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MASS COLLABORATION AND DATA MANAGEMENT
Raghu Ramakrishnan
Professor, University of Wisconsin-Madison
CTO, QUIQ
Page 2
University of Wisconsin-Madison
DATA MINING IN 2010
• Two possible futures:– Stand-alone suite of analysis tools
• E.g., part of SAS
– Embedded in various applications• E.g., Blue Martini, QUIQ
• What will the dominant paradigm be?
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University of Wisconsin-Madison
SERVICE ORGANIZATIO
N
MEETINCREASIN
G DEMAND
CONTROLRISINGCOSTS
SOLVESERVICE
COMPLEXITY
IMPROVECUSTOMER
SATISFACTION
CUSTOMER SERVICE CHALLENGES
Page 4
University of Wisconsin-Madison
“OLD” SERVICE PARADIGM
Support Center
Customer
Web Support
KB
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University of Wisconsin-Madison
MASS COLLABORATION
KNOWLEDGEBASE
MASS COLLABORATION-Experts -Partners-Customers -Employees
QUESTION
Answer added to power self
service
SELF SERVICE
ANSWER
People using the web to share
knowledge and help each other find
solutions
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University of Wisconsin-Madison
CURRENT KNOWLEDGE BASES
SupportKnowledge Base
•Requires expensive knowledge engineering
•FAQs & static knowledge not good enough … leading to increased call volume
•Knowledge base only contains what company knows
+ -•Agent knowledge management increases productivity
•“Solutions” eliminate repeat inquiries
•Web knowledge base enables “customer self-service”
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University of Wisconsin-Madison
CURRENT “MASS COLLABORATION”
SupportNewsgroups
•Low “signal to noise” ratio (designed for “social conversations”)
•Hard to find existing “solutions”… similar questions asked over & over again
•Threaded discussions not popular with novice users
+ -
•Many high-tech leaders offer informal support newsgroups or message boards
•Small circles of user enthusiasts actively use them
•Low-cost way to tap into the expertise of thousands …
Page 8
University of Wisconsin-Madison
Support Newsgroups
Support Knowledge Base
Call Center
QUIQ MASS COLLABORATION
Solutions
Interactions
Few
Exp
ert
s
Man
yExp
ert
s
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University of Wisconsin-Madison
TYPICAL SERVICE CHAIN
Self Service
Knowledge base
FAQAuto Email
Manual Email
ChatCall
Center2nd Tier Support
50% 40% 10%
$$ $$$$
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University of Wisconsin-Madison
SERVICE CHAIN WITH QUIQ
Self
Service
Manual Email
Chat Call Center
2nd Tier Support
80% 15% 5%
MassCollaboration
QUIQ QUIQ
$$ $$$$
Page 11
University of Wisconsin-Madison
CASE STUDIES: COMPAQ
“In newsgroups, conversations disappear and you have to ask the same question over and over again. The thing that makes the real difference is the ability for customers to collaborate and have information persistent. That’s how we found QUIQ. It’s exactly the philosophy we’re looking for.”
“Tech support people can’t keep up with generating content and are not experts on how to effectively utilize the product … Mass Collaboration is the next step in Customer Service.”
– Steve Young, VP of Customer Care, Compaq
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University of Wisconsin-Madison
CASE STUDIES: NI
“To reduce service costs and provide value, B-to-B sites must deploy a Meta-Service Network that permits customer-to-customer collaboration. Companies should seek out vendors that have domain experience, such as QUIQ, to assist in deploying such a network.
Austin-based National Instruments deployed such a Network to capture the specialized knowledge of its clients and take the burden off its costly support engineers, and is pleased with the results. QUIQ increased customers’ participation, flattened call volume and continues to do the work of 50 support engineers.”
– David Daniels, Jupiter Media Metrix
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University of Wisconsin-Madison
CASE STUDIES
“…I am thrilled that I found the [QUIQ] forum now. I will be able to solve my problems…” “…the [QUIQ] forum is best because there are SO MANY people having to fix problems… I look to other experienced users and plug away…”
– QUIQ end-users
“There is no better place to make customers for life than during their support interactions… Forums can be powerful retention tools because they create community and build loyalty, not only to your company, but to your customer base as well”
– Hans Peter Brondmo, author of “The Engaged Customer”
“iPlanet relies almost entirely on its 100,000 registered users to serve as a virtual help line. Each question answered this way saves iPlanet between $50 and $100.”
– Franz Aman, Director of iMarketing, iPlanet
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University of Wisconsin-Madison
DATA MANAGEMENT FOR MASS COLLABORATION
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University of Wisconsin-Madison
MASS COLLABORATION
• Content driven by users; changes rapidly.• Interactions must be structured to
encourage creation of “solutions”.• Search central to giving user best
available solution, avoiding noise.• Notifications drive participation, routing.
– Extension of search; scalable triggers.
Communities + Knowledge Management + Service Workflows
Page 16
University of Wisconsin-Madison
SEARCH AND INDEXING
• Quality and performance– Must exploit metadata to improve quality of results, in
addition to considering text.– Must be fast!
• Control– Enterprise customers demand ability to “tune” search
behavior.
• Timeliness– Can’t afford to index once a day.
Text plus metadata, updated constantly
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University of Wisconsin-Madison
SEARCH AND INDEXING
• KB of Qs and As, each with lots of metadata – Author status, popularity, date info, approval status, etc.
• User types in “How can I configure the IP address on my Presario?”– Need to find most relevant content that is of high quality and is
approved for external viewing.
• User decides to post question because no good answer was found in the KB.– Search controls when experts and other users will see this new
question; need to make this (near) real time.– Concurrency, recovery issues!
Page 18
University of Wisconsin-Madison
DBMS vs. IR
• Database systems and IR systems have developed as independent silos.– DB: Flexible tables, queries; concurrency control,
recovery– IR: Fast text search; based on “relevance secret
sauce”, with little user control
• Mass collaboration requires a hybrid system.
Page 19
University of Wisconsin-Madison
A HYBRID DB-IR SYSTEM
• Searches are queries that can specify boolean filters, and control relevance:– Degree of match– Quality of matching document
• Can effectively leverage metadata about text, including some obtained by data mining.
• Data indexed (near) real-time.• Foundation of QUIQ’s mass collaboration
application.
Page 20
University of Wisconsin-Madison
DATA MINING TASKS
• There is a lot of insight to be gained by analyzing the data.– What will help the user with his problem?– Who does a given user trust?– Identify high-quality content.– Summarize content.– Who can answer this question?
• Question: What does it take to leverage this insight?
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University of Wisconsin-Madison
LEVERAGING DATA MINING
• How do we get at the data?– Relevant information is distributed across
several sources, not just the DBMS.
• How do we incorporate the insights obtained by mining into the search phase?– Need to constantly update info about every
piece of content (Qs, As, users …)
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University of Wisconsin-Madison
LEVERAGING DATA MINING
• Three-step approach:– Off-line analysis to gather new insight– Periodic refresh of KB and/or indexes– Use insight (from KB/index) to improve
search
• “Periodically” updating an “offline” index is the key idea behind:– Supporting (near) real-time search– Incorporating mining results into
search
Page 23
University of Wisconsin-Madison
A LIST OF CHALLENGES
• Similarity (real-time)• Matching (real-time)• Trends (off-line)• Correlation (off-line)
Page 24
University of Wisconsin-Madison
The Similarity Problem
• Find users with similar tastes, in context.– Joe’s looking at an Athlon processor; which users are
similar to Joe in their PC tastes? Whose recommendations is Joe likely to follow?
• Find similar content, in context.– Which processors are similar in that they appeal to the
same groups of people?– Which processors are similar in that they have similar
performance characteristics?– Which articles appeal to the same people?
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University of Wisconsin-Madison
The Matching Problem
• Match user to data, in context.– What related information should you
recommend to Joe when he is looking at the Athlon PC product?
• Related products: graphics cards, monitors• Related reviews, discussions • If Joe’s been looking only at AMD products, other
AMD chips; if not, show alternatives from Intel
• Match data to user, in context.– Which expert is best qualified to answer Joe’s
question?
Page 26
University of Wisconsin-Madison
The Trends Problem
• Identify trends in sales.• Identify trends in overall user preferences,
user segmentation.• Identify trends for individual users.• Identify trends in overall product
popularity, product segmentation.• Identify trends for specific products.
Page 27
University of Wisconsin-Madison
The Correlations Problem
• Given a set of trends (e.g., in pricing) track the impact on other trends. – Are there correlated trends?– Are there causal relationships?
• Note that correlating a given trend to an overall trend is hard enough, but trying to find all other individual or product-specific trends that happen to be correlated is much harder!