Data Analytics for Customer Facing Applications

(1)

Data Analytics for

Customer Facing Applications

Jaideep Srivastava

(2)

2/25/2008 © Jaideep Srivastava 2

Presentation Outline

Technology trends

Customer facing applications Status of CRM efforts Analytical CRM Customer segmentation Customer loyalty Customer retention Analytical CRM architecture Data warehouse

Dimensional data modeling On-line analytical processing (OLAP)

Data mining

Amazon.com: case study in building customer loyalty Analytics behind

e-marketing

Yodlee.com: case study in web business intelligence Privacy issues

(3)

Technology Trends

Internet growth

Faster than any other infrastructure

Data collection

Rapid drop in storage costs

Dramatic improvement in resolution and rate of data collection ‘probes’

Data analytics

Increasing deployment of warehouses Major leap forward in data mining

technologies and tools

(4)

Infrastructure Adoption in the US

(5)

Product Marketing – 75 years ago

• Production – a la Adam Smith • You can have any color as long as its black –

(6)

Product Marketing - today

5

Add the spice of flexibility, courtesy of robotics,

(7)

New approach to marketing

TO: Finding products that are right for each customer

TURN the process

through 90 degrees

FROM: Finding customers that are right for each product

Products: 1 2 3 4 5 …..

To achieve this we need to align around •Organization and culture

(8)

“Mass Customization” –

B. Joseph Pine

Mass production

Cheap to produce Efficient to produce

Uniform features/quality ‘one size fits all’ approach Optimize production cost

Customization

Expensive to produce Inefficient to produce Customized features ‘tailor made’ approach Optimize customer satisfaction

Mass customization

Cheap & efficient to produce Customized features

‘tailor made’ approach

(9)

(10)

Customer Facing Applications

Consumer marketing

Campaign management Opportunity management

Web-based encyclopedia, configurator Market segmentation

(11)

Customer Facing Applications

Customer care & support

Incident assignment/escalation/tracking/reporting Problem management/resolution

Order management/promise fulfillment Warranty/contract management

Field service support

Work orders, dispatching

(12)

Customer Facing Applications

Corporate sales

Contact management profiles and history Account management including activities Order entry

Proposal generation

Sales management

Pipeline analysis, e.g. forecasting Sales cycle analysis

Territory alignment

(13)

(14)

Companies are spending

mega-budgets on CRM

CRM = software + support services

European CRM expenditure = $1.2B + $3.0B = $4.2B* UK marketing service industry growing at 17.4% to $7.7B CRM Relationship marketing Customer service

Value added programs Loyalty programs Culture change

(15)

(16)

(17)

Increasing customer resistance

98% of customer solicitations are irrelevant

82% of individuals would like to block all

marketing access to their own data

(18)

Consequently

The ‘best’ customers are being over

communicated to

Today’s less valuable customers are not being

developed into tomorrow’s ‘best’ customers

(19)

Solution: Analytical CRM

CRM = Customer Understanding +

Relationship Management

(20)

Example Customer Facing Applications

Helped by Analytical CRM

Customer segmentation

Customer loyalty building

(21)

Customer segmentation

Purpose of segmentation is to identify groups of customers with similar needs and behavior patterns, so that they be offered

more tightly focused Products Services Communications Segments should be Identifiable Quantifiable Addressable

Of sufficient size to be worth addressing Two approaches to segmentation

cluster common characteristics, and then map out behavior patterns

(22)

Customer base segmentation

Potential business High Care & Maintenance Retain Develop Observe & Incentivize Actual business Low High Low

(23)

(24)

Express profits as deciles, and

ask questions

1200 1000 800 600 400 200 0 -200 -400 -600 -800 -1000 -1200

Who are these customers; what do they look like?

Middle 60%, either side of break even. What can we do about these?

Are these worth keeping? Can we service them with a lower cost channel?

What can we do to make this segment profitable?

Should the focus be on retaining wallet share from segments 8 – 10? Or, on gaining from segments 1 – 4?

Profit

(25)

Customer loyalty: close

(26)

Relationship intensity and

defection odds

Evidence suggests that customer ‘lock in’ occurs once 4 or more products are purchased Odds of not defecting 1.1% 10.2% 18.1% 98.3% 1 2 3 4

(27)

A difference of opinion …

70%

90%

Company view _{Customer view}

32% 2%

Customers are happy with our customer service

(28)

… and action

Company view _{Customer view}

98% 43% 7% We want to develop a relationship with our customers

We want to form and develop a relationship with our suppliers

(29)

Increasing propensity to buy over

a customer life cycle

Actions which build relationship warmth

•No-fault service •“Have a nice day” •Targeted sales Customer

(30)

Loyalty is built through a virtuous

circle of new customer experience

Virtuous circle of customer experience

Superlative Customer service Provides legitimacy to offer advice Provides legitimacy to offer advice Innovative new products Individualized and helpful dialog

(31)

Lifetime Impact of Customer

Loyalty

TIME

“Realized” customer value Customer potential

“Maximized” customer value

(32)

Managing Credit-Card Retention in the

Pacific Rim

•Behavioral Propensity Model based Campaigns generate New Customers

•Selective score-based phone follow-up more than doubles response

•“Event-driven”(Trans. Vol. & Value) Campaigns to stimulate initial usage of credit-card.

•Propensity model + “Event-driven” Customer Retention program identifies likely non-renewers 3 months prior to renewal, and kicks in usage stimulation program

•Different offers (“Frequent User Club” versus Premium) being tested

(33)

Using Negative Events to drive

Positive Sales

Event = “ATM request for cash” is rejected due to lack of funds.

For credit-worthy customers, unsecured personal loan is offered by mail or phone

the next day!

30% acceptance rate of product offered.

(34)

2/25/2008 34

(35)

(36)

Traditional Growth of Functions

in an Organization

Inbound Call Centre Branch ATM Fax Kiosk Outbound Call Centre WAP Email 3rd_Party Resellers Data Data Data Data WEB THE PRESENT

MULTIPLE CHANNELS & DATA STORES / IMPERSONAL SERVICE

(37)

DATA

THE NEAR FUTURE

MULTIPLE CHANNELS & DATA STORES / PERSONALISED SERVICE

Impact! Impact! • PERSONALISED • HIGH QUALITY • INFORMED • CONSISTENT

(38)

Canonical Analytics Architecture

(39)

(40)

Data Warehouse

A decision support database that is

maintained separately from the organization’s

operational database

A data warehouse is a

subject-oriented, integrated, time-varying, non-volatile

(41)

Data Warehouse - Subject Oriented

subject oriented: oriented to the major

subject areas of the corporation that have

been defined in the data model.

E.g. for an insurance company: customer, product, transaction or activity, policy, claim, account, and etc.

operational DB and applications may be

organized differently

(42)

Data Warehouse - Integrated

(43)

Data Warehouse - Non-Volatile

Operational data is regularly accessed and

manipulated a record at a time and update is done

to data in the operational environment.

Warehouse Data is loaded and accessed. Update

of data does not occur in the data warehouse

(44)

Data Warehouse - Time Variance

The time horizon for the data warehouse is

significantly longer than that of operational

systems.

Operational database contain current value data.

Data warehouse data is nothing more than a

sophisticated series of snapshots, taken as of

some moment in time.

The key structure of operational data may or may

not contain some element if time. The key

(45)

Data Sources

Data sources are often the operational systems, providing the lowest level of data.

Data sources are designed for operational use, not for decision support, and the data reflect this fact.

Multiple data sources are often from different systems run on a wide range of hardware and much of the software is built in-house or highly customized.

(46)

Data Cleaning

Important to warehouse clean data

(operational data from multiple sources are

often dirty).

Three classes of tools

Data migration: allows simple data transformation Data Scrubbing: uses domain-specific knowledge to scrub data

(47)

Load and Refresh

Loading the warehouse includes some other

processing tasks: checking integrity constraints,

sorting, summarizing, build indxes, etc.

Refreshing a warehouse means propagating

updates on source data to the data stored in the

warehouse

when to refresh

determined by usage, types of data source, etc.

how to refresh

data shipping: using triggers to update snapshot log table and propagate the updated data to the warehouse

(48)

Monitor

detect changes to an information source that

are of interest to the warehouse

define triggers in a full-functionality DBMS examine the updates in the log file

write programs for legacy systems

(49)

Integrator

receive changes from the monitors

make the data conform to the conceptual schema used by the warehouse

integrate the changes into the warehouse

(50)

Metadata Repository

Administrative metadata

source database and their contents gateway descriptions

warehouse schema, view and derived data definitions

dimensions and hierarchies

pre-defined queries and reports data mart locations and contents data partitions

data extraction, cleansing, transformation rules, defaults

data refresh and purge rules user profiles, user groups

(51)

Metadata Repository

Business data

business terms and definitions ownership of data

charging policies

Operational metadata

data lineage: history of migrated data and sequence of transformations applied

(52)

Data Marts

A data mart (departmental data warehouse) is a

specialized system that brings together the data

needed for a department or related applications.

Data marts can be implemented within the data

warehouse by creating special, application-specific

views

.

Data marts can also be implemented as

materialized

views departmental subsets that focus

on selected subjects.

(53)

Other Tools

User interface that allows users to

interact with the warehouse

query and reporting tools

analysis tools

(54)

2/25/2008 54

(55)

Conceptual Modeling of Data

Warehouses

Modeling data warehouses: dimensions &

measurements

Star schema: A single object (fact table) in the middle connected to a number of objects (dimension tables) Snowflake schema: A refinement of star schema where the dimensional hierarchy is represented explicitly by normalizing the dimension tables.

(56)

Example of Star Schema

Date Month Year Date CustId CustName CustCity CustCountry Cust

(57)

Example of Snowflake Schema

Date Month Date CustId CustName CustCity CustCountry Cust

(58)

(59)

Summary Tables

Data warehouse may store some selected

summary data, the pre-aggregated data.

Summary data can store as

separate fact tables

sharing the same dimension tables with the base

fact table.

Summary data can be encoded in the original fact

table and dimension tables.

DateID ProdID Sales 0 1 1000 1 1 20000 1 2 40000

id level date month year

0 1 1 1 1998

1 2 NULL 1 1998

2 2 NULL 2 1998

(60)

Multidimensional Data

Sales volume as a function of product, time, and

geography

Product

Reg ion

month

Dimensions: Product, Region, week Hierarchical summarization paths

Industry Country Year Category Region Quarter Product City Month Week

(61)

A Sample Data Cube

(62)

2/25/2008 62

(63)

Sample Operations

Roll up: summarize data

total sales volume last year by product category

by region

Roll down, drill down, drill through: go from

higher level summary to lower level summary

or detailed data

For a particular product category, find the detailed

sales data for each salesperson by date

Slice and dice: select and project

Sales of beverages in the West over the last 6

months

(64)

Cube Operation

SELECT date, product, customer, SUM (amount)

FROM SALES

CUBE BY date, product, customer

Need compute the following Group-Bys

(

date, product, customer),

(65)

Cuboid Lattice

(B) (A) (C) (D) (B,C) (B,D) (C,D) (A,D) (A,C) (A,B,D) (A,C,D) (B,C,D) (A,B) ( all ) (A,B,C,D) (A,B,C) R

Data cube can

be viewed as a

lattice of

cuboids

The bottom-most cuboid is the base cube. The top most cuboid

(66)

Cube Computation -- Array Based

Algorithm

An MOLAP approach: the base cuboid is stored as a multidimensional array

Read in a number of cells to compute partial cuboids

(67)

ROLAP versus MOLAP

ROLAP

exploits services of relational engine effectively provides additional OLAP services

design tools for DSS schema

performance analysis tool to pick aggregates to materialize

SQL comes in the way of sequential processing and columnar aggregation

(68)

ROLAP versus MOLAP

MOLAP

the storage model is an n-dimensional array

Front-end multidimensional queries map to server capabilities in a straightforward way

Direct addressing abilities

Handling sparse data in array representation is expensive

(69)

(70)

What Is Data Mining?

Data mining (knowledge discovery in databases):

Extraction of interesting ( non-trivial, implicit, previously

unknown and potentially useful) information from data in large databases

Alternative names and their “inside stories”:

Data mining: a misnomer?

Knowledge discovery in databases (KDD: SIGKDD), knowledge extraction, data archeology, data dredging, information

harvesting, business intelligence, etc.

What is not data mining?

(Deductive) query processing.

(71)

Examples of Interesting

Knowledge

Association rules

98% of people who purchase diapers also buy beer

Classification

People with age less than 25 and salary > 40k drive sports cars

Similar time sequences

Stocks of companies A and B perform similarly Outlier Detection

(72)

Motivation: “Necessity is the Mother of

Invention”

Data explosion problem:

Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories.

We are drowning in data, but starving for knowledge! Data warehousing and data mining :

On-line analytical processing

(73)

Data Mining and Business Intelligence

Increasing potential

to support

business decisions End User

Business Analyst Data Analyst DBA Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration OLAP, MDA

Statistical Analysis, Querying and Reporting

Data Warehouses / Data Marts

(74)

Data Mining: Confluence of Multiple

Disciplines

Database systems, data warehouse and OLAP Statistics

Machine learning Visualization

Information science

High performance computing Other disciplines:

(75)

(76)

Data Mining: A KDD Process

Data mining: the core of

knowledge discovery process.

(77)

Steps of a KDD Process

Learning the application domain:

relevant prior knowledge and goals of application

Creating a target data set: data selection

Data cleaning and preprocessing: (may take 60% of effort!) Data reduction and projection:

Find useful features, dimensionality/variable reduction, invariant representation.

Choosing functions of data mining

summarization, classification, regression, association, clustering.

Choosing the mining algorithm(s)

Data mining: search for patterns of interest Interpretation: analysis of results.

visualization, transformation, removing redundant patterns, etc.

(78)

2/25/2008 78

(79)

Three Schemes in Classification

Knowledge to be mined:

Summarization (characterization), comparison,

association, classification, clustering, trend, deviation and pattern analysis, etc.

Mining knowledge at different abstraction levels: primitive level, high level, multiple-level, etc.

Databases to be mined:

Relational, transactional, oriented, object-relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, etc.

Techniques adopted:

(80)

Data Mining: Classification Schemes

General functionality:

Descriptive data mining Predictive data mining

Different views, different classifications:

(81)

Data Mining Functionality

Concept description: Characterization and Comparison:

Generalize, summarize, and possibly contrast data characteristics, e.g., dry vs. wet regions.

Association:

From association, correlation, to causality.

finding rules like “inside(x, city) near(x, highway)”. Classification and Prediction:

Classify data based on the values in a classifying attribute, e.g., classify countries based on climate, or classify cars based on gas mileage.

(82)

Data Mining Functionality

(Cont.)

Clustering:

Group data to form new classes, e.g., cluster houses to find distribution patterns.

Time-series analysis:

Trend and deviation analysis: Find and characterize evolution trend, sequential patterns, similar sequences, and deviation data, e.g., stock analysis.

Similarity-based pattern-directed analysis: Find and characterize user-specified patterns in large databases.

Cyclicity/periodicity analysis: Find segment-wise or total cycles or periodic behaviours in time-related data.

(83)

Data Mining: On What Kind of

Data?

Relational databases Data warehouses

Transactional databases

Advanced DB systems and information repositories

Object-oriented and object-relational databases Spatial databases

Time-series data and temporal data

Text databases and multimedia databases Heterogeneous and legacy databases

(84)

Are All the “Discovered” Patterns

Interesting?

A data mining system/query may generate thousands of patterns, not all of them are interesting.

Suggested approach: Query-based, focused mining

Interestingness measures: A pattern is interesting if it is

easily understood by humans

valid on new or test data with some degree of certainty. potentially useful

novel, or validates some hypothesis that a user seeks to confirm

Objective vs. subjective interestingness measures:

Objective: based on statistics and structures of patterns, e.g., support, confidence, etc.

(85)

Can It Find All and Only Interesting

Patterns?

Find all the interesting patterns: Completeness.

Can a data mining system find all the interesting patterns?

Search for only interesting patterns: Optimization.

Can a data mining system find only the interesting patterns?

Approaches

First general all the patterns and then filter out the uninteresting ones.

(86)

Requirements and Challenges in Data

Mining

Mining methodology issues

Mining different kinds of knowledge in databases.

Interactive mining of knowledge at multiple levels of abstraction.

Incorporation of background knowledge

Data mining query languages and ad-hoc data mining. Expression and visualization of data mining results.

Handling noise and incomplete data

Pattern evaluation: the interestingness problem. Performance issues:

(87)

Requirements/Challenges in Data Mining

(Cont.)

Issues relating to the variety of data types:

Handling relational and complex types of data

Mining information from heterogeneous databases and global information systems.

Issues related to applications and social impacts: Application of discovered knowledge.

Domain-specific data mining tools Intelligent query answering

Process control and decision making.

Integration of the discovered knowledge with existing knowledge: A knowledge fusion problem.

(88)

2/25/2008 88

(89)

The continuing relationship …

Amazon.com “Loyalty” model

Need Creation

Need Creation anticipate/stimulate

Information search

Information search provide /assist

Evaluate alternatives

Evaluate alternatives assist / negate

Purchase transaction

Purchase transaction optimise /reward

Post purchase experience

(90)

(91)

(92)

Information Search

provide /assist

Information search

(93)

Evaluation of Alternatives

assist / negate

Evaluate alternatives

(94)

Purchase Optimisation/Reward

optimise /reward Purchase transaction Purchase transaction •

•11--click purchaseclick purchase •

(95)

Post-purchase experience

add value

Post purchase experience

(96)

(97)

Why is loyalty important

Amazon’s ‘customer lifetime value’ model (for

book buyers

Average $50 for first time purchase Average $40 per visit thereafter

Average of one visit per 2 months

Assume customer will be active for 10 years – not validated yet ☺

(98)

Build more loyalty faster

“Loyalty”LTV

(99)

(100)

Internet Marketing Insight –

Jeff Bezos

Role of

Advertisement – get customer to the store Customer experience – get customer to buy

Brick & mortar stores

Getting customer to store is the hard part

Shopping cart abandonment is not common, since the

overhead of going to another store is very high – especially in Minnesota winters!

Marketing expenses

80% for advertisement; 20% for customer experience

The 80-20 rule is reversed for on-line stores

(101)

Remarks on Amazon.com

A very innovative company – the poster child

for e-commerce

Is pushing the envelope in personalization

Customers love it

Will it make money – we’re all waiting to see

(102)

2/25/2008 102

(103)

Web Logs – Record of consumer

behavior

looney.cs.umn.edu han - [09/Aug/1996:09:53:52 -0500] "GET mobasher/courses/cs5106/cs5106l1.html HTTP/1.0" 200 mega.cs.umn.edu njain - [09/Aug/1996:09:53:52 -0500] "GET / HTTP/1.0" 200 3291

mega.cs.umn.edu njain - [09/Aug/1996:09:53:53 -0500] "GET /images/backgnds/paper.gif HTTP/1.0" 200 3014

mega.cs.umn.edu njain - [09/Aug/1996:09:54:12 -0500] "GET /cgi-bin/Count.cgi?df=CS home.dat\&dd=C\&ft=1 HTTP mega.cs.umn.edu njain - [09/Aug/1996:09:54:18 -0500] "GET advisor HTTP/1.0" 302

mega.cs.umn.edu njain - [09/Aug/1996:09:54:19 -0500] "GET advisor/ HTTP/1.0" 200 487

looney.cs.umn.edu han - [09/Aug/1996:09:54:28 -0500] "GET mobasher/courses/cs5106/cs5106l2.html HTTP/1.0" 200

. . . . . . . . .

Access Log Format

IP address userid time method url protocol status size

mega.cs.umn.edu njain 09/Aug/1996:09:54:31 advisor/csci-faq.html

Other Server Logs: referrer logs, agent logs

(104)

Shopping Pipeline Analysis

Overall goal:

•Maximize probability

of reaching final state

•Maximize expected

sales from each visit

Enter store Browse catalog Select items Complete purchase cross-sell

promotions up-sellpromotions

‘sticky’ states

‘slippery’ state, i.e. 1-click buy

• Shopping pipeline modeled as state transition diagram • Sensitivity analysis of state transition probabilities

• Promotion opportunities identified

(105)

Original Amazon Model for

Customer Segmentation

(106)

Data Driven Customer

Segmentation Model

frequency

tenure monetary

recency

• modeled customers in a 4-dim space • used PCA to determine relative weights

of each dimension

(107)

Customer Score Interpretation

Recency Frequency Monetary Tenure Composite

Score

… … … … …

10 days 4 times $480 3 months 80%

… … … … … 30 days 2 times $900 10 months 72% … … … … … … … … … … Cust M Cust H

• Cust M => frequent visitor but low spender

=> potential for acquiring higher wallet share => focus on improving relationship

(108)

2/25/2008 108

(109)

Current Situation: Consumer

Confusion

“It takes me two hours to

get to all my accounts” “I can’t look at my assets across accounts”

“I can’t remember all my user IDs and passwords”

“I want the web to work for me, not the

other way around”

“This is overwhelming……I

(110)

(111)

Aggregation Service Model

Communication Site

(content partner) FinanceSite TravelSite Capabilities

(112)

Business Intelligence Benefits to

Corporation

‘Tip-of-the-iceberg’ analysis for a

brokerage house

Lifestyle preference analysis of banking

customers for a survey

‘True-wallet-share’ analysis for a credit

card organization

Dynamic targeting for banner

(113)

‘Tip-of-the-Iceberg’ Analysis for a

Brokerage House

Asset Based Tiers Number of Users < $20K 7579 $20K - $100K 2539 $100K - $500K 1994 $500K - $1M 525 $1M - $5M 547 $5M - $25M 106 > $25M 9 • This brokerage house treated customers with net worth > $1M as ‘high net worth’ (HNW) customers with specialized services

(114)

Household Lifestyle Preference

Analysis for a Survey

- 53% have at least one online banking account

- 51% have an online credit card account -- higher than

Yodlee users as a whole - 31% also have an E*Trade account, and 11% also have a

Schwab account

- Have a preference for FirstUSA over Citibank, the opposite

preference for users as a whole - The most popular credit card is American Express

Financial Preferences

25% make travel reservations online --fewer than users as a whole

- Expedia is more popular as an on-line travel site than Travelocity

- 49% have a frequent flier account --higher than users as a whole

-The favorite frequent flier programs are United, Delta, American, in that order

- Half as many of co-brand users shop on Ebay than users as a whole

(115)

‘True-Wallet-Share’ Analysis for a

Credit Card Organization

Analysis of credit card balance habits of user base

• There are1386 people, each of which carries a total balance between $1000 and $2000 on all credit cards that (s)he owns

• 292 of these 1386 people own discover cards, and carry an average balance of $174.55

• 540 of these 1386 people own AmEx cards, with an average balance of $988.97

• 323 of these 1386 people carry one or more Visa, with an average Visa network balance of $1018.50

Range Total Users Discover American Express

(116)

Business Implications of

True Wallet Share Analysis

A credit card offeror knows exactly how much money customers holding its cards spend (every month) on its card vs. that on the competition’s cards

Offeror can target users falling within various segments for specific customer acquisition, retention, etc. purposes

Detailed profile and history information of these users can be used for precision targeting and customer messaging through various channels including ad serving, e-mail campaigns,

promotions, etc.

If transaction level detail information of these users is analyzed, it can be determined exactly which credit cards are being used by aggregation users as a whole for what kind of lifestyle

(117)

Business Implications (contd.)

The analysis above, if carried out at an individual user level detail, can be used to target individual customers with specific

promotions, etc.

Transaction level detail can be classified into charges to specific organizations, department stores, airlines, etc. This will identify the top organizations that aggregation users spend money at, either on the partner’s card or on a competing network. This would be useful in determining which organizations to partner with for customer retention, and acquisition, respectively

All of these analyses if performed periodically, and tracked over time, can provide valuable insight into the evolving credit balance distribution and usage behavior at the user population or

(118)

(119)

(120)

2/25/2008 120

(121)

(122)

(123)

(124)

Problem: Tired of mistreatment

by financial institutions …

You have tons of money in your investment

portfolio

But you are over-worked and slipped a couple

of credit card payment deadlines – after all

you are busy managing your investment

portfolio ☺

(125)

Solution

Why not let the credit card institution know

what your investment portfolio balance is?

Impress them ☺

Perhaps even authorize credit card company

to transfer funds from your investment

(126)

So, what’s the catch…

Shopping example

Allow the vendor to collect detailed information about you and build an accurate profile

Junk mail is only a nuisance for the receiver, but an expense for the sender! – the sender wants to avoid it more than the receiver!!

Credit card example

Allow the credit card company and investment company to share your information

Multiple online accounts example

Hand over your account names and passwords to aggregation service

(127)

let’s now talk about privacy …

Merriam Webster definition

a: the quality or state of being apart from

company or observation b : freedom from unauthorized intrusion

Justice Oliver Wendell Holmes

“the right to be left alone”

Operational definition

(128)

Public Attitude Towards Privacy

A (self-professed) non scientific study carried out by a USA Today reporter

Asked 10 people the following two questions

Are you concerned about privacy? 8 said YES

If I buy you a Big Mac, can I keep the wrapper (to get fingerprints)? 8 said YES

ACM E-Commerce 2001 paper [Spiekermann et al] Most people willing to answer fairly personal questions to

anthropomorphic web-bot, even though not relevant to the task at hand

Different privacy policies had no impact on behavior

(129)

Public Attitude (contd.)

Amazon.com (and practically

every commercial site) uses cookies to identify and track visitors

97.6% of Amazon.com

customers accepted cookies

Airline frequent flier programs with cross promotions

We willingly agree to be tracked

Get upset if the tracking fails!

Over 1.5 million people have trusted the aggregation

service (called Yodlee) with the names and passwords of their financial accounts in less than 18 months

Adoption rate has been over 3 times the most optimistic projections

(130)

What people really want

Some people will not share any kind of

private data at any cost – the ‘paranoids’

Some people will share any data for returns –

the ‘Jerry Springerites’

The vast majority in the middle wants

a reasonable level of comfort that private data about them will NOT be misused

(131)

Remarks on Privacy

Is it ‘much ado about nothing’?

If indeed data collection was outlawed, and thus

personalization impossible, wouldn’t the public lose – faced with generic, undifferentiated products/services?

Given the public’s attitude about privacy (as shown in their actions), are privacy advocates barking up the wrong tree? Is it just a matter of time or generational issue, e.g.

adoption of credit cards Where do we stand?

Current position - loss of your privacy may be beneficial for you

Emerging position (post September 11th _{) - loss of your} privacy will be beneficial for everyone

Critical emerging debate - is privacy a right or a

(132)

Concluding Remarks

Internet is a high bandwidth, low latency, negligible cost, interactive channel to the customer

Very high adoption rates for this channel

Processing speeds and storage capacities continuing to increase while costs continue to fall

Data analytics technology has grown rapidly Customer facing applications are ready for a paradigm shift

Innovative companies have moved ahead