more data mining success stories for marketing and related fields
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More Data Mining Success Stories for Marketing and Related Fields. Wolfgang Jank RH Smith School of Business University of Maryland. What is “Data Mining”?. What is Data Mining?. Many Definitions - PowerPoint PPT PresentationTRANSCRIPT
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More Data Mining Success Stories for Marketing and Related FieldsWolfgang JankRH Smith School of BusinessUniversity of Maryland
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What is “Data Mining”?
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What is Data Mining?
Many Definitions Non-trivial extraction of implicit, previously
unknown and potentially useful information from data
Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns
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Related Fields
Statistics
MachineLearning
Databases
Visualization
Data Mining and Knowledge Discovery
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Why Mine Data?
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Because there are Data Floods….
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Lots of data is being automatically collected and warehoused Web data, e-commerce Scanner data at department/
grocery stores Bank/Credit Card/Insurance
transactions
Computers have become cheaper and more powerful
Competitive Pressure is Strong Provide better, customized services for an edge
Why Mine Data?
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Big Data Examples
Europe's Very Long Baseline Interferometry (VLBI) has 16 telescopes, each of which produces 1 Gigabit/second of astronomical data over a 25-day observation session storage and analysis a big problem
AT&T handles billions of calls per dayso much data, it cannot be all stored -- analysis
has to be done “on the fly”, on streaming data
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Data Growth
In 2 years, the size of the largest database TRIPLED!
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Data Mining is particularly promising Online
Why?Because every “click” leaves a digital footprintWe can use these footprints to better
understand our customers… Coupons, ads, discount, dynamic pricing, …
…or guard them against predators Fraud detection, account protection, spam, junk
mail, viruses, …
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Blog Pulse
Measures what the world (= the internet) is thinking Measured in
terms of the blogging activity
The “Obama Buzz” started here!
The Republican Convention & Sarah Palin
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Google Trends
Measures what the world is looking for Measured in
terms of search words
The world’s interest in “Lehman Brothers” and “AIG”
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Google Flu Trends Detects outbreaks of
flu early and only based on search terms More accurate and
faster than CDC Read more at
http://www.google.org/flutrends/
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Data Mining Success Stories
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The Netflix Recommendation Engine Netflix uses data mining to
make recommendations to its users Based on past user behavior Based on movie similarities
Helps cross-selling of products
Improves the search experience for users
However, developing good recommendation engines is not easy; therefore, Netflix has initiated the Netflix Challenge
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The Netflix Challenge
Netflix offers $1 million for the person/team that can improve their current data mining method by 10% (i.e. classification accuracy) http://www.netflixprize.com/ Incremental progress prizes of $50,000 every year AT&T team has won progress prize in 2007
“The Netflix Prize seeks to substantially improve the accuracy of predictions about how much someone is going to love a movie based on their movie preferences”
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Amazon’s Recommendation Engine
Every time we buy a book on Amazon, we receive recommendations about similar books
How are they doing this?
The answer: massive data mining
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Google’s Search Algorithm
Google continuously collects data about web pages using web spiders
It transforms this massive data into search information using the famous “page-rank” algorithm
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AT&T’s Fraud Detection
Name Elizabeth Harmon
Address APT 1045
4301 ST JOHN RD
SCOTTSDALE, AZ
Balance $149.00
Disconnected 2/19/04 (nonpayment)
Name Elizabeth Harmon
Address 180 N 40TH PL
APT 40
PHOENIX, AZ
Balance $72.00
Connected 1/31/04
Fraudulent account: terminated!
Should this new account be allowed?
In the AT&T telephone network, every day old nodes drop out (terminated accounts) and new nodes pop up (new accounts)
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AT&T’s Fraud Detection
AT&T uses massive graph mining to detect fraud in their telephone network data
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Mining Accounting Fraud at PricewaterhouseCoopers
PwC uses data mining for the automatic analysis of company general ledgers to detect accounting fraud
Helps conform with Sarbanes-Oxley Act Improves efficiency Improves accuracy
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Sales Lead Identification at IBM
IBM uses predictive modeling to estimate opportunities for cross-selling to existing customers, selling of existing services to new customersUses analytic tools to estimate
A potential customer’s wallet size A potential customer’s probability of purchasing
a service
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Data Mining at IBM
New Rational sales
Historical System p sales
Historical total Software sales
State is CA
Sector is IT
Company is HQ
Historical Lotus sales
Historical System x sales
IBM RelationshipFirmographics
Historical System z sales
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zata3: Data-Driven Decisions in Election Campaigns
zata3 is an election campaign consulting company
They recently decided to add data mining technology to their services
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zata3: Lot’s of data on voters and past voting behavior
PARTY_CODE Gender Education Children Home_Owner Income Times DonatedA 0 3 1 4 3 0R 0 0 4 2 0A 1 0 3 1D 1 0 0 0D 1 5 0 2 6 0R 1 0 4 3 0R 1 0 4 1 0R 1 0 0 0D 1 7 1 2 4 0D 1 0 0 0
General(00-03) Presidential Primary VH General VH Presidential VH Primary G04 VotedY 1 0 0 0YYYY R 4 1 0 1YY 2 0 0 1
0 0 0 1YYYY DD 4 2 2 1YYY RR 3 2 1 1YYYY RR 4 2 1 1
0 0 0 1YYYY DD 4 2 3 1
0 0 0 1
Goal: to predict who will vote in the next election
Idea: better targeted spending of election campaign resources
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zata3: Huge savings with data mining Zata3 anticipates savings of over 30%
using data mining models
Traditional Total Cost Voted Cost Per Vote74,522.50$ 14664 5.08$
With Data Mining Total Cost Voted Cost Per Vote52,806.64$ 15626 3.38$
Savings Total Cost Votes Cost Per Vote % Savings21,715.86$ 962 1.70$ 34%
Analysis
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Data Mining and Mass e-Customization
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Customization for Online Services
Opportunities: Combination of countless
features for highly individualized solutions
“A single personalized solution for every customer”
Challenges: How does the customer
understand what’s right for them?
Moving from consultative selling to self-consultative buying
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Ex.: Freddie Mac Mortgage Services Freddie Mac mass
customizes mortgage products Combines hundreds of
different loan characteristics
Challenge: How does the customer find the loan that’s right for them?
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Ex.: Mass Customization at eBay
eBay offers any possible product & service in “garage-type” sales However, it does not assist the customer much in
finding the right product/service.
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Ex.: Books on Amazon
Amazon.com offers books for every taste But: How can we find the book that’s right for us?
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Managing Mass Customization at Amazon How does Amazon assure that customers
find what they are looking for?Answer: by making (automated)
recommendations
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Managing Mass Customization From Expert Salesperson to Expert System:
How can we assure that our customers get what they are looking for?
Pre-Internet customization: Expert Salesperson
Experienced with product, process
Consultative selling Salesperson provides
expertise, identifies needs, defines configuration
Early/current-Internet customization: Expert Customer
Experiences with product Revelation, Transaction buying Customer provides expertise,
knows needs, defines configuration
Future Internet Customization: Non-Expert Customer
Inexperienced with product, process
Self-consultative buying System provides expertise,
identifies needs, defines configuration
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Providing the non-expert customer with decision support
Moving from Expert to Non-Expert Buyers: Computerization
Assisted service Telephone, email,
instant messaging Drawback: requires
human interaction, only limited scalability
Self service Search, user ratings, forums,
blogs, expert recommendations
Drawback: does not help the customer that is unsure about their needs
Automated service Expert systems for the non-expert Replaces the salesperson Translates customer characteristics and usage requirements into
recommended product configurations Consists of rule-based systems and data mining algorithms Advantage: fully automatic, scalable, updatable
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Ex.: Automated-Service at AmEx Offers online tool that, based on desired
features, recommends best card Compensates only for lack of product knowledge,
but assumes customer knows why they need the product.
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Ex.: Blockbuster’s Recommendation System
Blockbuster recommends similar movies based on movie features and user behavior “If you liked Indiana
Jones, then you will also like Tomb Raider”
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Key Component for Automated Service Systems: Data Mining
Collect and mine customer information in order to, e.g., Segment the market
Understand customers’ different needs, expertise, profitability E.g. Dell distinguishes between the segments “Home”, “Small
Business”, “Medium/Large Business”, “Public Sector” Analyze behaviors and events
Understand when customer has needs and the events that lead to them
E.g. path tracking, click stream analysis Optimize prizing
Bundling, price discrimination E.g. Amazon’s price testing; Zilliant’s data-driven pricing software
Key requirement: understand customer data
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Dangers of Data Mining
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Dangers of Data Mining
The danger of using data mining software/technology as a “black box”Data does not mine itself!We still need the domain knowledge and
expertise of the user; otherwise outcomes may be meaningless
Data qualityJunk-in, junk-out
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What Data Mining Isn’t
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Data Mining Isn’t… …smarter than you
Example from DeVeaux: A new backpack inkjet printer is showing higher
than expected warranty claims A neural networks analysis shows that Zip code is
the most important predictor
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Data Mining Isn’t… …always about algorithms
Sometimes collecting an plotting the right data is enough
Blogpulse
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More Data Mining Resources
Repository:http://www.kdnuggets.com/http://www.the-data-mine.com/
Tutorialshttp://www.autonlab.org/tutorials/
SoftwareSAS Enterprise Miner, SPSS Clementine,
Orange, Weka, Rattle, R, …