bsc 3362 - big data and social analytics - iod conference (ibm)
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#ibmiod#ibmiod
Revolutionizing How Business Understands Customers -- Big Data Meets Social Analytics
Session Number BSC-3362
Aya Soffer | Director, Information Management & Analytics Research | IBMMark Heid | Program Director, Social Analytics | IBM
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Please noteIBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion. Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion.
Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.
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Agenda
12
3
Our Perspective on Big Data Analytics
A Look at Big Data Social Analytics• Multi-channel Marketing• Customer Care and Insight• End-to-End Demo
IBM Research: Driving the Revolution in Big Data Social Analytics
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Variety
Volume Velocity
Veracity
of Tweets create daily
12 terabytes
trade eventsper second
5 million
Of video feeds from surveillance cameras
100’s
We’ve Moved into a New Era of Computing
“We have for the first time an economy based on a key resource [Information] that is not only renewable, but self-generating.
Running out of it is not a problem, but drowning in it is.”
– John Naisbitt
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Challenges of Big Data – The New Mix of Information
Enterprise Data Machine Data Social Data
• Volume
• Structured
• Throughput
• Velocity
• Semi-structured
• Ingestion
• Variability
• Highly unstructured
• Veracity
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Compute Intensive
Storage Intensive
• Fraud Detection• Smart Grids and Smarter Utilities
• Risk Management and Modeling• Asset Management and Optimization • Call Detail Records• Call Center Transcripts• Log Analytics
• 360°View of the Customer• Data Warehouse Evolution
Gain more complete answers to business decisions to make better decisions faster
Ask new questions about their business to uncover new value or realize cost-savings
Explore and experiment to find new opportunities and create new business models
Typical Client Use Cases with New Types of Analytics
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IBM Big Data – Analytics and Platform
• Addresses 4Vs of information
• Harnesses the next wave of analytics that exploits value from a rich information mix
• Fosters a new era in analytical applications
Visualize and Experiment
Predict
Integrate and Govern
HadoopSystem
Stream Computing
DataWarehouse
Analyze Real-time
Search and Discover
IBM Big Data –Analytics and Platform
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Pre-processing • Ingest and analyze unstructured data types
and convert to structured data
Combine structured and unstructured analysis • Augment data warehouse with additional external
sources, such as social media
Combine high velocity and historical analysis • Analyze and react to data in motion; adjust models
with deep historical analysis
Reuse structured data for exploratory analysis
• Experimentation and ad-hoc analysis with structured data
Visualize and Experiment
Predict
Integrate and Govern
HadoopSystem
Stream Computing
DataWarehouse
Analyze Real-time
Search and Discover
IBM Big Data - Analytics and Platform
Most Client Use Cases Combine Multiple Technologies
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The intersection of social media and big data
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Agenda
12
3
Our Perspective on Big Data Analytics
A Look at Big Data Social Analytics• Multi-channel Marketing• Customer Care and Insight• End-to-End Demo
IBM Research: Driving the Revolution in Big Data Social Analytics
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Source: Q4 2010, Unica’s Global Survey of Marketers
About half of marketers admit that their social
media marketing efforts are totally siloed
Measurement and ROI are elusive
Campaigns are poorly integrated
Only brand / mass marketing techniques are employed
Opportunity to engage individuals is ignored
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Even though social media is pervasive, using it successfully in marketing campaigns today is hit or miss
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By linking together social and customer data, we can help our clients market more effectively across multiple channels
Planning, coordinating and executing marketing campaigns to stimulate demand – it’s a process that includes social media
Create relevant
messages
Deliver targeted messages and offers
Optimize email, display and search ad programs
Insights from social media
and other data sources
Capture & analyze responses and
refine
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Introducing: Multi-channel campaign management with integrated social analytics
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An integrated approach which allows organizations to measure, adjust and, ultimately, use social media data to gain greater precision for their campaigns.
How can I leverage social analytics to optimize return on my campaigns?
How can I maximize the value of our social insights
for marketing?S oc ia l Media
Ana lys t
Marketing Mana ger
• Measure the social impact of campaigns through earned and owned media
• Gain greater campaign precision by applying predictive models to socially-derived segments
• Evolve and align marketing and social campaigns through a centralized workspace
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Big Data Social Analytics inSocial Business & Smarter Commerce
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“Benjamin's Grocery” - Winning with Social Analytics & Smarter CommerceHow does it work?
Customer Website Behavior• Clicks• Searches• ViewsPrevious Campaign Data• Contact history• Response/purchases• Test campaigns Modeling Scoring Campaigns
Rank best offers
Social Media• Tweets• Blogs• ForumsCommunities• Surveys• Advocate dialog• Discussions
Derive ideas, insights and actions from Social Media
Execute the campaign using Individual Data for consumers who opted-in
Multi-Channel Marketing
AnalyticsSentiment dashboard
Emerging Topics Affinities Conversations you asked
about and those you didn'tWhat is correlated with what?
Pulling consumers from where the conversation is on the web, match them to segments based on
their actions on Benjamin's website
1
2
3
Perceptual Map Spatial alignment of attributes
Predict who is likely to respond
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“Benjamin's Grocery” - Winning with Social Analytics & Smarter CommerceWhat is the storyline?
Introducing Benjamins Grocery Stores Competition in the grocery business can be intense and Benjamins faces their fair share with Jurassic, a low-price chain with broad presence in the market.
The Market Event On January 20th, 2012, Jurassic announces the end of ad hoc campaigns and the beginning of “every-day low prices”. They drop prices by 12-15% for 3000 products.
Benjamins' Research Knowing that they can't profitably copy Jurassic's price strategy, Benjamins mobilizes a team of experts to search for a better response. They discover that customers have a core un-met need for “healthy, interesting meals at a fair price”.
Benjamins' Response The Benjamins team rapidly tests a creative plan to hire well-known chefs to sponsor new recipes that use Benjamins store brand products. Their communities-of-interest like it – particularly “Moms”, “Singles” and “Gourmets”. They kick-off a new 1:1 cross-channel campaign that lasts through the rest of Q1.
The Results Over the two-month campaign, Benjamins gains market share and grows profit by 8%.
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“Benjamin's Grocery” - Winning with Social Analytics & Smarter CommerceWhat products are used?
Customer Website Behavior• Clicks• Searches• ViewsPrevious Campaign Data• Contact history• Response/purchases• Test campaigns Modeling Scoring Campaigns
Rank best offers
Social Media• Tweets• Blogs• ForumsCommunities• Surveys• Advocate dialog• Discussions
Derive ideas, insights and actions from Social
Media
Execute the campaign using Individual Data for consumers who
opted-in
Multi-Channel Marketing
AnalyticsSentiment dashboard
Emerging Topics
Affinities Conversations you asked
about and those you didn'tWhat is correlated with what?
Pulling consumers from where the conversation is on the web, match them to segments based on
their actions on Benjamin's website
1
2
3
Perceptual Map Spatial alignment of attributes
Predict who is likely to respond
How can Benjamin's quickly understand their differentiators and competitor vulnerabilities?
Where can all of the relevant information be brought together for productive decision-making?
What optimization can be applied to campaign parameters?
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What can they use to do root cause analysis and uncover un-met needs among their target customers?
How can Benjamin's pivot from aggregate to individual data?
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“Benjamin's Grocery” - Winning with Social Analytics & Smarter CommerceWhat products are used?
Customer Website Behavior• Clicks• Searches• ViewsPrevious Campaign Data• Contact history• Response/purchases• Test campaigns Modeling Scoring Campaigns
Rank best offers
Social Media• Tweets• Blogs• ForumsCommunities• Surveys• Advocate dialog• Discussions
Derive ideas, insights and actions from Social
Media
Execute the campaign using Individual Data for consumers who
opted-in
Multi-Channel Marketing
AnalyticsSentiment dashboard
Emerging Topics
Affinities Conversations you asked
about and those you didn'tWhat is correlated with what?
Pulling consumers from where the conversation is on the web, match them to segments based on
their actions on Benjamin's website
1
2
3
Perceptual Map Spatial alignment of attributes
Predict who is likely to respond
● Cognos Consumer Insight 1.1● SPSS Modeler 15.0● Cognos 10.1● Connections 4.0
● Coremetrics Web Analytics ● Cognos Consumer Insight 1.1
● Unica Campaign● SPSS Modeler 15.0● Cognos Consumer Insight
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© 2011 IBM Corporation19
Speakers• Leslie Ament, Vice President, Research & Client Advisory, Hypatia Research Group, • Mark Heid, Program Director, Social Analytics, IBM
Join IBM & Hypatia Research Group for insightful November 6th WebcastSocial Analytics & Intelligence: Converting Contextual to Actionable Insights Creating social intelligence by mining social media networks is no longer the sole purview of elite decision scientists or statisticians. Social analytics is increasingly integrated into work-flows and processes driven every day by business users.
This webinar will review the recent findings from Hypatia Research Group’s benchmark study, Social Analytics & Intelligence: Converting Contextual to Actionable Insights, and demonstrate how business users and analysts collaborate to transform a multitude of online contextual sources into insight, predict optimal next best actions and outcomes and act upon this consumer insight for business gain.
November 6th, 1:00-2:00 pm EThttp://events.unisfair.com/rt/IBM~SocialAnalytics
Converting Contextual to Actionable InsightsNovember 6th, 1:00-2:00 pm EThttp://events.unisfair.com/rt/IBM~SocialAnalytics
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Business Analytics and Big Data Platform Integration
Business Analytics
InfoSphere BigInsights
Hadoop (Map-reduce)
File system (GPFS, HDFS)
Hive HBase
Cognos Insight
Cognos BI
Export and Explore
Unstructured Analysis
Reporting / Analysis Dashboards
CCI
Load through UDFs
SPSS Predictive
Cognos RTM
Real-time Analytics
Predictive
BigSheets BigIndex
InfoSphere Streams
IBM Confidential: References to potential future products are subject to the Important Disclaimer provided earlier in the presentation
Data Warehouse
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Agenda
12
3
Our Perspective on Big Data Analytics
A Look at Big Data Social Analytics• Multi-channel Marketing• Customer Care and Insight• End-to-End Demo
IBM Research: Driving the Revolution in Big Data Social Analytics
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Social Analytics in IBM Research - moving up the value stack to extract actionable insight
Filtering social media is challenging and critical
Summarization is critical in diffuse content streams)Relevance Filtering Topic Modeling
Information Summarization
Sentiment Lexical Pattern ExtractionNeeds to be multi-lingual and tuned to specific domains
Detecting intent to buy or intent to act or mood or brand attributes
Lexical Extraction
Influence Community DetectionInfluence is critical component for social media filtering and Enterprise expertise
Discover hidden pockets of expertise in an enterprise setting
Influence and Communities
Customer Modeling Situational ContextExtract customer demographic features that can be joined with legacy attributes
Context (eg location) is key differentiator in an increasing number of applications
User Modeling
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Social Pulse – What are employees saying about their company’s brand
Social Pulse
• A Social Analytics Solution for marketing and communications professionals
• Focuses on internal versus external consumer perception of your brands and products
• Based on the idea of your workforce being brand ambassadors
• Experimenting within IBM• Externally
>25,000 employees on Twitter, >300,000 on LinkedIn, and > 198,000 on Facebook
• And Internally > 300,000 IBMers use IBM Connections Communities, Blogs, Wikis, Profiles, Forums etc.
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The UsersWhat brand
related topics are IBMers talking
about this week?Is everyone on board with our new
Smarter Planet strategy?
Which business units get the
message, which ones are still struggling?
Are our management teams helping our brands to be presented in
the best light?
Social PulseSocial Pulse
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View Topics and Sentiment of your Workforce by Country
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By Business Unit & Common Topics Across Business Units
Search for brand specific topics
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Not All Business Units are Positive
Let’s see if there aredifferences across countries
Within S&D
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S&D Ireland Very Positive, Opening New Technology Center, Ireland Research (= new
Technology Center) is reserved.
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Brandy – Associating brand perceptions with customer traits
Mining of customer traits• Demographics• Personality• Fundamental needs• Preferences• …
• Integrating mined information with existing customer data
• Associating brand perceptions with customer traits especially their “needs map”
[Ford, 2005][Ford, 2005]
Brandy
inventive/curious vs.
consistent/cautious
friendly/compassion
ate vs. cold/unkind
outgoing/energetic vs. solitary/reserved
sensitive/nervo
us vs.
secure/confident
effic
ient
/org
aniz
ed
vs. e
asy-
goin
g/ca
rele
ss
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Example: Modeling and Deriving Personality
0% 20% 40% 60% 80%
Openness
Conscientiousness
Extraversion
Agreeableness
Neuroticism
[Tausczik&Pennebaker 2010, Yarkoni 2010] [Tausczik&Pennebaker 2010, Yarkoni 2010]
Map the use of words, frequency, & correlation with Big5 based on LIWC
“Agreeableness”wonderful (0.28), together (0.26) …porn (-0.25), cost (-0.23)
Brandy
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All Brands
Retailer 1
Retailer 2
Retailer 3
Openness – Liberalism Conscientious - Cautiousness
Example comparing 3 Retailers Brandy
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Campaign management: a Retail Example
Openness: 83% Idealist: 62%Interest: Dining50% close ties: openness 75%
Openness: 83% Idealist: 62%Interest: Dining50% close ties: openness 75%
Openness: 23% Realist: 87%
Interest: Travel35% close ties: interested in travel
Openness: 23% Realist: 87%
Interest: Travel35% close ties: interested in travel
Help Retailer identify customer segments to launch “CoolBrand” collectionHelp Retailer identify customer segments to launch “CoolBrand” collection
… Want your luggage to stand out at the airport? Never need to dust it? Here comes “CoolBrand” collection…
Save 5% by sharing this with your 5 (travel-loving) friends such as…
… Want your luggage to stand out at the airport? Never need to dust it? Here comes “CoolBrand” collection…
Save 5% by sharing this with your 5 (travel-loving) friends such as…
… experience fine dining at home in Italian fashion style: “CoolBrand” dinnerware…
Save 5% by sharing this with your 5 (open-minded) friends such as …
… experience fine dining at home in Italian fashion style: “CoolBrand” dinnerware…
Save 5% by sharing this with your 5 (open-minded) friends such as …
Brandy
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A Smarter Cities Example
Conscientiousness: 23% Realist: 92%Interest: Foodies50% close ties: Conscientiousness 25%
Conscientiousness: 23% Realist: 92%Interest: Foodies50% close ties: Conscientiousness 25%
Neuroticism: 53% Idealist: 71%
Interest: Travel35% close ties: interested in travel
Neuroticism: 53% Idealist: 71%
Interest: Travel35% close ties: interested in travel
Help DMV identify suitable segments for different campaignsHelp DMV identify suitable segments for different campaigns
… Your current insurance policy is up for renewal …
Share this with your 5 (travel-loving) friends such as… and ask them to follow us to receive reminders…
… Your current insurance policy is up for renewal …
Share this with your 5 (travel-loving) friends such as… and ask them to follow us to receive reminders…
… Holiday is around the corner …Here are holiday safe driving tips: http://dmv.ca.gov/...
share this with your close friends such as …
… Holiday is around the corner …Here are holiday safe driving tips: http://dmv.ca.gov/...
share this with your close friends such as …
Brandy
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Emergencies, call for help
Limited coverage
Analytics and fusion
in near-real-time
• Event / fact summarizations
• KPIs
sensorssensors
Crowdsource(voice& text)
Socialmedia
COPS – Crowdsource Oriented Public Safety
Automatic detection of Public Safety incidents and KPIs, from crowdsourcing data, which is incomplete, inaccurate and noisy
Use innovative “fusion analytics” to reliably detect incidents and trends from uncertain data, textual, spoken and numerical
COPS
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Sample Use Case (Managing Natural Disasters)
Event 1 – 10:10 river water surging (from accumulation of tweets)
Event 2 – 11:15 fast moving water (from accumulation of
mobile messages)Event 3 – 11:15 – flood, major
road blocked (from accumulation of tweets and mobile messages)
Event 4 – 12:30 – flood (from accumulation of tweets and
mobile messages)Event 5 – 12:30 – traffic
accident (from accumulation of mobile messages)
COPS
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COPSSystem automatically aggregates and filters the data
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Event 1 – 10:10 river water surging (from accumulation of tweets)
Event 2 – 11:15 fast moving water (from accumulation of
mobile messages)Event 3 – 11:15 – flood, major
road blocked (from accumulation of tweets and mobile messages)
Event 4 – 12:30 – flood (from accumulation of tweets and
mobile messages)Event 5 – 12:30 – traffic
accident (from accumulation of mobile messages)
Crowd-source events that are progressive – updated as more crowd-source data becomes available
Crowd-source events that display the inherent uncertainty (confidence) – from the event description to the location
Crowd-source events that reflect aggregated data – to avoid overloading by large volume of crowd-source data
and to reduce uncertainty by fusing multiple posts
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Main Module - Event Profile Generation
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Event Event representationrepresentation
Join/FuseJoin/Fuse
/Aggregate/Aggregate
Entity/ Entity/ Event Event ExtractionExtraction
Unstructured data sources
BigInsights /BigData Platform
(3) Automatic Model Generation from entity schema to Event model on BigInsights
Event Schema
(2) Extraction/Integration Flow from unstructured data (tweets and crowd data) to JSON objects
FiltersFilters
Fuse & Fuse & AggregateAggregate
Data Data ingestingest
Streams / BigData Platform
(1) Data Ingestion filter relevant information from millions of messages
Event Detection
(4) Event Detection Statistical detection & model-based detection
(5) Reporting/Alerting/Dashboarding
Events, event summaries, trends, KPIs, Predictions
Statistical Statistical patternspatterns
COPS
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Microcosm - uncover the commercial potential of local microcosms
• Understand the marketing potential of particular locations beyond the individual level
• Understand the potential of viral marketing• Identify promising community types and target marketing to them• Lower marketing costs by targeting earned media
Microcosm
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Social Analytics to extract communities and Locations
Extended communityof people that talk about some subject
Identifying participants locationbased on profiles and discussions
Microcosm
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Geographical Analytics – How it works
• GPS Geotagging (<5% of tweets)• Even if explicit in profile – disambiguation might be needed:
• E.g., “Springfield” by itself can refer to 30 different cities in the USA.• Techniques used
• Rule-basedE.g., “I live in ..”, “lets meet at ..”
• Machine learning (supervised):Statistical methods- find the most characteristic terms of people that report they live in some location x. E.g., “The Strip”, “Bellagio fountains”, “Freemont St.”…-> Las Vegas
• Based on Social Network, • i.e. learn location of people
based on the locations of their friends
Location 1 Location 2 Location 3
Microcosm
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Community Analytics - How it works:
How we build the communities:• Build social graph based on the data flow in the social media. For
example, in Twitter, using the @Reply tag.• Extend the connections with friends, followers, following, etc.• Then use clustering-based approach
What we gain from the communities analysis?• which features have commercial significance• which features can be acted upon
Microcosm
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