MASS COLLABORATION AND DATA MANAGEMENT Raghu Ramakrishnan Professor

MASS COLLABORATION AND DATA MANAGEMENT Raghu Ramakrishnan Professor, University of Wisconsin-Madison CTO, QUIQ

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? University of Wisconsin-Madison Page 2

CUSTOMER SERVICE CHALLENGES MEET INCREASING DEMAND IMPROVE CUSTOMER SATISFACTION SERVICE ORGANIZATION CONTROL RISING COSTS SOLVE SERVICE COMPLEXITY University of Wisconsin-Madison Page 3

“OLD” SERVICE PARADIGM Web Support KB Customer Support Center University of Wisconsin-Madison Page 4

MASS COLLABORATION QUESTION People using the web to share knowledge and help each other find solutions SELF SERVICE KNOWLEDGE BASE Answer added to power self service ANSWER MASS COLLABORATION -Experts -Partners -Customers -Employees University of Wisconsin-Madison Page 5

CURRENT KNOWLEDGE BASES + Support Knowledge Base • Agent knowledge management increases productivity • “Solutions” eliminate repeat inquiries • Web knowledge base enables “customer self-service” • Requires expensive knowledge engineering • FAQs & static knowledge not good enough … leading to increased call volume • Knowledge base only contains what company knows University of Wisconsin-Madison Page 6

CURRENT “MASS COLLABORATION” + Support Newsgroups • 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 … • 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 University of Wisconsin-Madison Page 7

Support Newsgroups Few Experts Many Experts QUIQ MASS COLLABORATION Call Center Support Knowledge Base Interactions Solutions University of Wisconsin-Madison Page 8

TYPICAL SERVICE CHAIN 40% 50% FAQ Self Service Knowledge base $ Auto Email Manual Email Chat $$ 10% Call Center 2 nd Tier Support $$$ University of Wisconsin-Madison Page 9

SERVICE CHAIN WITH QUIQ 80% 15% QUIQ Mass Collaboration QUIQ Self Service $ Manual Email Chat $$ 5% Call Center 2 nd Tier Support $$$ University of Wisconsin-Madison Page 10

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 University of Wisconsin-Madison Page 11

CASE STUDIES: NI “To reduce service costs and provide value, B-to-B sites must deploy a Meta-Service Network that permits customer-tocustomer 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 University of Wisconsin-Madison Page 12

CASE STUDIES “i. Planet relies almost entirely on its 100, 000 registered users to serve as a virtual help line. Each question answered this way saves i. Planet between $50 and $100. ” – Franz Aman, Director of i. Marketing, i. Planet “…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” University of Wisconsin-Madison Page 13

DATA MANAGEMENT FOR MASS COLLABORATION University of Wisconsin-Madison Page 14

MASS COLLABORATION Communities + Knowledge Management + Service Workflows • 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. University of Wisconsin-Madison Page 15

SEARCH AND INDEXING Text plus metadata, updated constantly • 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. University of Wisconsin-Madison Page 16

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! University of Wisconsin-Madison Page 17

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. University of Wisconsin-Madison Page 18

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. University of Wisconsin-Madison Page 19

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? University of Wisconsin-Madison Page 20

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 …) University of Wisconsin-Madison Page 21

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 University of Wisconsin-Madison Page 22

A LIST OF CHALLENGES • • Similarity (real-time) Matching (real-time) Trends (off-line) Correlation (off-line) University of Wisconsin-Madison Page 23

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? University of Wisconsin-Madison Page 24

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? University of Wisconsin-Madison Page 25

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. University of Wisconsin-Madison Page 26

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! University of Wisconsin-Madison Page 27