Predictive Analytics, AI and the Promise of Personalization

Copyright © 2016 Earley Information Science1
Predictive Analytics, AI and the
Promise of Personalization
May 25, 2016
Copyright © 2016 Earley Information Science
Seth Earley, EIS
Dino Eliopulos, EIS
Julie Penzotti, Amplero
Adam Pease, Articulate Software

Today’s Agenda
• Welcome & Housekeeping
• Dino Eliopulos, Managing Director, Earley Information Science
(@DEliopulos)
• Session duration & questions
• Session recording & materials
• Take the polls & the survey!
• The Panelist Point of View
• Seth Earley, CEO, Earley Information Science (@SethEarley)
• Julie Penzotti, VP, Customer Analytics, Amplero
• Adam Pease, CEO & Principal Consultant, Articulate Software
• Expert Panel Discussion
• Questions & Answers
• Join the conversation: #earleyroundtable

Copyright © 2016 Earley Information Science3 Copyright © 2016 Earley Information Science
Predictive Analytics, AI and the Promise of
Personalization

Dino Eliopulos - Biography
Dino Eliopulos
Managing Director
Earley Information
Science
Deep specialization User experience and highly complex business
applications
Over 20 years of experience Machine Learning, Data Mining and other AI techniques
applied to deliver rich content-driven solutions
Financial Services, Retail / CPG, Telecommunications,
Travel and Entertainment, Healthcare, Pharmaceuticals,
Hi-Tech Manufacturing and Energy
Strategy, planning, forecasting, budgeting, measurement,
sales, talent acquisition / management and retention,
career stewardship, program management and service
delivery
IT professional services
Highly collaborative and results-oriented management
style delivers outstanding outcomes for clients, employers
and teams
Industry experience
Experienced leader and innovator in industry and high-end professional IT consulting
Outstanding outcomes

Predictive Analytics, AI and the Promise of Personalization
Personalization has been the big promise for the past 15 years. The problem is that this
vision is still a long way from reality.
Meaningful personalization requires
• meaningful knowledge and content assets
• the use of analytics to understand and model customers
• prediction to anticipate what they need and principles of AI
to fulfill the promise
This roundtable will review the state of the industry and discuss the problems and
challenges inherent in understanding and anticipating user needs and ways that
organizations can move the needle, improve engagement and move up the
personalization functionality maturity curve.

Personalization, predictive analytics and product data
6
Personalization, Predictive Analytics and Product Information three aspects of making
more appropriate recommendations for customers.
• Well curated product information in an ontology is at the core of ecommerce
offerings
• Predictive Analytics draws from sales and customer data sources in order to
provide product recommendations
• Personalization depends on rich structured product content and digital assets
• Machine Learning algorithms can improve the effectiveness of targeting and
improve effectiveness of merchandizers
• A combination of crowdsourcing, merchandizing expertise, curation and
automated techniques can be leveraged in an optimal solution

Personalization Components
Grouped Product
Sets
Product Data
Sales Data
Contextualized
filtering
Customer
Profile Data
Search History
Solution Domains
Solution
Landing Pages
Natural Language
Processing &
Inference
Domain
Knowledge
Information
Extraction
PDF/
Unstructured
7

Seth Earley - Biography
Seth Earley
CEO and Founder
Earley Information
Science
Over 20 years experience
Current work
Co-author
Editor
Member
Former Co-Chair
Founder
Former adjunct professor
Guest speaker
AIIM Master Trainer
Course Developer &
Master Instructor
Data science and technology, content and knowledge
management systems, background in sciences (chemistry)
Enterprise IA and Semantic Search
Information Organization and Access
US Strategic Command briefing on knowledge networks
Northeastern University
Boston Knowledge Management Forum
Long history of industry education and research in emerging fields
Academy of Motion Picture Arts and Sciences, Science
and Technology Council Metadata Project Committee
Editorial Journal of Applied Marketing Analytics
Data Analytics Department IEEE IT Professional Magazine
Practical Knowledge Management from IBM Press
Cognitive computing, knowledge and data
management systems, taxonomy, ontology and
metadata governance strategies

• Personalization is based on “electronic body language”
– Web site behaviors, click streams, downloads, consumed content
– Past purchases
– Social media
– Social graph
– Explicit preferences
– Derived attributes
– Hidden characteristics
Personalization signals

• The question is, what do we offer?
• Once we know something about a user, what do we do with that
knowledge?
• We are trying to give them something we think they want
– In the context of their task
– To meet a specific need
– Solve a particular problem
• Personalization is making a recommendation about a product, service,
solution, piece of content or next action based on what we know in
advance and what the customer is telling us at that moment
Personalization as Recommendation

• Offers – need offers that can be recombined
• Content – content to support the user’s task
• Products – what products will be presented to the user
• Rules (derived or developed) – how will assets and content be
assembled for the user
• Need to identify and understand customer segments, behaviors
and content to drive desired behaviors
Components of Personalization

Mine data sources for customer behaviors and product groupings
12
• Product Attributes derived from analytics
• Correlating POS data, PIM, Tech Support
• Structured textual data mining text
• Reuse rich and mature ontology
• Inference engine to deduce relationships
• Derived, curated and synthesized product
data to support customer tasks and processes
• Integrated into user experience to generate
custom suggested search results
How can we infer what products customers
want to see when they enter a search term?
Can we improve conversions of products based
on search engine marketing (paid search)?
Knowledge Content
• Portion of revenue from high
value customers
• Time between purchases
Sales analysis
• High value customers
• One time buyers
• Lapsed customers (retargeting)
• Tasks, solutions, interests
Customer Profiles
• Keyword searches and
subsequent behaviors
(conversions vs abandonment)
Web Behaviors
• Hi value product bundles,
product bundles
• Segment and product
bundle relationships
Product Data
• Organizing principles and
related content
Competitors/Suppliers
PRODUCT DATA
ENHANCEMENT
DATA MINING
DATA SOURCES
EXISTING ECOMMERCE PLATFORM
RESULTING DATA ASSETS
What products are purchased together?
What keywords lead to what behaviors?
How are customers described and grouped?
+

Analysis Approach
13
Use case Input Analytical
approach
Output Purpose or Benefit
Sales pattern
analysis
Order size, product
mix
Unsupervised to
identify clustering
Sales correlated with
customer types, segments
and product combinations
Repeat customers, one time buyers, lapsed customers (for personalized
retargeting offers), time between purchases – customer journey
(segment and product bundles), customer value, top value customers
generating most revenue, highest profit, portion of revenue from high
value customers and related clickstream behaviors
Real time behavior
(electronic body
language)
Search logs, paid
search, click
stream data, email
marketing results
Supervised learning
to identify keywords
leading to high
margin sales clusters
Keywords and messaging
clustered with concepts and
customer attributes leading
to conversions
Insights on keywords and concepts related to purchase funnel – what
people do once they are on the web site, where they abandon based on
intent inferred from email traffic, organic search, paid search and onsite
search and subsequent behaviors
Competitor
analysis
Product results
from keywords
Crawling, content
mining, graph
building
Target product classes for
optimization
Compare search results from failed searches with competitor results to
identify opportunities for improved experience
Interests
questionnaire
Customer
responses, sales
data
Combination of
supervised and
unsupervised
Topic map of possible
offering areas aligned with
customer interests
Correlate interests with RFM, seasonality, industry, product bundles,
high value customers, tasks, solutions
Failed conversions Web traffic logs,
web analytics
Tree search
methods (binary,
monte carlo, etc)
List of
terms/phrases/concepts for
optimization
Supervised learning to identify customers who are abandoning cart
versus not abandoned, what search terms are driving customers to
abandon, areas for remediation (content for search terms)

Discovery of product combinations
Identify competitive differentiators,
strategic initiatives, priority
categories.
5 – 10 target processes
Products grouped to support
task, process or solution
MERCHANDIZERS
Target categories
Target processes
Intelligent Parser
USE CASES TARGET PROCESSES
PRODUCT
COMBINATIONS
KNOWLEDGE AND
EXPERTISE CONTENT
Customer Support Content
Maintenance manuals
Key Opinion Leaders
What products are used in combination?
Supports SEO, surfaces
expertise and related
content
RELATED
CONTENT
14

Poll Question #1
What is the maturity level of your knowledge and use of data-driven
personalization?

• None
• Dabbling
• Successful proof-points
• Concentrated capability development
• Core strength
What is the maturity level of your knowledge and
use of data-driven personalization?

• Continued development of Amplero, a self-learning
personalization technology platform for B2C marketing
automation
• Focus on deep data understanding, developing key findings,
driving customer interpretation/understanding
• Previously a scientist and consultant in the pharmaceutical
industry, specializing in data mining and analytics for drug
discovery
• 25+ publications and several patents.
• Earned a Ph.D. in Bioengineering and M.S. in Physical Chemistry
from the University of Washington and received her B.S. in
Biomedical Engineering from Duke University.
Julie Penzotti - Bio
Julie Penzotti
VP, Customer Analytics
Amplero

CONFIDENTIAL
Purchases,
Usage,
Contacts,
Demographics,
Social Connections
& their demographics,
Propensity Models
Other…
BigData andtheAge of theCustomer
Channel,
Day,
Time,
Location,
Other…
Execution
Offer,
Offer Expiry period,
Incentive Type,
Incentive Amount,
Message,
Creative,
Semantic Tags,
Other…
Experience
Context
Customer
Today’s customers…
• Expect you know them
• Are fickle and jaded
• Tell others what they think
• Are always connected
• Are empowered to act

CONFIDENTIAL
Unfortunately,rules-basedapproachesdon’tscalefor B2C
[ 19 ]
With
20 – 30
targeting rules
which one
will work
best?
?

CONFIDENTIAL
MachineLearningto automateandoptimizetargetingat scale
[ 20 ]
Modelling &
Enrichment
Marketing
Asset
Library
Machine
Learning
Experimenting
Enriched
Data
Decision
Tree
Offers
Customer
Data
Customers
Marketer
Decisioning
Tomorrow’s marketer…
• Agile and responsive
• Runs campaigns in a loop
• Gathers and applies
insights constantly
• Thinks empirically rather
than intuitively
• Let’s the machine do the
heavy lifting

CONFIDENTIAL
Machinelearningto discover personalizedcontextsthat optimizeperformance
[ 21 ]
DISCOVERED BY AMPLERO
Revenue Lift: +4%
Confidence: Low
CONFIGURED BY CAMPAIGN MANAGER
Offer: Unlimited Upgrade
Eligibility: International Saver Plan Subscriber
Revenue Lift: -4%
Confidence: Medium
Revenue Lift: +8%
Confidence: Medium
Condition:
+Voice Consumption Cluster 5
Condition:
+Voice Consumption Cluster 4
Revenue:
+14%
High
Revenue:
-1%
High
Revenue:
-5%
High
Offer Price: $10 $15 $20
Revenue:
+6%
High
Revenue:
-10%
High
Smart Package Owner: No Yes
KPI
Targets
KPI
Controls
Revenue Lift:
+10%
Confidence: High

CONFIDENTIAL
Multi-armed bandits to manage
decisioning for marketing contexts:
– Hedge bets about which choice is best
– Increasing certainty as more response data
is gathered from customers
– Exploration/exploitation trade-off permits
agility and adaptation
– Generalized learning over customer and
marketing attributes
– Automatically segments population
according to responses to different
experiences
[ 22 ]
Machinelearningfor adaptivepersonalizationandmaximum benefits
Mean Lift Estimates of Performance
Context 1 Context 2 Context 3 Context 4
Probability
of Selection
Bandit Policy
Customer Attributes + Experience + Execution
Optimization
Models

Poll Question #2
What is the appetite and interest of applying machine learning in
your organization?

• Organization still skeptical
• Open to, but not sure where to dive in
• Trying out techniques
• Clear identified ROI and priorities
What is the appetite and interest of applying machine
learning in your organization?

Adam Pease - Bio
Adam Pease
CEO & Principal
Consultant
Articulate Software
apease@articulatesoftware.com
History
Undergrad CompSci, Doctorate Linguistics
Program Manager, Teknowledge (mostly DARPA contracts)
CEO, Articulate Software (commercial consulting in data
modeling)
Cognitive R&D Manager, IPsoft
Specialties
Suggested Upper Merged Ontology
Open source, higher-order logic, 15 yr history, mapped to
WordNet
http://www.ontologyportal.org
Sigma
Open source, Reasoning, ontology modeling, deep NLP
“Ontology: A Practical Guide” - 2011

Adam Pease – Articulate Software Earley Executive Roundtable

Don't forget about knowledge based methods
• What's the problem you're trying to solve?
• There's more than just matching to do
• Matching methods reaching asymptote on many tasks
• Semantics is often what's missing
• Semantics and KR matters
• What's most popular may not be the best technical
solution

Personalization as Dialogue
• Are we making the problem too hard?
• Billings and Reynard (1981) – 73% of air traffic incident
reports involved problem in communication
• People have problems answering questions and communicating too
• Dialog is how we address the problem with people

Knowledge Discovery
• Use Data Mining to discover trends and relationships
• Express them in computable semantics
• Can be explained
• Spurious correlations can be understood and corrected
• Consolidate gains – don’t learn things that are already known

Suggested Upper Merged Ontology
• Initial versions: 1000 terms, 4000 axioms, 750 rules
• Mapped by hand to all of WordNet 1.6
• then ported to 3.0 and continually updated
• Associated domain ontologies totalling 20,000 terms and 80,000 axioms
• Now linked with factbases including YAGO for millions of facts
• New ontologies of Hotels and Dining
• If-then rules, not just a taxonomy or semantic web structure
• Free
• SUMO is owned by IEEE but basically public domain
• Domain ontologies are released under GNU
• www.ontologyportal.org

Poll Question #3
Does your organization take a rules-based or statistical based
approach to personalization?

• Primarily rules-based
• Primarily statistical-based
• Both
• None
Does your organization take a rules-based or
statistical based approach to personalization?

Panel Discussion

Roundtable Discussion
Dino Eliopulos
Managing Director
Earley Information
Science
Seth Earley
CEO
Earley Information
Science
Adam Pease
CEO & Principal Consultant
Articulate Software
Julie Penzotti
VP, Customer Analytics
Amplero

Suggested Resources
• Introductory video on ontology - http://www.youtube.com/watch?v=EFQRvyyv7Fs
• Pease, Adam. “Ontology: A Practical Guide” - http://www.ontologyportal.org/Book.html
• SUMO on line - http://54.183.42.206:8080/sigma/Browse.jsp?kb=SUMO
• Ontology Publications - http://www.adampease.org/professional/
• Adam Pease’s Podcasts & Blog - http://www.ontologyportal.org/
• Earley, Seth. "Cognitive Computing, Machine Learning and Personalization: New Marketing Constructs or
New Capabilities?" KMWorld, November/December 2015. http://www.kmworld.com/
• “Making it Personal: Strategies for Creating Meaningful Customer Interactions”
http://www.earley.com/blog/making-it-personal-strategies-creating-meaningful-customer-interactions
• “Contextualizing Customer Journeys” Earley Executive Roundtable, Nov 2015
http://info.earley.com/roundtable-contextualize-customer-journeys
• Penzotti, Julie, “Marketing in the Age of Machine Learning: How optimising personalization granularity
leads to better performance in a dynamic market”, Applied Marketing Analytics, Vol 2 (1):41-51
• Amplero research links: http://www.amplero.com/research/article/?s=why-offer-response-rate-is-the-wrong-
metric-for-evaluating-marketing-performance

Earley Information Science
(EIS)
Information Architects
for the Digital Age
Founded – 1994
Headquarters – Boston, MA
www.earley.com
For more info contact:
info@earley.com
careers@earley.com
Thanks to our Sponsors
Next Roundtable topic
June 22 – Site Search: The Battle for
Relevance

Backup

SUMO+Domain Ontology
Military
Geography
Elements
Terrorist
Attack TypesCommunicationsPeople
Transnational Issues
Finance
Terrorists
EconomyNAICS
Terrorist
Attacks
Distributed
Computing
Biological
Viruses
WMD
ECommerce
Services
Government
Transportation
World Airports
Total Terms Total Axioms Total Rules
20977 88257 4730
Relations: 1280
Hotel
Food
Hotel
Dining
Media
Domain
Cars
UI/UX
SUMO
Mid-Level
Qualities
Mereotopology
Graph ProcessesMeasure Objects
Structural Ontology
Base Ontology
Set/Class Theory TemporalNumeric

WordNet
• A dictionary for computational linguistics applications
• 100,000 word senses, hand-created
• Mapped by hand to SUMO
• Open source
• Semantic links
• Aid in computation
• Verification of meaning during construction

Formal Ontology
• WordNet has synsets for “earlier” etc
• But nothing in WordNet would allow a computer to assert that the
end of one event precedes the start of another if one event is earlier
than the other
• This is not a criticism of WordNet
time
(<=>
(earlier ?INTERVAL1 ?INTERVAL2)
(before
(EndFn ?INTERVAL1)
(BeginFn ?INTERVAL2)))
Interval 1 Interval 2

Example Rules
(=>
(instance ?DRIVE Driving)
(exists (?VEHICLE)
(and
(instance ?VEHICLE Vehicle)
(patient ?DRIVE ?VEHICLE))))
“If there's an instance of Driving, there's a
Vehicle that participates in that action.”
Not just an English definition for humans to read, but
a logical definition that can be used in proofs.

Predictive Analytics, AI and the Promise of Personalization

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Predictive Analytics, AI and the Promise of Personalization