranet, Intranet)
Document Sharing
BI Report Viewer
Knowledge Mgmt.
Related Links
(Sites & Apps)
SOURCE: META GROUP
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 requests
remotehost 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
DATE
REFERRING QUERY
REQUESTING IP ADDRESS
REQUESTING DOMAIN
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
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
Analysis
Hits
Number of Successful Hits for Entire Site
Average Number of Hits Per Day
184,558
15,379
Number of Hits for Home Page
Page Views
2,248
Number of Page Views (Impressions)
Average Number of Page Views Per Day
Visitor Sessions
46,438
3,952
Document Views
43,829
Number of User Sessions
13,564
Average Number of User Sessions Per Day
Average User Session Length
International User Sessions
1,130
00:03:09
26.13%
User Sessions of Unknown Origin
31.01%
User Sessions from United States
Visitors
SOURCE: WEBTRENDS
42.81%
Number of Unique Visitors
11,685
Number of Visitors Who Visited Once
10,720
Number of Visitors Who Visited More Than Once
20-751 ECOMMERCE TECHNOLOGY
SUMMER 2003
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COPYRIGHT © 2003 MICHAEL I. SHAMOS
Key Takeaways
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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
Q&A
20-751 ECOMMERCE TECHNOLOGY
SUMMER 2003
COPYRIGHT © 2003 MICHAEL I. SHAMOS