Predictive Analytics over On-line and Social Network Data · 2010-06-08 · finance, autos, travel,...
Transcript of Predictive Analytics over On-line and Social Network Data · 2010-06-08 · finance, autos, travel,...
Open Insights
Predictive Analytics overOn-line and Social Network Data
Usama Fayyad, Ph.D.CEO, Open Insights
www.open-insights.com
Copyright 2009 by U.M. Fayyad, All rights reserved0
www.open-insights.com
Open Insights
Some of this material extracted from prior talk: The Evolution of Search Technology
and the Role of Social Networking in Marketing
Research
Usama Fayyad, Ph.D.CEO, Open Insights
1
Acknowledgement
Some of the work presented in This talk was done in
former position:
Chief Data Officer & Executive VP
Yahoo! Inc.
Copyright 2009 by U.M. Fayyad, All rights reserved www.open-insights.com
Background and Lessons
• Grad student forever… at University of Michigan, Ann Arbor
– after 5 degrees, Lesson: real world can be more exciting
• Jet Propulsion Lab, California Institute of Technology (1989-95)
– Data mining can help the highly specialized tremendously
• Microsoft (1995-2000)
– The real problem is not data mining technology, but operations
• digiMine Inc. (2000-2003) – [now Audience Science]
– The problem is explaining the strategic value of Data to exec team and connect with business strategy
• DMX Group (2003-2004+)
– Data strategy in business intelligence and delivering on the deeper value
• Yahoo! (2004 – 2008)
– The Internet is a much deeper social and commercial transformation than I ever imagined
• Open Insights (October 2008 - )
– Back to Data Strategy and Data Mining
2
Copyright 2009 by U.M. Fayyad, All rights reserved www.open-insights.com
Overview• Yahoo! and On-Line Marketing Primer
• From Search/Advertising to Marketing:
– The Evolution of Search and search marketing
– More focus on Search Marketing
• Case-study: Impulse (Search-after)
• Case Study: Behavioral Targeting
• Case Study: Retargeting
• Case Study: Choicestream ad personalization
• The Evolution of “Social Media” (Introduction)
• Wisdom of the Crowds
• Case Study: Social Targeting at Yahoo!
• Case Study: Social Targeting using Twitter (VH1 example)
• What Does this mean for Search?
– Where does Search evolve to?
• Concluding Thoughts
3
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506.3
602.4
702.4
787.5
868.3
974.3
1,076.7
0.0
200.0
400.0
600.0
800.0
1,000.0
1,200.0
2001 2002 2003 2004 2005 2006 2007
Worldwide Total Japan
Rest of World Western Europe
Asia/Pacific United States
Globally, Internet Users Outnumber 1 Billion by 2007
Source: IDC, December 2003.
Internet Users in Millions:
4
Copyright 2009 by U.M. Fayyad, All rights reserved www.open-insights.com
Yahoo! is the #1 Destination on the Web
73% of the U.S. Internet population uses Yahoo!
– Over 500 million users per month globally!
• Global network of content, commerce, media, search
and access products
• 100+ properties including mail, TV, news, shopping,
finance, autos, travel, games, movies, health, etc.
• 25 terabytes of data collected each day… and
growing
• Representing thousands of cataloged consumer behaviors
More people visited
Yahoo! in the past
month than:
• Use coupons
• Vote
• Recycle
• Exercise regularly
• Have children
living at home
• Wear sunscreen
regularly
Sources: Mediamark Research, Spring 2004 and comScore Media Metrix, February 2005.
Data is used to develop content, consumer, category and campaign insights for our
key content partners and large advertisers
5
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Yahoo! Data – A league of its own…
Terrabytes of Warehoused Data
25 49 94 100500
1,000
5,000
Amaz
on
Kore
a
Tele
com
AT&T
Y! L
iveS
tor
Y! P
anam
a
War
ehou
se
Wal
mar
t
Y! M
ain
war
ehou
se
GRAND CHALLENGE PROBLEMS OF DATA PROCESSING
TRAVEL, CREDIT CARD PROCESSING, STOCK EXCHANGE, RETAIL, INTERNET
Y! Data Challenge Exceeds others by 2 orders of magnitude
Millions of Events Processed Per Day
50 120 225
2,000
14,000
SABRE VISA NYSE YSM Y! Global
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What we needed at Yahoo!
• Data Strategy
• Prioritization conducted based on business need, not IT
– Drove new innovations in DB
– Data segmentation management
– Specialized stores
– File systems for grid computing (Hadoop)
• Had to resort to own home-grown data stores for largest applications
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Advertising: Brand and DR
Knowledge of users & their behavior throughout the purchase funnel can
grow brand & direct response revenue
Awareness
Purchase
Consideration
> $220B Brand Advertising Market
> $250B Direct Marketing Market
Most time & activity is in consideration & engagement, but there are limited metrics & reach strategies
8
Open Insights
A Tale of Two Search Engines
Research 9
www.open-insights.com
Copyright 2009 by U.M. Fayyad, All rights reserved www.open-insights.com
Algorithmic results
=Audience
Advertisements
=Monetization
-$ +$
10
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Algorithmic vs. Ad Search
• Analogous to classical separation of editorial vs commercial content
• Technical underpinnings:
– Some commonalities (IR, ML)
– Many differences (incentives, spam, mechanism design)
• Will cover both algo and ads
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Open Insights
Search advertising
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www.open-insights.com
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Search query
Ad
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A Question for the audience
• Do you think an “average” user, knows the difference between sponsored search links and algorithmic search results
• Do you think an “average” user knows there are sponsored links on the page?
• Do you think a user knows where a sponsored link would navigate to upon a click?
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Ads go
in slots
like
these
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Higher
slots
get
more
clicks
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Economic ordering
• Yahoo!/Overture started with Bid Ordering of search adsThe only business model at the time
• Google introduced revenue ordering to avoid the Overture patent
– Resulted in 4x – 6x revenue per search!
• Bid and revenue ordering: two forms of ordering by an econscore
– Auction mechanism matters (Vickrey Auction)
– Is not the right generalization to Search Ads (Edelman, Ostrovsky, Schwarz 2006)
• Does revenue ordering maximize revenue?
• No – advertisers react to ordering scheme, by changing their bid behavior!
• Lahaie+Pennock ACM EC 2007
– Family of schemes bridging Bid and Revenue ordering
– Game-theoretic analysis
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Copyright 2009 by U.M. Fayyad, All rights reserved www.open-insights.com
A new convergence
• Monetization and economic value an intrinsic part of system design
– Not an afterthought
– Mistakes are costly!
• Computing meets humanities like never before –sociology, economics, anthropology …
• We call it Computational Advertising
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Open Insights
Why is search-related advertising so powerful?
A question for the Audience:
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Moving Customers up the Funnel
Impulse Banners– Target users based on their activity – both search and
property -- within the NEXT HOUR
• Behavioral Categories – Apparel, Computers, Home Appliances – all the same categories that you can use for regular behavioral targeting!
Awareness
Purchase
Consideration
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Impulse Banner Example
All within
1 HOUR2
Sees that “Credit
Card” falls under
the category:
“Finance/Credit
and Credit
Services”
25% - 261% higher CTR than RON
1
User
searches on
the word
“Credit
Card”
3
serves
“Finance/Credit and
Credit Services”
banners to User
anywhere on the
Yahoo! network
within 1 HOUR
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Way Impulse Works
• Searches are not at all associated or tracked through personally identifiable information
• No long-term memory of search terms, all stored on client cookie.
• We generalize the category is targeting is at generic category: e.g. Financial Services, not “credit card”
• All targeting done in anonymous mode
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BT2.0 vs. Impulse (test data)
+475.8%
CTR Lift
+291.5%
Conv Lift
*Note: these are straight averages rather than weighted averages. Therefore, might not be the best representation of the true overall CTR lift.
•Shoppers Testing (18 tests across 10
verticals)
CONCLUSION:
BT2.0 can utilize
Search+Behavior Data to
deliver higher effectiveness,
and much larger inventory
than targeting by Search
queries only!
CTR Lift
Over
Impulse
Reach lift
Over
Impulse
Autos 171% 254%
CPG 147% 308%
Entertainment 156% 559%
Finance 264% 404%
Health & Pharma 70% 588%
Life Stages 75% 493%
Retail 65% 424%
SMB & B2B 116% 984%
Sports 222% 429%
Tech 129% 867%
Travel 59% 246%
Average 144% 652%
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Behavioural Targeting (BT)
Search
Ad Clicks
Content
Search Clicks
BT
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What‟s „Behavioural Targeting‟?
• Targeting your ads toconsumers whoserecent behaviors online indicate that your product category is relevant to them...
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Moving Down the Funnel
• New generation marketing solutions to take brand advertisers down the marketing funnel
Awareness
Purchase
Consideration
26
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AUTO
S / SE
DAN
Beha
vioura
l Inter
est
Sedan
What goes into user behavior models?
• Relevant behaviors…
• Pageviews
• Ads clicked
• Search queries
• Search clicks
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How it works | Network + Interests + Modelling
Analyze predictive patterns for purchase cycles
in over 100 product categories
In each category, build models to describe
behaviour most likely to lead to an ad response
(i.e. click).
Score each user for fit with every
category…daily.
Target ads to users who get highest ‘relevance’
scores in the targeting categories
Varying Product Purchase CyclesMatch Users to the ModelsRewarding Good BehaviourIdentify Most Relevant Users
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Recency Matters and So Too Intensity
Active now… …and with feeling
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Differentiation | Category specific modelling
time
inte
nsi
ty s
core
time
inte
nsi
ty s
core
Inte
nse
Cli
ck Z
one
Example 1: Category Automotive Example 2: Category Travel/Last Minute
Different models allow us to weight and determine intensity and recency
Alt Behaviour 1: 5 pages, 2 search keywords, 1 search click, 1 ad click Alt Behaviour 1: 5 pages, 2 search keywords, 1 search click, 1 ad click
Inte
nse
Cli
ck Z
one
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Differentiation | Category specific modelling
time
inte
nsi
ty s
core
Intense Click Zone
Example 1: Category Automotive
Different models allow us to weight and determine intensity and recency
with no further activity, decay takes effect
Alt Behaviour 1: 5 pages, 2 search keywords, 1 search click, 1 ad click
user is in the Intense Click Zone
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Audiences Defined by Interests and Actions
We found:
1,900,000 people looking for
mortgage loans.
+122%
CTR Lift
Mortgages Home Loans Refinancing Ditech
Financing section in Real Estate
Mortgage Loans area in Finance
Real Estate section in Yellow Pages
+626%
Conv Lift
Example search terms qualified for this target:
Example Yahoo! Pages visited:
Source: Campaign Click thru Rate lift is determined by Yahoo! Internal
research. Conversion is the number of qualified leads from clicks over number of impressions served. Audience size represents the audience within this behavioral interest category that has the highest propensity to engage with a brand or product and to click on an offer.
Date: March 2006
Results from a client campaign on Yahoo!
NetworkExample: Mortgages
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Products: Retargeting
• Target customers based on any past event
• Lead consumers down the purchase funnel
• Can be quite clever creatively
– Improving offers
• There are time limitations
Next visit on Yahoo!
An event
e.g. People who viewed a certain
ad
Second ad with additional
information increases awareness,
engagement!
Retargeting
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Example: Retargeting – BMW in Germany
BMW ran a homepage event in Germany
Can re-target those who saw the event, clicked on the event or
searched for BMW or a combination
Can increase conversion
Copyright 2009 by U.M. Fayyad, All rights reserved www.open-insights.com
Brand Ads and Search Ads Interact!
• Is ad search strategy enough for a direct marketer?
• Do brand ads play a role in search advertising?
• Harris Direct Case Study
Awareness
Purchase
Consideration
35
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Case Study: Harris Direct
On:
Viewing These Ads: Had This Effect On:
• Brand Favorability– Up 32%
• Purchase Intent– Up 15%
• Aided Brand Awareness– Up 7%
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Case Study: Harris Direct
People who saw display ads were 61% more likely to search on related topics…
…and drove 139% more clicks on
algorithmic and sponsored links…
…specifically driving 249% more
sponsored search clicks …
…and driving 91% more activity
on the HarrisDirect.com website.
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Personalization Case-Study
Overstock.com Case Study
Copyright 2009 by U.M. Fayyad, All rights reserved www.open-insights.com
Brand LoyaltyPersonalized
Brand Experience
Personalized Home Page
Brand Advertising
Personalization Effects on Consumer Marketing
Across All Retail Channels
Personalization Drives Performance Everywhere
SalesPersonalized Up Sells
& Cross-sells
Product Detail
Shopping Cart
Order Confirmation
RetentionPersonalized
Re-Targeting
Display Ads + Landing Page
Email & Subject Line Offers
AcquisitionRelevant
Advertising
Landing Pages
Display Advertising
Banner Affiliates
Call Center & In-Store Kiosk
Product Recommendations
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Targeted AdvertisingPersonalized Ad Delivery
Results:
550% higher
revenue per
impression
Consumer Value Drives Business Performance
RecommendationsDiscovery, Serendipity
Results:50% longer
rental queues
CommerceOptimized „Next Sell‟ & „Cross Sell‟
Results:250% more sales
per visit
Direct MarketingTargeted Mail & Promotions
Results:30% Increase in
VOD sales
CommunityLike-Minded Buddies& Social Groups
Results:
300% longer
subscriber
lifetime
Product Promotions„More Like This…‟ Complements
Results:
400% ROI
in 5 mos.
40
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Marcia• Bought comforter last week
• Registered for baby items
• Abandoned cart with coat
Fran• Recently bought a coat
• Lately browsing jewelry
• Added shoes to wish list
Overstock Showed Every User the Same Ad
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Marcia• Bought comforter last week
• Registered for baby items
• Abandoned cart with coat
Fran• Recently bought a coat
• Lately browsing jewelry
• Added shoes to wish list
ChoiceStream Personalizes Each Ad…
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FranClicks on product in ad
Personalized Landing PageContains more product recommendations
…Giving Shoppers a Compelling “Call to Purchase”
Copyright 2009 by U.M. Fayyad, All rights reserved www.open-insights.com
Overstock Cares About ChoiceStream RealRelevance®
Advertising Lift*
Conversions
per Thousand Impressions Over 3x
Revenue
per Thousand Impressions 3x-5x
Improving the Metrics that Matter Most
* Source: Actual results based on A/B tests comparing advertiser's best-performing display ads against ChoiceStream
ads with personalized product recommendations.
Open Insights
and the wisdom of the crowds...
Social Media
Research
www.open-insights.com
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Evolution of Social Media
• Although the “traditional notion” of portal and web content is still attracting growing audiences
• The original notion of “publishing content” to attract audiences is changing fast
– As people discover the fact that the Internet is an Interactive Medium
– The uses of the Internet enter areas we could not imagine a short time ago
• A new notion of “publishing” is fast emerging
– The opportunity of user-generated content
46
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Examples of Social Media
• Blogs
– The individual as publisher
– Comments and tags part of the process
• Sharing Photos: e.g. Flickr
• Social Search
– My Web 2.0
– Yahoo! Answers
– Del.icio.us
• Web communities:
– Yahoo! Groups
– Individual web presence: Facebook, MySpace, Yahoo! 360, Friendster, …
• Video sharing: You Tube, Yahoo! Video, etc…
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What makes Flickr special?
1. User Generated Content
Content not licensed from providers such as Corbis or Getty, but rather contributed by
users.
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What makes Flickr special?
2. User Organized Content
Content is tagged, described, organized, discovered, etc. not by “editors” but by the
users themselves.
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What makes Flickr special?
3. User Distributed Content
Flickr achieved distribution across the internet, not through “business deals” per se, but
rather through the Flickr community which distributed Flickr content on 3rd-party blogs.
52
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What makes Flickr special?
4. User Developed Functionality
Flickr exposed APIs (PHP, Perl, etc.) that allowed the community of developers to build
against the Flickr platform.
53
Copyright 2009 by U.M. Fayyad, All rights reserved www.open-insights.com
What makes Flickr special?
1. User Generated Content
Content not licensed from providers such as Corbis or Getty, but rather contributed by
users.
2. User Organized Content
Content is tagged, described, organized, discovered, etc. not by “editors” but by the
users themselves.
3. User Distributed Content
Flickr achieved distribution across the internet, not through “business deals” per se, but
rather through the Flickr community which distributed Flickr content on 3rd-party blogs.
4. User Developed Functionality
Flickr exposed APIs (PHP, Perl, etc.) that allowed the community of developers to build
against the Flickr platform.
Entire ecosystem created by less than ten employees…
aided by millions in the Flickr community.
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New Science?
• The Internet touches all of our lives: personal, commercial, corporate, educational, government, etc…
• Yet many of the basic notions we talk about:
– Search, Community, Personalization, Engagement, Interactive Content, Information Navigation, Computational Advertising
– Are not at all understood, or well-defined
– These are not disciplines that academia or any industry research labs focus on…
55
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Areas of Research
• Community:– How do you know what to believe on the Internet?
– Trust models on-line and trust propagation
– What makes communities thrive? Whither?
– Social media, tagging, image and video sharing
• Microeconomics: a new generation of economics driven by massive interactions– Auction marketplaces
– The web as a new LEI of activities and economies
• Information Navigation and Search– We are in the early days of search and retrieval
• Computational Advertising
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Open Insights
“Wisdom of Crowds”
Is the Turing test always the right question?
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A Digression:Computer Vision is hard
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What makes Flickr special?
2. User Organized Content
Content is tagged, described, organized, discovered, etc. not by “editors” but by the
users themselves.
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Challenges in social media
• How do we use these tags for better search?
• What‟s the ratings and reputation system?
• How do you cope with spam?
• The bigger challenge: where else can you exploit the power of the people?
• What are the incentive mechanisms?
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Open Insights
Illustrating New Directions on Yahoo! Network
Case Study: Social Targeting
Research
www.open-insights.com
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What is Social Targeting?
Social Targeting (ST) extends individual targeting techniques by considering
individual characteristics of a user’s close social contacts.
Individual Targeting + Social Networks = Social Targeting
Demographic
Geographic
Behavioral
Contextual
+ = Demographic
Geographic
Behavioral
Contextual
All of the above data
for friends
&
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Why use Social Targeting?
Illustrative Social Graph People demonstrating interest in
Football by playing Yahoo Fantasy
Football
Social Targeting provides indications of unobservable
and off-network interest categories.
Friends with shared interest in
Football, but who don’t play Fantasy
Football at Yahoo
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Why does Social Targeting work?
Diffusion
People within a group are exposed to similar ideas
Homophily
People seek out others with similar interests
Social Identity
Cultural preferences signal group memberships
People share similar interests and
preferences with their close social contacts.
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Yahoo! data for Social Targeting
Instant Messenger
Fantasy Sports
Yahoo!’s social networks can help improve monetization and user experience
through better ad/content targeting.
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Empirical Tests Demonstrate Value of ST
Targeting Ads at custom lists of users based on Social Extension increases
inventory without decreasing CTR
Instant Messenger
Inventory Expansion
from Seed Population
+483% from sample of
BT Weight Loss
+68% from sample of
BT Food & Nutrition
+36% from sample of
Y! Fantasy Football users
CTR of Social Extension vs
Seed Population
117% vs sample of
BT Weight Loss
104% vs sample of
BT Food & Nutrition
107% vs sample of
Y! Fantasy Football users
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Case Study: TWITTER Social Marketing?
Diffusion
Give 20 free DVD’s to major related artists/groups, ask them
To notify Twitter groups – reached over 2M people
Marketing ANVIL movie
Very niche audience, how do you reach them?
Social Identity: power of word of mouth...
What is the Cost to VH-1?Compare with traditional approach: TV commercials to promote a
documentary film?
Viacom’s VH1 Twitter campaign on ANVIL (the movie)(week of May 14th , 2009 – see AdAge article)
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Open Insights
Many, many challenges...
What Does this all Mean for Search?
Research
www.open-insights.com
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Content trends
[Ramakrishnan and Tomkins 2007]
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Metadata trends
[Ramakrishnan and Tomkins 2007]
Open Insights
Towards Getting Things Done… vs. Searching
Research 82
www.open-insights.com
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Example
I want to book a vacation in Tuscany.Start Finish
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Loved the vacation, want to make that sweet Italian coffee at home
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Trends in task complexity
• Dawn of search:
– Navigational queries
– Pockets of information
• Today:
– Increasing migration of content online
– New forms of media only available online
– Infrastructure for payments and reputation sufficient for many users
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Things to notice
• Long-running user goals
• Search as hub:
– start there
– return for resource discovery and at task boundaries
– traverse the web broadly to complete task
• Web services integrated into task
87
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What does this mean to Search?
• Publishers and search engine collaborate
• Users see richer search experience
• Accomplish their tasks faster and more effectively
• Example: abstracts surfacing structured content
• Back to the Future?
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babycenter
epicurious
An Example: Search results in Search Monkey
yelp.com
answers.com
webmd
Gawker
New York Times
Copyright 2009 by U.M. Fayyad, All rights reserved www.open-insights.com90
An Example: Search results in Search Monkey
yelp.com
babycenter
epicurious
answers.com
webmd
New York Times
Gawker
Copyright 2009 by U.M. Fayyad, All rights reserved www.open-insights.com91
What are the challenges?
• Community of users
– Social system
• Incentives and reputations
– Economic system
• Poorly phrased, grammatically limited queries
– Language analysis
• Improving user experience from past data
– Data mining
Copyright 2009 by U.M. Fayyad, All rights reserved www.open-insights.com
What we did not cover today...
• Applications of On-line Data for Enhanced Prediction
– Product Sales (e.g. Car Manufacturer)
– Customer Churn in Telco
– Banking and Financial Services
– Retail Cross-sell and Up-Sell
• The new generation of On-line Marketing Solutions
based on the evolution of Search
– Task orientation
– Long-running queries
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Copyright 2009 by U.M. Fayyad, All rights reserved www.open-insights.com
Experience summary at Yahoo!
• Dealing with the largest data source in the world (25 Terabyte
per day)
• Search is in its early incarnations still... where will it evolve to?
We don’t know
– Verticalization trend
– Solving tasks not retrieving pages
– Back to the future: editorial input and directory V2.0?
– Objects and entity extraction
• We cannot separate Search from Search Advertising
– Think of the two search engines as intertwined and
inseparable
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Copyright 2009 by U.M. Fayyad, All rights reserved www.open-insights.com
Lessons Summarized• Evolution from Search and Analytics to Consumer relevance
and advertising
• On-line marketing is ripe with opportunities for predictive
analytics applications
• Results are very powerful and we lack a true understanding
• New generation using social media data and more advanced
predictive analytics are very promising
• Combining context analysis (including text analytics) with user
modeling/targeting leads to some very powerful advances in
relevance
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