2016 XUG Conference Big Data: Big Deal for Personalized Communications or Meh?

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  • XUG Conference Atlanta, GA

    November 14, 2016

  • What is Big Data and why is it important? How is Big Data being used for Marketing? Big Data is a driver of Artificial Intelligence? What is a Graph? Graph Database?

    Accepting questions

    goo.gl/slides/zzjzkj

    http://goo.gl/slides/zzjzkjhttp://goo.gl/slides/zzjzkj

  • ...or is it just \_()_/

    Big Data

    Semantics

    Patterns

    Paths

    Answers

    Insights

    http://emojipedia.org/shrug/

  • Big Data

  • 4 Vs Volume, Variety, Veracity, Velocity

    http://www.ey.com/gl/en/services/advisory/ey-big-data-big-opportunities-big-challenges

    https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwji94Ggvo3QAhWG5oMKHYllCUIQjRwIBw&url=http%3A%2F%2Fwww.ey.com%2Fgl%2Fen%2Fservices%2Fadvisory%2Fey-big-data-big-opportunities-big-challenges&psig=AFQjCNHf0j7wFATs4Ox-yyGYK0BaPFDoIg&ust=1478293406553506

  • Beyond the Hype is Big Data Analytics

    http://www.sciencedirect.com/science/article/pii/S0268401214001066

    Text analyticstechniques that extract information from textual data. Information extraction Text summarization Question answering Sentiment analysis

    Social Media analyticsanalysis of structured and unstructured data from social media channels. Community detection Social influence

    analysis Link prediction

    Predictive analyticstechniques that predict future outcomes based on historical and current data. Regression

    techniques Machine learning

    techniques

  • Analytical Techniques

  • Why Big Data: Big Actionable Insights

    Big Data NoSQL databases like MongoDB, CounchDB, Cassandra, DynamoDB, MarkLogic, and Neo4j.

    Big Data processing tools such as Apache Hadoop, HDFS, HBase, MapReduce , Spark...

    data mining, data modelingpredictive modeling.

  • Big Data often uses a different, simpler, semantic data model

    Data is easily added and similar but different data is relatable

    Powerful tools allow new knowledge to be discovered and explored

  • Semantics

    Semantic data models utilize Graph data structures to link things to properties and to other things (think things not strings).

    With the form Object - RelationType - Object.

    For example:

    http://en.wikipedia.org/wiki/Semantic_data_model

  • Things not Strings

  • _RA name: Zushi Zam_RB name: iSush

    _RA LOCATED IN _P1_RB LOCATED IN _P1

    _P1 location: New York

    Graph Databases

    _0 IS_FRIEND_OF _2_0 IS_FRIEND_OF _1

    _2 LIKES _RA_1 LIKES _RB

    _RB SERVES _C0_RA SERVES _C0_C0 cuisine: Sushi

  • Fuzzy Similarity

    _bday1 birthDate 10-Oct-1799_bday2 birthDate Abt. 1798_bday3 birthDate 09-Oct-1798:

    _person perfBirthDate _bday1_person altBirthDate _bday2_person altBirthDate _bday2

  • Big Data and Linked Data

    Semantic data models basis for Linked Data Open Datasets can extend LD objects Linked Open Data (LOD) repositories offer

    50B+ triples with 10B in DBpedia alone

    http://en.wikipedia.org/wiki/Datahttp://en.wikipedia.org/wiki/Informationhttp://en.wikipedia.org/wiki/Knowledge

  • Big Data -> Artificial Intelligence

    1. Big Data

    2. Cheap parallel computation

    3. Better algorithms

    Fueled by technology advancements (e.g. big data processing power, advanced machine learning, predictive analytics and natural language processing) and by the marketing engines of tech heavyweights, media are latching onto AI as the next big technology trend.

    https://www.wired.com/2014/10/future-of-artificial-intelligence/

    https://www.wired.com/2014/10/future-of-artificial-intelligence/https://www.wired.com/2014/10/future-of-artificial-intelligence/

  • Artificial Intelligence Marketing Race

    AI in common use Search Recommendation Systems Programmatic Advertising Marketing Forecasting Speech / Text Recognition Recommendations Fraud and data breaches Social semantics Website design Product pricing Predictive customer service Ad targeting Speech recognition Language recognition Customer Segmentation Sales forecasting Image recognition Content generation Bots, PAs and messengers

    AI rapidly developing Image recognition Customer Segmentation Content Generation Personalization Personalize Content, Recommendations and Site Experiences Lifetime Value (LTV) Algorithms Whole Journey Optimize Personalized Recommendations A/B/N Testing to Create Unique,

    Optimized Experiences

  • Will AIs want to use Electric Toasters?

    Blade Runner: Do Androids Dream of Electric Sheep?

    AI is the new electricity, he says. Just as 100 years ago electricity transformed industry after industry, AI will now do the same.Why Deep Learning is Suddenly Changing Your Life

    AI is like electricity, and that when it was first incorporated into appliances they were referred to by names such as the electric toaster. Now its just a toaster. Salesforce Einstein Proves that AI is Relative

  • Patterns

    Knowledge Representation

    Pattern recognition Machine Learning

    https://en.wikipedia.org/wiki/Semanticshttps://en.wikipedia.org/wiki/Concept

  • Machine Learning Deep Learning

    Facial recognition Voice analysis Best path analysis

  • Customer Journey Modeling

    Patterns and goals Machine Learning Unsupervised Learning

  • Append Enhance Expand Infer

    AI as a Service IBM IBM AlchemyLanguage IBM Conversation IBM Retrieve and Rank IBM Personality Insights

    AI as a Service Google Prediction API Sentiment Analysis Purchase Prediction Spam Comment Detection

    AI as a Service Microsoft Computer Vision API Emotion API Face API Bing Speech API Linguistic Analysis API Text Analytics API Recommendations API

    AI as a Service Amazon Content Personalization Propensity Modeling Customer Churn Prediction Solution Recommendation Amazon Alexa

  • Personality Propensity?

    Analytics vendors user Personality Profiles for messaging / targeting

    Richer models helped marketers to understand and predict behavior

    Use data that is available in datasets such as Acxiom and Experian

    Leverage digital content such as individual writing example or self-improvement surveys

  • Example: IBM Personality Insights

    You are likely to... be sensitive to ownership cost

    when buying automobiles have spent time volunteering prefer quality when buying clothes

    You are unlikely to... prefer safety when buying

    automobiles volunteer to learn about social

    causes be influenced by brand names

    when making product purchases

  • Matchmaker Matchmaker

    Cluster Targeting Persona Segmentation Journey Triggering Personalization Variations Emotive Predictors

    Conference Attendees Skill Finders Job Postings Volunteer Opportunities Geo Targeting

  • Example: Matching Jobs with Skills

    Recommended Skills Job Opportunity Needs

  • Relational databases cannot easily have new varieties of data added

    Similar but not exact data was difficult to associate, align, understand

    Richer semantic models can generate new understanding, and questions

    New questions generate more data, and knowledge - processes increasing autonomous

  • Answers

    Information Extraction Deep Learning

    Knowledge Bases Pathfinding and Scoring Speech Recognition Natural Language

    Processing Reasoners and Question

    Answers

  • IBM Watson, Come here, I want...

  • So what are the questions?

    How do marketers define successful customer experiences?

    How do customers define successful interactions with

    brands?

    Does everyone want the same things?

    Isnt the best price for the best product good enough?

    So many questions! Q&A conversations led to new

    questions and to new insights about the nature of the

    conversation.

  • Product, Price, Promotion, Place +

  • Dicks Sporting Goods CX

    One-to-one Customized Personalized Emotionalized

  • If Answers are Easy...

    A lesson of big data is that finding answers to those questions is increasingly trivial with AI based machines.

    The challenge is to ask the right questions.

    As we'll see later the right question for personalizing messaging are Who, What and How?

  • Insights

    What is the next best message

    How can information be linked and analyzed to help us understand individuals and how they want to be communicated to individually?

    How do I move from personalized communication to individualized conversations?

  • Customize, Personalize, Emotionalize

    7 Questions with suggestions for ...

    What are the intended outcomes for each step?

    What data can we use as inputs to insight generation?

    What AI / Big Data Tools that can be considered?

  • Next Best Message 7 Questions

    Why are we generating a message or conversation?

    What do we start or continue a conversation about?

    Who are we having a conversation with?

    Where is the best place to send message / have a conversation?

    When is the best time to send the next message?

    With individualized information do we communicate personally?

    How does an individual want to be talked with?

  • Why are we generating a message or conversation?

    Outcome Triggering Conditions

    Input Campaign Map Transaction History Behavioral Event

    Services IBM Conversation Microsoft Bot Framework Google DeepMind Amazon Machine Learning

  • What do we start or continue a conversation about?

    Outcome Campaign Trigger Message Type

    Input Segmentation Cluster Campaign Persona

    Services IBM Retrieve and Rank Microsoft Text Analysis API Google Purchase Prediction Amazon Propensity Modeling

  • Example: Myers-Briggs Type Indicator

    THE ARCHITECT

    INTJ personality types think strategically and see the big picture.

    Have original minds and great drive for implementing their ideas and achieving their goals. Quickly see patterns in external events and develop long-range explanatory perspectives. When committed, organize a job and carry it through. Skeptical and independent, have high standards of competence and performance - for themselves and others.

  • Who are we having a conversation with?

    Output Segmenting Audience

    Input Campaign Recipients Segment Candidates GeoTargeted Customers

    Services IBM AlchemyLanuage Microsoft Linguistic Analysis Google Prediction API Amazon Churn Prediction

  • Example: PersonicX Cluster Perspectives

    Cluster #5: Active & Involved

    Active & Involved households are wealthy empty nesters. At a mean age of 60, they are extremely well educated and still well compensated in professional and managerial white-collar jobs, as well as being active investors. With a third having lived at their residence for 6-14 years, and another third for 15+ years, these homeowners are well established in their communities. They are likely to own a recreation vehicle and enjoy travel to Hawaii and to national parks. Their substantial discretionary time and money are spent on high-quality clothing, dining out, golf and live theater. However, they are also community activists, belonging to charitable, religious and civic organizations.

  • Where is the best place to send message / have a conversation?

    Outcome Channeling Medium

    Input GeoFencing Device Preferences Geography profile

    Services IBM Conversation Microsoft Entity Linking Google Sentiment Analysis Amazon Alexa

  • When is the best time to send the next message?

    Outcome Customizing Event Trigger

    Input Campaign Map TOD Best Practices Preferences Behavioral profile

    Services IBM Conversation Microsoft Entity Linking Google Prediction API Amazon Machine Learning

  • With which individualized information do we communicate personally?

    Outcome Personalizing Message Content

    Input Cluster attributes Demographic profile Psychographic profile Personality profile

    Services Amazon Content Personalization Microsoft Recommendation API

  • Example: DiSC Profile Comparison

    Jeff Stewart John Leininger Eric Remington

    Disc: Dci Disc: Isd Disc: Cdi

    is fairly aggressive, methodical, and results-driven, but can be approachable and supportive of others.

    thrives in an unstructured environment, loves exploring new ideas, and occasionally makes gut-driven decisions that might seem risky.

    is analytical, inventive, and craves tough problems to solve, but you can bore him easily with predictability.

    Do: focus on a single, clear message (ex: "I am reaching out to get your opinion.")

    Do: use personal anecdotes and information (ex: "I used to work in the same industry and want to get your perspective")

    Do: ask straightforward, even yes or no questions (ex: "Would you like to meet about this?")

    Don't: make any claims that cannot be backed up with proof (ex: "Our mutual friend wanted us to connect.")

    Don't: be overly formal and cold (ex: "I have 30 minutes to review this information.")

    Don't: use anecdotal expressions (ex: "I thought you might like this.")

  • How does an individual want to be talked with?

    Outcome Emotionalizing

    Input Psychographic profile Temperament profile

    Services IBM Personality Insights Google Prediction API CrystalKnows Profile Traxion Customer Insights

  • Example: Traxion Temperament

    Characteristics

    extroverted enthusiastic emotional sociable impulsive optimistic

    You want to be the first to experience something, and never miss out on an opportunity.

  • Expressive, Analytical, Passive, Aggressive

  • Personas Are Not Personal

    Personas are analogies, useful but not personal. What Is? Perse and meGraph

  • Perse Ontology

    Perse is an ontology and set of classes for creating and publishing a personalization profile with multiple facets or dimensions.

  • Perse Geography

    Current Residence Work Location Past Locales

  • Perse Demography

    VCard Contact Info Myers-Briggs Type Indicator Acxiom Demographics Personicx Clusters

  • Perse Knowledge

    Education Recommendations References Patents

  • Perse Experience

    Job History Volunteer Projects Publications

  • Perse Skills

    LinkedIn Skills Personal Competencies

  • Perse Interests

    Acxion Interest Categories LinkedIn Interests

  • Perse Personality

    Watson Personality Insights Traxion Customer Insights Kersey Temperament Sorter DiSC Profiles

  • Perse MatchMaker

    Job Match Campaign Match Targeting Match Email Match

  • meGraph Perse Personality Profile

  • Question Answerer Semantic Graph

    Now what questions can we ask?

    Lets ask Alexa!

  • Right message at the right time in the right place with the right tone

    Effective use of good data with advanced models and

    techniques can provide the margin of victory.

    Semantic models and information enhancement and

    discovery can help with understanding how people want

    to be communicated with.

    The right message at the right time in the right place

    with the right tone can motivate customers along their

    customer journey path.

  • Take-a-Ways...

    ...can I have a ( ) ?

    What is and Why Big Data

    NoSQL and Graph Databases

    Big Blue and others Deliver Answers

    The Best One is the Next One

    Me Per Se

  • More Questions? Contact me @

    https://www.linkedin.com/in/jeffreyastewart

    Jeffrey StewartIT and Management Consultant

    Asterius Media LLC

    Email: jstewart@asteriusmedia.com

    stewjeffrey@gmail.com

    Twitter: JeffreyAStewart

    LinkedIn: jeffreyastewart

    SlideShare: stewtrekk