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BSC 3362 - Big Data and Social Analytics - IOD Conference (IBM)

The document outlines IBM's approach to leveraging big data and social analytics to improve business understanding of customer behavior. It emphasizes the integration of various data sources for more effective marketing and campaign management, including case studies like 'Benjamin's Grocery.' Additionally, it discusses the potential of social media data to enhance decision-making and provides an overview of IBM's analytics tools and methodologies.

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Revolutionizing How Business Understands Customers -- Big Data Meets Social Analytics Session Number BSC-3362 Aya Soffer | Director, Information Management & Analytics Research | IBM Mark Heid | Program Director, Social Analytics | IBM #ibmiod #ibmiod
Please note IBM’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. #ibmiod
Agenda 1 Our Perspective on Big Data Analytics 2 A Look at Big Data Social Analytics • Multi-channel Marketing • Customer Care and Insight • End-to-End Demo 3 IBM Research: Driving the Revolution in Big Data Social Analytics 3 #ibmiod
We’ve Moved into a New Era of Computing 12 terabytes 5 million of Tweets trade events create daily per second “We have for the first time an economy based on a key resource Volume Velocity [Information] that is not only renewable, but self- generating. Variety Running out of it is not a Veracity 100’s problem, but drowning in it is.” Of video feeds from surveillance cameras – John Naisbitt 4 #ibmiod
Challenges of Big Data – The New Mix of Information Enterprise Data Machine Data Social Data • Volume • Velocity • Variability • Structured • Semi-structured • Highly unstructured • Throughput • Ingestion • Veracity 5 #ibmiod
Typical Client Use Cases with New Types of Analytics Compute Intensive Gain more complete • Fraud Detection answers to business • Smart Grids and Smarter Utilities decisions to make better decisions faster • Risk Management and Modeling Ask new questions • Asset Management and Optimization about their business to • Call Detail Records uncover new value or • Call Center Transcripts realize cost-savings • Log Analytics Explore and • 360°View of the Customer experiment to find • Data Warehouse Evolution new opportunities and Storage create new business Intensive models 6 #ibmiod
IBM Big Data – Analytics and Platform IBM Big Data – Analytics and Platform • Addresses 4Vs of information Visualize and Experiment Predict Analyze Real-time • Harnesses the next wave of analytics that exploits value from a rich information mix Search and Discover Hadoop Stream Data • Fosters a new era in analytical System Computing Warehouse applications Integrate and Govern 7 #ibmiod
Most Client Use Cases Combine Multiple Technologies Pre-processing • Ingest and analyze unstructured data types and convert to structured data IBM Big Data - Combine structured and unstructured analysis Analytics and Platform Visualize and Experiment • Augment data warehouse with additional external Predict Analyze Real-time sources, such as social media Search and Discover Hadoop System Stream Computing Data Warehouse Combine high velocity and historical analysis • Analyze and react to data in motion; adjust models Integrate and Govern with deep historical analysis Reuse structured data for exploratory analysis • Experimentation and ad-hoc analysis with structured data 8 #ibmiod
The intersection of social media and big data 9 #ibmiod
Agenda 1 Our Perspective on Big Data Analytics 2 A Look at Big Data Social Analytics • Multi-channel Marketing • Customer Care and Insight • End-to-End Demo 3 IBM Research: Driving the Revolution in Big Data Social Analytics 10 #ibmiod
Even though social media is pervasive, using it successfully in marketing campaigns today is hit or miss  Measurement and ROI are elusive  Campaigns are poorly About half of marketers integrated admit that their social  Only brand / mass marketing media marketing efforts techniques are employed  Opportunity to engage are totally siloed individuals is ignored Source: Q4 2010, Unica’s Global Survey of Marketers 1111 #ibmiod
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 Insights from Create Optimize email, display Deliver targeted social media relevant and search ad programs messages and offers and other messages data sources Capture & analyze responses and refine 1212 #ibmiod
Introducing: Multi-channel campaign management with integrated social analytics 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 • Measure the social impact social analytics to optimize of campaigns through return on my campaigns? earned and owned media Ma rke ting • Gain greater campaign Ma na ge r precision by applying predictive models to socially-derived segments How can I maximize the • Evolve and align value of our social insights marketing and social for marketing? campaigns through a S oc ia l Me dia centralized workspace Ana lys t 13 13 #ibmiod
Big Data Social Analytics in Social Business & Smarter Commerce 14 #ibmiod
“Benjamin's Grocery” - Winning with Social Analytics & Smarter Commerce How does it work? Analytics Emerging Topics Affinities Conversations you asked What is correlated with what? Sentiment dashboard about and those you didn't Perceptual Map Social Media Spatial alignment of attributes • Tweets • Blogs • Forums Communities 1 Derive ideas, insights and • Surveys • Advocate dialog • Discussions actions from Social Media 2 Pulling consumers from where the conversation is on the web, match them to segments based on their actions on Benjamin's website Customer 3 Execute the campaign using Individual Data for consumers who opted-in Website Behavior • Clicks • Searches Previous • Views Campaign Data • Contact history • Response/purchases • Test campaigns Modeling Scoring Campaigns Predict who is likely to Rank best offers Multi-Channel Marketing 15 respond #ibmiod
“Benjamin's Grocery” - Winning with Social Analytics & Smarter Commerce What 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%. 16 #ibmiod
“Benjamin's Grocery” - Winning with Social Analytics & Smarter Commerce What products are used? Analytics Emerging Affinities Where can all ofSentiment dashboard the Conversations you asked Topics What is correlated with How can Benjamin's quickly about and those you didn't what? relevant information be understand their differentiatorsPerceptual Map and Social Media brought together for competitor vulnerabilities? Spatial alignment of • Tweets • Blogs productive decision- attributes • Forums making? What can they use to do root cause Communities analysis and uncover un-met needs 1 Derive ideas, insights • Surveys • Advocate dialog among their target customers? • Discussions and actions from Social Media 2 Pulling can Benjamin's pivot from conversation is How consumers from where the aggregate to individual data? on the web, match them to segments based on their actions on Benjamin's website 3 What optimization can beusing Execute the campaign applied Customer to campaign parameters? Individual Data for consumers who Website Behavior opted-in • Clicks • Searches Previous • Views Campaign Data • Contact history • Response/purchases • Test campaigns Modeling Scoring Campaigns Predict who is likely to Rank best offers Multi-Channel Marketing 17 respond #ibmiod
“Benjamin's Grocery” - Winning with Social Analytics & Smarter Commerce What products are used? Analytics Emerging Affinities Conversations you asked Topics What is correlated with Sentiment dashboard about and those you didn't what? Perceptual Map Social Media Spatial alignment of • Tweets attributes • Blogs • Forums Communities Cognos Consumer Insight 1.1 ● 1 Derive ideas, insights ● SPSS Modeler 15.0 • Surveys • Advocate dialog • Discussions and actions 10.1 Social ● Cognos from Media ● Connections 4.0 2 Pulling consumers fromAnalytics conversation is ● Coremetrics Web where the ● on the web, match them to segments based on Cognos Consumer Insight 1.1 their actions on Benjamin's website ● Unica Campaign Customer 3 Execute the campaign using ● SPSS Modeler 15.0 Individual Data for consumers who ● Cognos Consumer Insight Website Behavior opted-in • Clicks • Searches Previous • Views Campaign Data • Contact history • Response/purchases • Test campaigns Modeling Scoring Campaigns Predict who is likely to Rank best offers Multi-Channel Marketing 18 respond #ibmiod
Converting Contextual to Actionable Insights November 6th, 1:00-2:00 pm ET http://events.unisfair.com/rt/IBM~SocialAnalytics Join IBM & Hypatia Research Group for insightful November 6th Webcast Social 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 Speakers users and analysts collaborate to transform a multitude of online contextual sources into insight, • Leslie Ament, best actions and outcomes Client upon this consumer insight Group, predict optimal nextVice President, Research &and actAdvisory, Hypatia Research for business gain. • Mark Heid, Program Director, Social Analytics, IBM November 6th, 1:00-2:00 pm ET 19 © 2011 IBM Corporation http://events.unisfair.com/rt/IBM~SocialAnalytics
Business Analytics and Big Data Platform Integration Business Analytics SPSS Cognos Cognos Cognos CCI Predictive RTM BI Insight Predictive Real-time Reporting / Analysis Export and Unstructured Analytics Dashboards Explore Analysis InfoSphere BigInsights InfoSphere Data Streams Warehouse BigSheets BigIndex Hive HBase Hadoop (Map-reduce) File system (GPFS, HDFS) Load through UDFs 20 IBM Confidential: References to potential future products are subject to the Important Disclaimer provided earlier in the presentation #ibmiod
Agenda 1 Our Perspective on Big Data Analytics 2 A Look at Big Data Social Analytics • Multi-channel Marketing • Customer Care and Insight • End-to-End Demo 3 IBM Research: Driving the Revolution in Big Data Social Analytics 21 #ibmiod
Social Analytics in IBM Research - moving up the value stack to extract actionable insight Filtering social media is Summarization is critical in challenging and critical Relevance Filtering Topic Modeling diffuse content streams) Information Summarization Needs to be multi-lingual Detecting intent to buy or intent to and tuned to specific Sentiment Lexical Pattern Extraction act or mood or brand attributes domains Lexical Extraction Discover hidden pockets of Influence is critical component for Influence Community Detection expertise in an enterprise setting social media filtering and Enterprise expertise Influence and Communities Extract customer demographic Context (eg location) is key features that can be joined with Customer Modeling Situational Context differentiator in an increasing legacy attributes number of applications 22 User Modeling #ibmiod
Social Pulse Social Pulse – What are employees saying about their company’s brand • 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. 23 #ibmiod
The Users Social Pulse What brand related topics are IBMers talking about this week? everyone on Is 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? 24 #ibmiod 24
View Topics and Sentiment of your Workforce by Country 25
By Business Unit & Common Topics Across Business Units Search for brand specific topics 26
Not All Business Units are Positive Let’s see if there are differences across countries Within S&D 27
S&D Ireland Very Positive, Opening New Technology Center, Ireland Research (= new Technology Center) is reserved. 28
Brandy Brandy – Associating brand perceptions with customer traits Mining of customer traits • Demographics [Ford, 2005] • Personality • Fundamental needs • Preferences •… • Integrating mined inv s. co ent information with existing u sv ns ive vo ist /c er ent en u ri customer data e/n fid t/c ou au s v itiv /con t io s . ns se cure us se • Associating brand frie s. col d ate nize perceptions with customer ndly v vs. e nt/orga ss /com /unkin traits especially their rele asy- d pas d g/ ca “needs map” ie effic sion goin outgoing/energetic vs. 29 solitary/reserved #ibmiod
Brandy Example: Modeling and Deriving Personality 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) Openness Conscientiousness Extraversion Agreeableness Neuroticism 0% 20% 40% 60% 80% [Tausczik&Pennebaker 2010, Yarkoni 30 2010] #ibmiod
Example comparing 3 Retailers Brandy Openness – Liberalism Conscientious - Cautiousness All Brands Retailer 1 Retailer 2 Retailer 3 31 #ibmiod
Campaign management: a Retail Example Brandy Help Retailer identify customer segments to launch “ CoolBrand” collection Openness: 83% Openness: 23% Idealist: 62% Realist: 87% Interest: Dining Interest: Travel 50% close ties: openness 75% 35% close ties: interested in travel … experience fine dining at … Want your luggage to stand out home in Italian fashion style: at the airport? Never need to dust “CoolBrand” dinnerware… it? Here comes “CoolBrand” collection… Save 5% by sharing this with your 5 (open-minded) friends Save 5% by sharing this with your 5 such as … (travel-loving) friends such as… 32 #ibmiod
A Smarter Cities Example Brandy Help DMV identify suitable segments for different campaigns Conscientiousness: 23% Neuroticism: 53% Realist: 92% Idealist: 71% Interest: Foodies Interest: Travel 50% close ties: Conscientiousness 25% 35% close ties: interested in travel … Holiday is around the corner … Your current insurance policy … is up for renewal … Here are holiday safe driving tips: http://dmv.ca.gov/... Share this with your 5 (travel- loving) friends such as… and ask share this with your close friends them to follow us to receive 33 such as … reminders… #ibmiod
COPS COPS – Crowdsource Oriented Public Safety  Automatic detection of Public Safety incidents and KPIs, from crowdsourcing data, which is incomplete, inaccurate and noisy Emergencies, Limited  call for help coverage Use innovative “fusion analytics” to reliably detect incidents and trends from uncertain data, textual, spoken and numerical Analytics • Event / fact Crowd and fusion source summarizations (voice in near- • KPIs & text) real-time Social media sensors 34 #ibmiod
COPS 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 Event 3 – 11:15 – flood, major mobile messages) road blocked (from accumulation of tweets and mobile messages) Event 4 – 12:30 – flood (from Event 5 – 12:30 – traffic accumulation of tweets and accident (from accumulation mobile messages) of mobile messages) 35 #ibmiod
COPS System automatically aggregates and filters the data Crowd-source events that reflect aggregated data – to avoid overloading Event 1 – 10:10 river of crowd-source data by large volume water surging and to reduce uncertainty by fusing tweets) posts (from accumulation of multiple Crowd-source events that are progressive – updated as Event 2 – 11:15 fast crowd-source data becomes available more moving water (from accumulation of Event 3 – 11:15 – flood, major mobile messages) road blocked (from accumulation of tweets and mobile messages) Crowd-source events that display the inherent uncertainty (confidence) – from the event4description to(from location Event – 12:30 – flood the Event 5 – 12:30 – traffic accumulation of tweets and accident (from accumulation mobile messages) of mobile messages) 36 36 #ibmiod
COPS Main Module - Event Profile Generation (1) Data Ingestion filter (4) Event Detection relevant information from Statistical detection & millions of messages model-based detection Filters Data Statistical (5) patterns Reporting/Alerting/D ingest ashboarding Fuse & Event Detection Unstructured Aggregate data sources Streams / BigData Platform Event Events, event Entity/ representation summaries, trends, Event Extraction KPIs, Predictions Join/Fuse /Aggregate BigInsights /BigData Platform Event Schema (2) Extraction/Integration (3) Automatic Model Flow from Generation from unstructured data entity schema to (tweets and crowd Event model on data) to JSON objects BigInsights 3737 #ibmiod
Microcosm 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 38 #ibmiod
Microcosm Social Analytics to extract communities and Locations Extended community Identifying participants location of people that talk about based on profiles and discussions some subject 39 #ibmiod
Microcosm 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-based E.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 40 #ibmiod
Microcosm 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 41 #ibmiod
Thank You! Your Feedback is Important! • Access SmartSite to complete your session surveys o Any web or mobile browser at iodsmartsite.com o Any SmartSite kiosk onsite o Each completed session survey increases your chance to win an Apple TV with daily drawing sponsored by Alliance Tech #ibmiod
43 #ibmiod

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BSC 3362 - Big Data and Social Analytics - IOD Conference (IBM)

  • 1.
    Revolutionizing How Business UnderstandsCustomers -- Big Data Meets Social Analytics Session Number BSC-3362 Aya Soffer | Director, Information Management & Analytics Research | IBM Mark Heid | Program Director, Social Analytics | IBM #ibmiod #ibmiod
  • 2.
    Please note IBM’s statementsregarding 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. #ibmiod
  • 3.
    Agenda 1 Our Perspective on Big Data Analytics 2 A Look at Big Data Social Analytics • Multi-channel Marketing • Customer Care and Insight • End-to-End Demo 3 IBM Research: Driving the Revolution in Big Data Social Analytics 3 #ibmiod
  • 4.
    We’ve Moved intoa New Era of Computing 12 terabytes 5 million of Tweets trade events create daily per second “We have for the first time an economy based on a key resource Volume Velocity [Information] that is not only renewable, but self- generating. Variety Running out of it is not a Veracity 100’s problem, but drowning in it is.” Of video feeds from surveillance cameras – John Naisbitt 4 #ibmiod
  • 5.
    Challenges of BigData – The New Mix of Information Enterprise Data Machine Data Social Data • Volume • Velocity • Variability • Structured • Semi-structured • Highly unstructured • Throughput • Ingestion • Veracity 5 #ibmiod
  • 6.
    Typical Client UseCases with New Types of Analytics Compute Intensive Gain more complete • Fraud Detection answers to business • Smart Grids and Smarter Utilities decisions to make better decisions faster • Risk Management and Modeling Ask new questions • Asset Management and Optimization about their business to • Call Detail Records uncover new value or • Call Center Transcripts realize cost-savings • Log Analytics Explore and • 360°View of the Customer experiment to find • Data Warehouse Evolution new opportunities and Storage create new business Intensive models 6 #ibmiod
  • 7.
    IBM Big Data– Analytics and Platform IBM Big Data – Analytics and Platform • Addresses 4Vs of information Visualize and Experiment Predict Analyze Real-time • Harnesses the next wave of analytics that exploits value from a rich information mix Search and Discover Hadoop Stream Data • Fosters a new era in analytical System Computing Warehouse applications Integrate and Govern 7 #ibmiod
  • 8.
    Most Client UseCases Combine Multiple Technologies Pre-processing • Ingest and analyze unstructured data types and convert to structured data IBM Big Data - Combine structured and unstructured analysis Analytics and Platform Visualize and Experiment • Augment data warehouse with additional external Predict Analyze Real-time sources, such as social media Search and Discover Hadoop System Stream Computing Data Warehouse Combine high velocity and historical analysis • Analyze and react to data in motion; adjust models Integrate and Govern with deep historical analysis Reuse structured data for exploratory analysis • Experimentation and ad-hoc analysis with structured data 8 #ibmiod
  • 9.
    The intersection ofsocial media and big data 9 #ibmiod
  • 10.
    Agenda 1 Our Perspective on Big Data Analytics 2 A Look at Big Data Social Analytics • Multi-channel Marketing • Customer Care and Insight • End-to-End Demo 3 IBM Research: Driving the Revolution in Big Data Social Analytics 10 #ibmiod
  • 11.
    Even though socialmedia is pervasive, using it successfully in marketing campaigns today is hit or miss  Measurement and ROI are elusive  Campaigns are poorly About half of marketers integrated admit that their social  Only brand / mass marketing media marketing efforts techniques are employed  Opportunity to engage are totally siloed individuals is ignored Source: Q4 2010, Unica’s Global Survey of Marketers 1111 #ibmiod
  • 12.
    By linking togethersocial 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 Insights from Create Optimize email, display Deliver targeted social media relevant and search ad programs messages and offers and other messages data sources Capture & analyze responses and refine 1212 #ibmiod
  • 13.
    Introducing: Multi-channel campaignmanagement with integrated social analytics 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 • Measure the social impact social analytics to optimize of campaigns through return on my campaigns? earned and owned media Ma rke ting • Gain greater campaign Ma na ge r precision by applying predictive models to socially-derived segments How can I maximize the • Evolve and align value of our social insights marketing and social for marketing? campaigns through a S oc ia l Me dia centralized workspace Ana lys t 13 13 #ibmiod
  • 14.
    Big Data SocialAnalytics in Social Business & Smarter Commerce 14 #ibmiod
  • 15.
    “Benjamin's Grocery” -Winning with Social Analytics & Smarter Commerce How does it work? Analytics Emerging Topics Affinities Conversations you asked What is correlated with what? Sentiment dashboard about and those you didn't Perceptual Map Social Media Spatial alignment of attributes • Tweets • Blogs • Forums Communities 1 Derive ideas, insights and • Surveys • Advocate dialog • Discussions actions from Social Media 2 Pulling consumers from where the conversation is on the web, match them to segments based on their actions on Benjamin's website Customer 3 Execute the campaign using Individual Data for consumers who opted-in Website Behavior • Clicks • Searches Previous • Views Campaign Data • Contact history • Response/purchases • Test campaigns Modeling Scoring Campaigns Predict who is likely to Rank best offers Multi-Channel Marketing 15 respond #ibmiod
  • 16.
    “Benjamin's Grocery” -Winning with Social Analytics & Smarter Commerce What 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%. 16 #ibmiod
  • 17.
    “Benjamin's Grocery” -Winning with Social Analytics & Smarter Commerce What products are used? Analytics Emerging Affinities Where can all ofSentiment dashboard the Conversations you asked Topics What is correlated with How can Benjamin's quickly about and those you didn't what? relevant information be understand their differentiatorsPerceptual Map and Social Media brought together for competitor vulnerabilities? Spatial alignment of • Tweets • Blogs productive decision- attributes • Forums making? What can they use to do root cause Communities analysis and uncover un-met needs 1 Derive ideas, insights • Surveys • Advocate dialog among their target customers? • Discussions and actions from Social Media 2 Pulling can Benjamin's pivot from conversation is How consumers from where the aggregate to individual data? on the web, match them to segments based on their actions on Benjamin's website 3 What optimization can beusing Execute the campaign applied Customer to campaign parameters? Individual Data for consumers who Website Behavior opted-in • Clicks • Searches Previous • Views Campaign Data • Contact history • Response/purchases • Test campaigns Modeling Scoring Campaigns Predict who is likely to Rank best offers Multi-Channel Marketing 17 respond #ibmiod
  • 18.
    “Benjamin's Grocery” -Winning with Social Analytics & Smarter Commerce What products are used? Analytics Emerging Affinities Conversations you asked Topics What is correlated with Sentiment dashboard about and those you didn't what? Perceptual Map Social Media Spatial alignment of • Tweets attributes • Blogs • Forums Communities Cognos Consumer Insight 1.1 ● 1 Derive ideas, insights ● SPSS Modeler 15.0 • Surveys • Advocate dialog • Discussions and actions 10.1 Social ● Cognos from Media ● Connections 4.0 2 Pulling consumers fromAnalytics conversation is ● Coremetrics Web where the ● on the web, match them to segments based on Cognos Consumer Insight 1.1 their actions on Benjamin's website ● Unica Campaign Customer 3 Execute the campaign using ● SPSS Modeler 15.0 Individual Data for consumers who ● Cognos Consumer Insight Website Behavior opted-in • Clicks • Searches Previous • Views Campaign Data • Contact history • Response/purchases • Test campaigns Modeling Scoring Campaigns Predict who is likely to Rank best offers Multi-Channel Marketing 18 respond #ibmiod
  • 19.
    Converting Contextual toActionable Insights November 6th, 1:00-2:00 pm ET http://events.unisfair.com/rt/IBM~SocialAnalytics Join IBM & Hypatia Research Group for insightful November 6th Webcast Social 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 Speakers users and analysts collaborate to transform a multitude of online contextual sources into insight, • Leslie Ament, best actions and outcomes Client upon this consumer insight Group, predict optimal nextVice President, Research &and actAdvisory, Hypatia Research for business gain. • Mark Heid, Program Director, Social Analytics, IBM November 6th, 1:00-2:00 pm ET 19 © 2011 IBM Corporation http://events.unisfair.com/rt/IBM~SocialAnalytics
  • 20.
    Business Analytics andBig Data Platform Integration Business Analytics SPSS Cognos Cognos Cognos CCI Predictive RTM BI Insight Predictive Real-time Reporting / Analysis Export and Unstructured Analytics Dashboards Explore Analysis InfoSphere BigInsights InfoSphere Data Streams Warehouse BigSheets BigIndex Hive HBase Hadoop (Map-reduce) File system (GPFS, HDFS) Load through UDFs 20 IBM Confidential: References to potential future products are subject to the Important Disclaimer provided earlier in the presentation #ibmiod
  • 21.
    Agenda 1 Our Perspective on Big Data Analytics 2 A Look at Big Data Social Analytics • Multi-channel Marketing • Customer Care and Insight • End-to-End Demo 3 IBM Research: Driving the Revolution in Big Data Social Analytics 21 #ibmiod
  • 22.
    Social Analytics inIBM Research - moving up the value stack to extract actionable insight Filtering social media is Summarization is critical in challenging and critical Relevance Filtering Topic Modeling diffuse content streams) Information Summarization Needs to be multi-lingual Detecting intent to buy or intent to and tuned to specific Sentiment Lexical Pattern Extraction act or mood or brand attributes domains Lexical Extraction Discover hidden pockets of Influence is critical component for Influence Community Detection expertise in an enterprise setting social media filtering and Enterprise expertise Influence and Communities Extract customer demographic Context (eg location) is key features that can be joined with Customer Modeling Situational Context differentiator in an increasing legacy attributes number of applications 22 User Modeling #ibmiod
  • 23.
    Social Pulse SocialPulse – What are employees saying about their company’s brand • 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. 23 #ibmiod
  • 24.
    The Users Social Pulse What brand related topics are IBMers talking about this week? everyone on Is 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? 24 #ibmiod 24
  • 25.
    View Topics andSentiment of your Workforce by Country 25
  • 26.
    By Business Unit& Common Topics Across Business Units Search for brand specific topics 26
  • 27.
    Not All BusinessUnits are Positive Let’s see if there are differences across countries Within S&D 27
  • 28.
    S&D Ireland VeryPositive, Opening New Technology Center, Ireland Research (= new Technology Center) is reserved. 28
  • 29.
    Brandy Brandy –Associating brand perceptions with customer traits Mining of customer traits • Demographics [Ford, 2005] • Personality • Fundamental needs • Preferences •… • Integrating mined inv s. co ent information with existing u sv ns ive vo ist /c er ent en u ri customer data e/n fid t/c ou au s v itiv /con t io s . ns se cure us se • Associating brand frie s. col d ate nize perceptions with customer ndly v vs. e nt/orga ss /com /unkin traits especially their rele asy- d pas d g/ ca “needs map” ie effic sion goin outgoing/energetic vs. 29 solitary/reserved #ibmiod
  • 30.
    Brandy Example: Modelingand Deriving Personality 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) Openness Conscientiousness Extraversion Agreeableness Neuroticism 0% 20% 40% 60% 80% [Tausczik&Pennebaker 2010, Yarkoni 30 2010] #ibmiod
  • 31.
    Example comparing 3Retailers Brandy Openness – Liberalism Conscientious - Cautiousness All Brands Retailer 1 Retailer 2 Retailer 3 31 #ibmiod
  • 32.
    Campaign management: aRetail Example Brandy Help Retailer identify customer segments to launch “ CoolBrand” collection Openness: 83% Openness: 23% Idealist: 62% Realist: 87% Interest: Dining Interest: Travel 50% close ties: openness 75% 35% close ties: interested in travel … experience fine dining at … Want your luggage to stand out home in Italian fashion style: at the airport? Never need to dust “CoolBrand” dinnerware… it? Here comes “CoolBrand” collection… Save 5% by sharing this with your 5 (open-minded) friends Save 5% by sharing this with your 5 such as … (travel-loving) friends such as… 32 #ibmiod
  • 33.
    A Smarter CitiesExample Brandy Help DMV identify suitable segments for different campaigns Conscientiousness: 23% Neuroticism: 53% Realist: 92% Idealist: 71% Interest: Foodies Interest: Travel 50% close ties: Conscientiousness 25% 35% close ties: interested in travel … Holiday is around the corner … Your current insurance policy … is up for renewal … Here are holiday safe driving tips: http://dmv.ca.gov/... Share this with your 5 (travel- loving) friends such as… and ask share this with your close friends them to follow us to receive 33 such as … reminders… #ibmiod
  • 34.
    COPS COPS –Crowdsource Oriented Public Safety  Automatic detection of Public Safety incidents and KPIs, from crowdsourcing data, which is incomplete, inaccurate and noisy Emergencies, Limited  call for help coverage Use innovative “fusion analytics” to reliably detect incidents and trends from uncertain data, textual, spoken and numerical Analytics • Event / fact Crowd and fusion source summarizations (voice in near- • KPIs & text) real-time Social media sensors 34 #ibmiod
  • 35.
    COPS 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 Event 3 – 11:15 – flood, major mobile messages) road blocked (from accumulation of tweets and mobile messages) Event 4 – 12:30 – flood (from Event 5 – 12:30 – traffic accumulation of tweets and accident (from accumulation mobile messages) of mobile messages) 35 #ibmiod
  • 36.
    COPS System automaticallyaggregates and filters the data Crowd-source events that reflect aggregated data – to avoid overloading Event 1 – 10:10 river of crowd-source data by large volume water surging and to reduce uncertainty by fusing tweets) posts (from accumulation of multiple Crowd-source events that are progressive – updated as Event 2 – 11:15 fast crowd-source data becomes available more moving water (from accumulation of Event 3 – 11:15 – flood, major mobile messages) road blocked (from accumulation of tweets and mobile messages) Crowd-source events that display the inherent uncertainty (confidence) – from the event4description to(from location Event – 12:30 – flood the Event 5 – 12:30 – traffic accumulation of tweets and accident (from accumulation mobile messages) of mobile messages) 36 36 #ibmiod
  • 37.
    COPS Main Module- Event Profile Generation (1) Data Ingestion filter (4) Event Detection relevant information from Statistical detection & millions of messages model-based detection Filters Data Statistical (5) patterns Reporting/Alerting/D ingest ashboarding Fuse & Event Detection Unstructured Aggregate data sources Streams / BigData Platform Event Events, event Entity/ representation summaries, trends, Event Extraction KPIs, Predictions Join/Fuse /Aggregate BigInsights /BigData Platform Event Schema (2) Extraction/Integration (3) Automatic Model Flow from Generation from unstructured data entity schema to (tweets and crowd Event model on data) to JSON objects BigInsights 3737 #ibmiod
  • 38.
    Microcosm 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 38 #ibmiod
  • 39.
    Microcosm Social Analyticsto extract communities and Locations Extended community Identifying participants location of people that talk about based on profiles and discussions some subject 39 #ibmiod
  • 40.
    Microcosm 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-based E.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 40 #ibmiod
  • 41.
    Microcosm 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 41 #ibmiod
  • 42.
    Thank You! Your Feedbackis Important! • Access SmartSite to complete your session surveys o Any web or mobile browser at iodsmartsite.com o Any SmartSite kiosk onsite o Each completed session survey increases your chance to win an Apple TV with daily drawing sponsored by Alliance Tech #ibmiod
  • 43.
    43 #ibmiod