2016 xug conference big data: big deal for personalized communications or meh?
TRANSCRIPT
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
...or is it just ¯\_(ツ)_/¯
❏ Big Data
❏ Semantics
❏ Patterns
❏ Paths
❏ Answers
❏ Insights
Big Data
4 V’s Volume, Variety, Veracity, Velocity
http://www.ey.com/gl/en/services/advisory/ey-big-data-big-opportunities-big-challenges
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 modeling”“predictive 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
Semanticsəˈ
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:
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
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/
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 it’s just a toaster. ”Salesforce Einstein Proves that AI is Relative
Patterns
● Knowledge Representation
● Pattern recognition ● Machine Learning
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?
● Isn’t 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 OntologyΠέρση əː ˈ ɪ
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?
Let’s 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 ( ͡° ͜ʖ ͡°) ?
❖ 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: [email protected]
Twitter: JeffreyAStewart
LinkedIn: jeffreyastewart
SlideShare: stewtrekk