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

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Mass Personalization

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Mass personalization

Mass Personalization


Outline

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


Need for personalization

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)


Store visitors in the real world

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 COLLECTED

ONLY IF VISITOR

BUYS SOMETHING

IDENTITY UNKNOWN

PRODUCT/TIME KNOWN

IDENTITY KNOWN

IDENTITY, JOB, INCOME KNOWN


Store visitors in cyberspace

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 DETECT

THE DIFFERENCE

WE KNOW HOW HE

GOT HERE AND WHAT

HE WANTS TO BUY

WE HAVE HIS WHOLE FILE

WE KNOW WHAT OTHER PEOPLE

LIKE HIM ARE BUYING


Click behavior

Click Behavior

CASUAL VISITOR

STORE

HOME PAGE

OFFICEPRODUCTS

SPORTING

GOODS

HOUSEWARES

PRESENTATION

ITEMS

KITCHEN

HUNTING

GOLF

LASER

POINTERS

TOASTERS

RIFLES

CLUBS

LASER 1

LASER 2

LASER 3

CALLAWAY


Click behavior1

Click Behavior

PROSPECTING VISITOR

STORE

HOME PAGE

OFFICEPRODUCTS

SPORTING

GOODS

HOUSEWARES

PRESENTATION

ITEMS

KITCHEN

HUNTING

GOLF

LASER

POINTERS

TOASTERS

RIFLES

CLUBS

LASER 1

LASER 2

LASER 3

CALLAWAY


What is personalization

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.)


Adeptra response solutions

Adeptra Response Solutions

SOURCE: ADEPTRA


The secret know the user

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


Know your customer

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


Mass personalization

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


Mass personalization

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


Cookies

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


How doubleclick works

How DoubleClick Works

Merchant Cookie

Client

1. Client requests a page

Merchant

Server

e.g. Altavista

DoubleClick

Cookie

2. Server sends a page with

a DoubleClick URL

3. Text is displayed

4. Client requests the DoubleClick page

Web Page

5. DoubleClick

reads its cookie

DoubleClick

Server

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.

6. DoubleClick decides

which ads to send


Filtering techniques

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


Active decisions 7

Active Decisions 7

SOURCE: ACTIVE DECISIONS


Mass personalization

Web Servers

Navigational

Data

Recommend

Request

Recommend™

Front-end Server

Recommend™

Back-end Server

Real Time

Recorder

Matching

Agent

Recorder

Predictor

Real Time

Predictor

Analyzer

Synchronization

Personalization

Database

Operational

Database

Cache

Database

Real-Time CRM

SOURCE: PIONSOFT


Prime personalization candidates

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


Personalization roadblocks

Personalization Roadblocks

SOURCE: FORRESTER RESEARCH (12/98)


Portals

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

Infomaster

Guides Access

Discussion Database

Community

Groupware Apps

Fubar Corp. New products

Re: Fubar Corp New products

Not a big deal in my client base

Seeing interest out west. Help!

Help from engineering

Thanks. How about …

Try the attached slides

Marketing will prepare a paper

Customer Satisfaction survey

Looking for more responses

Real-Time Info. Feed

Memos

People

Project X

External

Plans

Status

You have a meeting in ...

Real-Time Chat

& Net Meetings

Search In:

Search For:

All Sources

Bixbie Intl.

Who’s Online?

Matt Cain

David Cearley

Mike Gotta

Steve Kleynhans

Dale Kutnick

Options

Search

Search & Retrieval

Buddy

List

Document Sharing

BI Report Viewer

Knowledge Mgmt.

Related Links

(Sites & Apps)

Interest Group Sites

(Internet, Extranet, Intranet)

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

SOURCE: META GROUP


Anonymizers

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


Server log analysis

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

    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


Analysis

Analysis

SOURCE: WEBTRENDS CORP.


Analysis1

Analysis

HitsNumber of Successful Hits for Entire Site184,558

Average Number of Hits Per Day15,379

Number of Hits for Home Page2,248

Page ViewsNumber of Page Views (Impressions)46,438

Average Number of Page Views Per Day 3,952

Document Views43,829

Visitor SessionsNumber of User Sessions13,564

Average Number of User Sessions Per Day1,130

Average User Session Length00:03:09

International User Sessions26.13%

User Sessions of Unknown Origin31.01%

User Sessions from United States42.81%

VisitorsNumber of Unique Visitors11,685

Number of Visitors Who Visited Once10,720

Number of Visitors Who Visited More Than Once 959

SOURCE: WEBTRENDS


Key takeaways

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


Mass personalization

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