Data Analytics for Customer Facing Applications - MISRC | Home

GeneralJaideep Srivastava Computer Science & Engineering
[email protected]
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 Conclusion
2/25/2008 © Jaideep Srivastava 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
Becoming possible to really understand what your customers want – even at the individual level!!
Millions of users
Radio TV Cable Internet
Product Marketing – 75 years ago
• Production – a la Adam Smith • You can have any color as long as its black – Ford Motor Co.
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 •Business processes and skill •Measurement and incentives •Information management •Technology
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 Optimize production cost & customer satisfaction
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Customer care & support Incident assignment/escalation/tracking/reporting Problem management/resolution Order management/promise fulfillment Warranty/contract management
Field service support Work orders, dispatching Real time information transfer to field personnel via mobile technologies
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 Roll-up and drill-down reporting
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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
*Hewson Consulting October 2000
But - satisfaction is declining
Increasing customer resistance
98% of customer solicitations are irrelevant 82% of individuals would like to block all marketing access to their own data Campaign hit rates and customer loyalty indicators are declining
Consequently
The ‘best’ customers are being over communicated to Today’s less valuable customers are not being developed into tomorrow’s ‘best’ customers The business potential of the customer base is not being maximized
Analytics = OLAP, Statistical analysis, data mining, etc.
Customer segmentation
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 Separate out behavior patterns, then identify segment characteristics
High
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
Deciles
Relationship intensity and defection odds
Evidence suggests that customer ‘lock in’ occurs once 4 or more products are purchased
Odds of not defecting
A difference of opinion …
32%
2%
Customers are happy with our customer service
We research customer service needs and wants as part of our customer service improvement
Customer service needs no improvement
Customer service today is better than ever
43%
7%
We want to develop a relationship with our customers
We want to form and develop a relationship with our suppliers
The relationship now is stronger than 12 months ago
Actions which build relationship warmth
•No-fault service •“Have a nice day” •Targeted sales
Customer relationship profitability
Loyalty is built through a virtuous circle of new customer experience
Virtuous circle of customer experience
Superlative Customer serviceProvides legitimacy
Innovative new products
TIME
•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
Impact: Over 100% improvement in both Acquisition and Retention. New market opened up.
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.
Impact: Significant cross-sales of additional product
Significant reduction in negative reactions
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Inbound Call Centre
• IMPERSONAL
Impact!Impact!
• PERSONALISED
Monitor &
Integrator
Data Sources Tools
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
collection of data that is used primarily in organizational decision making.
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
E.g. based on type of insurance's: auto, life, medical, fire, ...
Data Warehouse - Integrated
There is no consistency in encoding, naming conventions, … among different data sources When data is moved to the warehouse, it is converted.
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 environment.
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 structure of the data warehouse always contains some element of time.
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. Multiple data sources introduce a large number of issues - - semantic conflicts.
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 Data auditing: discovers rules and relationships by scanning data (detect outliers).
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 transaction shipping: shipping the updates in the transaction log
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
propagate the change in a generic form to the integrator
Integrator receive changes from the monitors
make the data conform to the conceptual schema used by the warehouse
integrate the changes into the warehouse merge the data with existing data already present resolve possible update anomalies
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 security: user authorization, access control
Metadata Repository Business data
Operational metadata data lineage: history of migrated data and sequence of transformations applied currency of data: active, archived, purged Monitoring information: warehouse usage statistics, error reports, audit trails
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. Data marts may use different data representations and include their own OLAP engines
Other Tools User interface that allows users to interact with the warehouse
query and reporting tools analysis tools data mining tools
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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.
tables.
Product
Store
Product
AIR-EXPRESS
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 3 1 300000
id level date month year 0 1 1 1 1998 1 2 NULL 1 1998 2 2 NULL 2 1998 3 3 NULL NULL 1998
Sales volume as a function of product, time, and geography
Pr od
uc t
Reg ion
Industry Country Year
Category Region Quarter
A Sample Data Cube Total annual sales of TV in China.Date
Pro du
India
Japan
sum
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
Pivot: reorient cube
FROM SALES
(date, product, customer), (date,product),(date, customer), (product, customer), (date), (product) (customer)
RData cube can be viewed as a lattice of cuboids
The bottom- most cuboid is the base cube.
The top most cuboid contains only one cell.
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
B {ABC} {AB} {AC} {BC}
{A} {B} {C} { }
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 Some queries are hard to formulate and can often be time consuming to execute
ROLAP versus MOLAP MOLAP
Front-end multidimensional queries map to server capabilities in a straightforward way
Direct addressing abilities
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Data Mining
© Jaideep Srivastava
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. Expert systems or small ML/statistical programs
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 Residential customers for telecom company with businesses at home
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
Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases.
Data Mining and Business Intelligence Increasing potential to support business decisions End User
Business Analyst
Data Analyst
Data Warehouses / Data Marts
Statistics
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Data mining: the core of knowledge discovery process.
Data Cleaning
Data Integration
Data Warehouse
Task-relevant Data
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. Use of discovered knowledge.:
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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, object-oriented, object- relational, active, spatial, time-series, text, multi- media, heterogeneous, legacy, etc.
Techniques adopted: Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, neural network, etc.
Predictive data mining
Different views, different classifications: Kinds of knowledge to be discovered,
Kinds of databases to be mined, and
Kinds of techniques adopted.
Generalize, summarize, and possibly contrast data characteristics, e.g., dry vs. wet regions.
Association:
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.
Predict some unknown or missing attribute values based on other information.
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.
Other pattern-directed or statistical analysis:
Relational databases
Data warehouses
Transactional databases
Spatial databases
Heterogeneous and legacy databases
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. Subjective: based on user’s beliefs in the data, e.g., unexpectedness, novelty, etc.
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. Generate only the interesting patterns --- mining query optimization
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: Efficiency and scalability of data mining algorithms. Parallel, distributed and incremental mining methods.
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. Protection of data security and integrity.
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Need CreationNeed Creation anticipate/stimulate
Post purchase experiencePost purchase experience add value
anticipate/stimulateNeed CreationNeed Creation
••11--click purchaseclick purchase ••‘‘slippery check out counterslippery check out counter’’ vs. vs. ‘‘sticky aislessticky aisles’’
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
‘4 buys and you are hooked’ empirical law Use Alexa data to bring back ‘prodigal sons’ (and daughters)
Time
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 – Jeff Bezos
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
A company of the future, with a product of the past, in a market of the present
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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 Application server logs: business event logging
of reaching final state •Maximize expected
sales from each visit Enter store
Browse catalog
Select items
Complete purchase
cross-sell promotions
up-sell promotions
‘sticky’ states
• Shopping pipeline modeled as state transition diagram • Sensitivity analysis of state transition probabilities • Promotion opportunities identified • E-metrics and ROI used to measure effectiveness
dollars spent in past quarter
1 2 3 4 65
1500
1000
500
7
H M
frequency
tenure
monetary recency
• modeled customers in a 4-dim space • used PCA to determine relative weights
of each dimension • Composite Score = w1*recency + w2*frequency +
w3*monetary + w4*tenure
Score
… … … … …
72%
Cust M
Cust H
• Cust M => frequent visitor but low spender => potential for acquiring higher wallet share => focus on improving relationship
• Cust H => infrequent visitor but heavy spender => focus on sustaining relationship
2/25/2008 108
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 need some help”
“Make it easier for me!”
Solution – Personal Information Aggregation
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 advertisements, e-mail campaigns, etc.
Asset Based Tiers
Number of Users
> $25M 9
• This brokerage house treated customers with net worth > $1M as ‘high net worth’ (HNW) customers with specialized services
• Almost none of the customers in the green region had > $1M with this brokerage
- 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
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 online 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
Lifestyle Preferences
‘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
Mastercard Visa Other Average Total< $100 462 4.13
(73)
$100 - $200 232 -12.61 (39) 120.17 (66) 0 89.95
(40)
167.10 (156) 149.44
$200 - $500 643 36.97 (107) 253.77 (207) 0 218.93 (135) 272.42 (421) 342.99 $500 - $1000 968 75.57 (182) 571.09 (378) 0 597.83 (217) 623.36 (593) 893.47
$1000 - $2000
(1)
957.69
(1)
3648.40
(3)
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 activity, e.g. travel, entertainment, shopping, groceries, etc; this can help partner decide which market segments to focus on
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 individual user level
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 Credit card institution treats you like a deadbeat
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 account to cover the payment? Or maybe not
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 Sounds scary – but over 1.5 million people have done this in about 18 months’ time!!
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 Collection and analysis of personal data beyond some limit
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 Study carried out in Europe, where privacy consciousness is (presumably) higher
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
Medical data is (perhaps) an exception to this
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 Tangible and compelling benefits in return for sharing their private data – Big Mac example, frequent flier programs
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 privilege?
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 Privacy is an issue, but not much of a concern
Data Analytics forCustomer Facing Applications
Presentation Outline
Technology Trends
New approach to marketing
Customer Facing Applications
Companies are spending mega-budgets on CRM
But - satisfaction is declining
Increasing customer resistance
Customer segmentation
Relationship intensity anddefection odds
A difference of opinion …
Increasing propensity to buy over a customer life cycle
Loyalty is built through a virtuous circle of new customer experience
Lifetime Impact of Customer Loyalty
Analytical CRM Architecture
Analytical CRM Loop
Example of Star Schema
Example of Snowflake Schema
ROLAP versus MOLAP
ROLAP versus MOLAP
Data Mining and Business Intelligence
Data Mining: Confluence of Multiple Disciplines
The Data Mining Process
Data Mining – Some Issues to Consider
Three Schemes in Classification
Data Mining: Classification Schemes
Are All the “Discovered” Patterns Interesting?
Can It Find All and Only Interesting Patterns?
Requirements and Challenges in Data Mining
Requirements/Challenges in Data Mining (Cont.)
Amazon.com: Case study in building customer loyalty
Why is loyalty important
Remarks on Amazon.com
The Analytics Behinde-Marketing
Shopping Pipeline Analysis
Data Driven Customer Segmentation Model
Customer Score Interpretation
Aggregation Service Model
‘Tip-of-the-Iceberg’ Analysis for a Brokerage House
Household Lifestyle Preference Analysis for a Survey
‘True-Wallet-Share’ Analysis for a Credit Card Organization
Business Implications ofTrue Wallet Share Analysis
Business Implications (contd.)
Targeted Ad Serving
Solution
let’s now talk about privacy …
Public Attitude Towards Privacy

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