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GILBANE'S DIGITAL EXPERIENCE CONFERENCE
TUESDAY, APRIL 30TH, 11:45 A.M. - 12:30 P.M.
There's No AI Without
Information Architecture
(IA)
Seth Earley, Founder & CEO, Earley Information Science
seth@earley.com
781-820-8080
www.earley.com
www.linkedin.com/in/sethearley
Copyright © 2017 Earley Information Science, Inc. All Rights Reserved. 2
1994
YEAR FOUNDED.
Boston
HEADQUARTERED.
50+
SPECIALISTS & GROWING.
Earley Information Science is a specialized information agency. We support measurable
business outcomes by organizing your data, content and knowledge assets.
Our proven methodologies are designed specifically to address product data, content
assets, customer data, and corporate knowledge bases. We deliver scalable governance-
driven solutions to the world’s leading brands, driving measurable business results.
We make information more
useable, findable, and valuable.
Copyright © 2017 Earley Information Science, Inc. All Rights Reserved. 3
AWARDS & RECOGNITION
2018 100 Companies that Matter in KM
2017 100 Companies that Matter in KM
2016 100 Companies that Matter in KM
2015 100 Companies that Matter in KM
2014 100 Companies that Matter in KM
2014 Trend-Setting Products Award
2013 Trend-Setting Products Award
2008 Trend-Setting Products Award (Wordmap)
2013 Applied Materials’ added to
InformationWeek 500 List of Business
Innovators
• “Cognitive Search Is Ready To Rev Up
Your Enterprise’s IQ”
• “Google-ize Your Site-Search Experience”
• “Polishing Up Your Products —
Why PIM Really Matters”
• “Artificial Intelligence Solution Landscape”
ANALYST MENTIONS
• “Unlocking the Hidden Value of
Information (Applied Materials)”
2015 KM Reality Award
(Allstate Business Insurance, ABIe project)
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Further Reading/Future Reading
The Problem With AI
There’s No AI Without IA
February 2020
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Three take aways
1. Artificial Intelligence is an evolution of technologies that have
been around for decades
2. Artificial Intelligence requires a solid foundation in information
management principles
3. Knowledge engineering can produce ROI today while preparing
for an AI future
5
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AI as Evolution
6
Principles of machine learning have been
embedded in software we have used for years
7
“ When [AI] finally works, it
gets co-opted by some other
part of the field. So, by
definition, no AI ever works;
if it works, it’s not AI.
Source: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-825-
techniques-in-artificial-intelligence-sma-5504-fall-2002/lecture-notes/Lecture1Final.pdf
“
8
Spell check Speech recognition
• Mitigation of compliance risks
• Removal of Personally Identifiable
Information (PII)
• Removal of Redundant, Outdated and
Trivial (ROT) content
• Protection of intellectual property
• Identification of patterns of fraud
• Detection of customer sentiment
• Prediction of credit risks
• Feature extraction from product data
Text Analytics,
a long time staple
of content
management, is
now called “AI”
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combined with a sophisticated UX”
Source: https://www.theregister.co.uk/2017/01/02/ai_was_the_fake_news_of_2016/
“The definition of “AI” has been stretched
so that it generously encompasses pretty
much anything with an algorithm”
vast knowledge
“What seems to be AI, is ,
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Managing vast amounts of knowledge
requires a Knowledge Engineering approach
11
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The Customer Experience and
Personalization
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How
customer-
oriented
are we?
13
Personalization has been
the big promise for the
past 15 years.
The problem is that this
vision is still a long way
from reality.
What Customers Want…
• Answers to their problems
• The right product
• Options and meaningful choices
• Help with their goal
• Assistance with a task
• Recommendations for a solution
• Expertise that they trust
• Responses that move them forward
• A “rewarding” experience
• Convenience, speed, efficiency
• Ease of doing business
What They Actually Get…
• Convoluted navigation
• Search results that are difficult to filter
• Too many choices
• Endless phone menus
• Call center reps w/o requisite knowledge
• Confusing content
• Marketing language that tries to sell them
• Incomplete information
• Frustrating, disconnected interactions
• Impediments, lack of responsiveness
• High friction processes
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Promise of Personalization
14
Personalization has been the big promise for the past 15 years. The problem
is that this vision is still a long way from reality.
• High fidelity journey models reflecting
audience goals based on buying stage
• A multi dimensional audience model
that describes their intent in data
terms along their journey
• Product data models aligned with
user buying criteria
• A messaging architecture to allow for
refactoring of offering components
• Componentized content that can be
reassembled to test offer and message
variations
• Knowledge and insights from across
the organization to articulate customer
needs and company solutions
Meaningful personalization requires
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Product Information, Content and the Customer Journey
Internal audiences need to easily find,
share and reuse content, data and
insights to support the external
customer experience
Merchandizers
Product managers
Category owners
MARKETING PROMOTION / PLANNING
PRODUCT DEVELOPMENT
Product Data/Content Product Content / Product Assets
PIM
PRODUCT ONBOARDING
PIM
Manager
Catalog
Manager
Merchandizer
Product Information Management
Campaigns
Email Marketing
Social media
Promotions
DEMAND GENERATION
$
Marketing managers
Marketing analysts
CONTENT STRATEGY
Editorial manager
Content manager
Category manager
Product content
Product assets
Marketing plans
ECOMMERCE
PERSONALIZATION
STRATEGIES
Purchase history
Demographics
Interest profile
Buyer persona
CUSTOMER SUPPORT
Call Center
Agents
Documentation
Warranty
Knowledgebase
Content/data source
Person/role
Collaboration
PROCESS
Support managers
K-base owner
CUSTOMER SELF
SERVICE
Reviews
Manuals
Knowledgebase
Regional managers
Market Analyst
Merchandizer
Market data
Regional demographics
Store sales
PROMOTIONS
Collaboration, Insights and Knowledge Sharing
Content Optimization
Customer Journey
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Intelligent Content Lifecycle
Internal audiences need to easily find,
share and reuse content, data and
insights to support the external
customer experience
Merchandizers
Product managers
Category owners
MARKETING PROMOTION / PLANNING
PRODUCT DEVELOPMENT
Product Data/Content Product Content / Product Assets
PIM
PRODUCT ONBOARDING
PIM
Manager
Catalog
Manager
Merchandizer
Product Information Management
Campaigns
Email Marketing
Social media
Promotions
DEMAND GENERATION
$
Marketing managers
Marketing analysts
CONTENT STRATEGY
Editorial manager
Content manager
Category manager
Product content
Product assets
Marketing plans
ECOMMERCE
PERSONALIZATION
STRATEGIES
Purchase history
Demographics
Interest profile
Buyer persona
CUSTOMER SUPPORT
Call Center
Agents
Documentation
Warranty
Knowledgebase
Content/data source
Person/role
Collaboration
PROCESS
Support managers
K-base owner
CUSTOMER SELF
SERVICE
Reviews
Manuals
Knowledgebase
Regional managers
Market Analyst
Merchandizer
Market data
Regional demographics
Store sales
PROMOTIONS
Collaboration, Insights and Knowledge Sharing
Content Optimization
Customer Journey
Product Data Maturity
Content Optimization Maturity
Knowledge Engineering Maturity
Customer Experience Maturity
Monitored by Metrics and
Governance Playbook to Track
Progress, ROI and Course
Corrections
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Multiple Components of Personalization
Content
Customer
Product Data
Knowledge
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Maturity of Capabilities
Personalization programs based on machine learning and intelligent content are part of a larger
ecosystem of tools, processes, methodologies and collaboration.
18
KNOWLEDGE ties
together all of the other
pieces – it is the human
element of judgement,
expertise and creativity
that is harvested from
experts and embedded in
data models and
processes.
CONTENT to engage the
customer must be findable,
and it must be relevant in the
moments that matter.
Content components and
snippets are the building
blocks for machine optimized
offerings
CUSTOMER DATA
needs to be consistent,
harmonized from different
systems and modeled to
provide and respond to
signals from interactions
both upstream and
downstream.
PRODUCT DATA
models must be
complete and aligned
with attributes and details
that are important to the
customer’s decision
making criteria.
METRICS DRIVEN
GOVERNANCE
measures ROI of projects
and provides feedback for
course corrections and
fine tuning of user
experience, product data,
and content decisions and
is the foundation for
automation
Maturity is required across all dimensions for long term success
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Modeling the Customer
Static Customer Data
Dynamic Customer Data
PRODUCT ARCHITECTURE
COMPONENTIZED CONTENT
CUSTOMER DATA FOUNDATION
DELIVERING PERSONALIZED CUSTOMER EXPERIENCES AT SCALE
What does it take to do this right?
advanced services & expertise
unified models standardized platforms & processes
20
• enriched customer journeys
• product attribute model & corresponding
taxonomies
• data intake, clean-up, aggregation.
• analysis, recommendation & decision
making
(ML, data science, human judgment)
• process setup (continuous or periodic)
• standard pipeline for insight
delivery to marketing teams
KNOWLEDGE & INSIGHTS
• product data with e-catalog and display
hierarchies optimized for customer
journeys
• back end product information onboarding
process aligned with customer
experience practices
• metrics driven decision making
• merchandizer collaboration with product
and solution experts
• configure price quote and recommendation
tools aligned with user personas and pain
points
• product information management
ecosystem aligned with rich media
• cross sell and upsell relationships
• merchandizing and solution bundles
• optimized content structure
• component architecture aligned with
messaging architecture
• content attribute model & corresponding
taxonomies
• omnichannel offer recommender
• dynamic offer generator
• content assembly based on offering
architecture and baseline hypotheses
tested against target outcomes
• recombination tested continuously using
changing messaging architecture
• component content management
system
• content production workflows
• content standards & governance
• high fidelity customer journeys with
augmentation and automation
opportunities
• customer attribute model &
corresponding multi-dimensional
audience taxonomies
• profile standardization
• pattern recognition
• customer signal reconciliation across
upstream platforms
• machine learning development & training
• customer data platform
• customer data modeling
• cross system normalization
• metrics aligned data governance
decision making
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Using a “High Fidelity” Journey Map
21
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Understand the customer journey
Identify details of the customer
Define content needed White Paper Product compare tool
Installation guide
Static Customer Data Dynamic Customer Data
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high-fidelity customer journey model
What does it take to do this right?
customer model
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
22
INTELLIGENT
PERSONALIZATION
Component content model
User journey/customer model
Product data model
Knowledge architecture
Static customer data:
industry, role, interests
Dynamic metadata identifies changing, real time. signals about customer
goals and intent while they go through their journey
Customer Data
Platform
Download white paper
Product compare &
purchase
Download installation guide Open email & click offer
Dynamic customer data
Dynamic customer data:
campaign responses, click
through, recent purchases
Delivering Personalized Customer Experiences – At Scale
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high-fidelity customer journey model
Dynamic customer data
customer model
CMS and PIM
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Content
23
INTELLIGENT
PERSONALIZATION
Component content model
User journey/customer model
Product data model
Knowledge architecture
Static customer data:
industry, role, interests
Dynamic customer data:
campaign responses, click
through, recent purchases
Customer Data
Platform
Top of funnel content
White Paper
Basic Widget
Product
New customer offer
Download white paper
1
Delivering Personalized Customer Experiences – At Scale
What does it take to do this right? Dynamic metadata identifies changing, real time. signals about customer
goals and intent while they go through their journey
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high-fidelity customer journey model
Dynamic customer data
customer model
CMS and PIM
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Content
24
INTELLIGENT
PERSONALIZATION
Component content model
User journey/customer model
Product data model
Knowledge architecture
Static customer data:
industry, role, interests
Dynamic customer data:
campaign responses, click
through, recent purchases
Customer Data
Platform
Top of funnel content
White Paper
Basic Widget
Product
Middle of funnel content
Product compare tool
Deluxe Widget
New customer offer
New customer offer
Download white paper
Product compare &
purchase
2
What does it take to do this right? Dynamic metadata identifies changing, real time. signals about customer
goals and intent while they go through their journey
Delivering Personalized Customer Experiences – At Scale
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high-fidelity customer journey model
Dynamic customer data
customer model
CMS and PIM
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Content
25
INTELLIGENT
PERSONALIZATION
Component content model
User journey/customer model
Product data model
Knowledge architecture
Static customer data:
industry, role, interests
Dynamic customer data:
campaign responses, click
through, recent purchases
Customer Data
Platform
Top of funnel content
White Paper
Basic Widget
Product
Middle of funnel content
Product compare tool
Deluxe Widget
Post purchase content
Installation guide
New customer offer
New customer offer
Download white paper
Product compare &
purchase
Download installation guide
3
Delivering Personalized Customer Experiences – At Scale
What does it take to do this right? Dynamic metadata identifies changing, real time. signals about customer
goals and intent while they go through their journey
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high-fidelity customer journey model
Dynamic customer data
customer model
CMS and PIM
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Content
26
INTELLIGENT
PERSONALIZATION
Component content model
User journey/customer model
Product data model
Knowledge architecture
Static customer data:
industry, role, interests
Dynamic customer data:
campaign responses, click
through, recent purchases
Customer Data
Platform
Top of funnel content
White Paper
Basic Widget
Product
Middle of funnel content
Product compare tool
Deluxe Widget
Post purchase content
Installation guide
Super Widget
Post purchase nurture
content
User tips
Promotion for upsell
New customer offer
Existing customer offer
New customer offer
Download white paper
Product compare &
purchase
Download installation
guide
Open email & click offer
4
What does it take to do this right? Dynamic metadata identifies changing, real time. signals about customer
goals and intent while they go through their journey
Delivering Personalized Customer Experiences – At Scale
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Personalization Example: Cross/Up-sell
27
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AI-driven personalization
requires
• meaningful knowledge and
content assets
• analytics to understand
and model customer
behaviors
• consistent architecture
across product information,
customer and content
models
• knowledge of problems to
anticipate needs
AI promises to help businesses meet / anticipate
customers’ needs
Product relationships
based on knowledge of
solutions
Browse history and
other constraints
(products available in a
region)
Configurations and
answers to questions
Product – review
relationships: what to
show, sorting rules
CHATBOTS
REVIEWS
CUSTOMER ALSO
VIEWED
RELATED
PRODUCTS
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Watson Intelligent Assistant
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Example: Amazon Alexa Skills
Skills are tuned for specific use cases. They use AI, but are backed up by a vast set of APIs and
knowledge. Skills surround a set of limited use cases (e.g., buy a movie ticket, order a pizza,
etc.), and all the AI, content and engineering are tuned for those specific interactions.
30
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Complex Advisory/ Diagnosis
Product Support
Product Configuration
Judgment Based
Domain
Complexity
Transaction Support Knowledge Retrieval
Information/
status inquiries/
order processing
Task/dialogue Complexity
31
31
Task Complexity versus Domain Complexity
“Helper bots”
“Configuration bots”
“Transaction bots”
Don’t start here
High domain complexity/
High task complexity
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Basic
Search Engine
Knowledge
Portal
Virtual
Agent
Intelligent
Assistant
Knowledge
Base
Any text
Multiple sources
Multiple sources, separate
Ontologies and schemas
Domain specific
Highly curated sources
Dynamic. Info enrichment
improves with interaction
Search
Interaction
Keyword or full text
query
Full text query or
Faceted Exploration
Query, explore facets
Offers related info
Implicit query / recommends
based on users’ history
Information
Architecture
None necessary, but
Improves with metadata
Ontologies, clustering,
classification
Ontologies, clustering,
classification, NLP
Ontologies, clustering,
classification, NLP,
personalization
User
Experience
Search box, documents list Role-Based Conversational
Conversational,
personalized,
contextual
Enabling
Technology
Search box, documents list
Search, classification,
databases
NLP, search, classification
Process engines
NLP, search, classification
Machine Learning
IA of Increasing Importance Moving to AI
Increasing Functionality / Increasing Information Richness
32
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1-Unpredictable 2-Aware 3-Competent 4-Synchronized 5-Choreographed
Metadata
Processes
Chaotic tagging practices
with no taxonomy
Business metadata in use, with
controlled vocabularies in isolation
Enterprise taxonomy is replicated
across sites and platforms, and
metadata use is manual but consistent
Content schemas with clear metadata
requirements are used effectively to
group, aggregate, sort, and filter
assets in the interface
Contextualized and/or personalized
metadata with auto-population of
values and ongoing quality audits
IA / User
Experience
Haphazard creation of content and
sites with inconsistent content models
and poor site experience
More consistency across sites and
platforms, but with little cross-site
structure
Consistency across sites in how
content is displayed, particular in
regards to metadata relevant to
specific groups and audiences
Enforced metadata standards provide
a context-dependent user experience
for content management and receipt,
in a Web-page-like experience
Integration of structured and
unstructured information from
multiple systems dynamically
presented to support user tasks in an
application-like experience
Search
Integration
Random document generator
Some tuning of search with
individualized content tagging
Scope-able search with consistent but
uncontrolled tagging
Faceted search with taxonomy-
governed tagging; metadata available
for use within the search experience
Search-based application with
associative relationships (related
search), tuned algorithms, facets, and
metrics-driven use cases
User
Proficiency &
Content
Practices
Poor or minimal usage, lack of
awareness of capabilities or content
practices
Early adopters and power users using
out-of-box features, little control of
content
Departmental collaboration with basic
content control
Cross-team project-oriented
collaboration with information
lifecycle management
Automated workflows and reporting
for compliance with enterprise
content standards
Governance
Information sprawl, lack of vision, no
intentional decision making, minimal
centralized architectural support
Awareness of challenges; activity is
monitored but not constrained;
analytics data exist but are not
reviewed and/or managed
consistently
Assigned responsibilities, oversight
and communications infrastructure in
place, best practice documentation is
published and available
Intentional decision making, resource
allocation, change controls in effect;
quality metrics are regularly reviewed
Agenda-driven business leadership
and stakeholder engagement to effect
continuous process improvement
through measurement and
collaboration
Technology
Business groups use their own
organically developed approaches;
business requirements are
undocumented. No global search.
Business groups follow improvised
processes within established tools;
multiple work-arounds used to
improve information findability
Global systems processes are
supported by home-grown or external
systems for tracking, managing, and
finding content
Dedicated, managed systems enable
multiple distinct workflows and
processes that business groups
understand, follow, and find
information
Systems are architected to share
information within and across teams
and divisions, to find content with high
relevancy
Building Maturity current target
priorities
33
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The Knowledge Management challenge is usually put into
language that confuses the issue:
Vendors say that they need to “train the AI”
What do you “train the AI” with?
…high value knowledge assets (quality data and curated content)
The Knowledge Management Challenge
34
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THE BOTTOM LINE
“Training data” is the biggest challenge to artificial intelligence program
development
The knowledge that artificial intelligence driven bots need is the same
knowledge that humans need
By building knowledge management programs to support employees, the
organization is preparing for improved access through emerging AI and Bot
technologies
35
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EIS Bot Factory workstreams
• Content type and
variable definitions
• Classification schema
design
• Feature engineering
• Vocabulary development
• Associative relationship
mapping
• Deconstruction and
componentization of
FAQ’s,
troubleshooting
guides, reference
materials, e-learning
modules, etc.
• Content refactoring
and component
tagging
• Integration of
component models
with user experience
• Crowdsourcing of
phrase variations for
intent triggers
(utterances)
• Classification of intent
using customer issue
and query data
• Entity extraction
training and tuning
• Escalation and handoff
model
• Feedback workflow
design: utterance,
intent and knowledge
• Success metric design
• Governance and
accountability model
• Speech to text conversion
• Text mining/ analytics on
call logs / support content
• Search analytics
• User journey mapping
• Scenarios and use cases
• Identification of
repeatable, unambiguous
processes
• Deconstruction of user
journeys into dialogue
components
• Precoordinated intent
design
• Disambiguation models
• Intent entity extraction
• dialogue context tagging
model
PROCESS
ANALYSIS
DIALOGUE DESIGN &
INTENT CLASSIFICATION
CONTENT ANALYSIS,
DOMAIN MODELING
& ONTOLOGY DESIGN
COMPONENTIZATION OF
KNOWLEDGE CONTENT
TRAINING DATA
CORPUS
DEVELOPMENT
HYBRID LEARNING &
CONTINUOUS
IMPROVEMENT MODEL
CREATION
36
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Standardized/Normalized Content is Portable and Reusable
37
Standardized domain
specific schemas for
reuse
Field 1
Field 2
Field n
…
Field 1
Field 2
Field 3
Field n
…
ELearning, FAQ’s,
Troubleshooting
charts, support
articles
Componentized
content
Tagging for ingestion
Componentized content can be
repurposed across tools and
technologies Improved CSR
Information Access
Faster time to value for all information
access scenarios
Portability across AI and
Chatbot systems
Improved customer self
service
Metrics aligned with specific
content performance
COMPONENTIZATION OF
KNOWLEDGE CONTENT
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Agent: “I need to determine liability coverages for employee
actions for a collection agency in Massachusetts”
Agent: “Hi, I need some help with a policy”
Semantic deconstruction of utterance
Bot: “OK. Can you tell me what kind of policy?”
Topic = “liability coverage”
Product = “employee practices liability”
Nature of business = “collection agency”
Region = “Massachusetts”
Content type = “Guideline”
Entity derivation
Context derivation
Audience = “Certified agent”
Topic
Product
Nature of business
Region
Content type
Audience
Faceted retrieval from
knowledge base
Returns content tagged
with appropriate metadata
38
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Domain modeling, knowledge mapping
ABI manual maps – Concept Topics
Job Aid maps - Process Topics
FAQ maps - QA Pairs
39
CONTENT ANALYSIS,
DOMAIN MODELING
& ONTOLOGY DESIGN
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Allstate Strategy
ABI Objectives
ABI Revenue
Call Center Traffic Agent Book of Business
Business Processes
ABIe Effectiveness Content Quality
DITA Content
& Taxonomy
ABIe Stats,
Google
Analytics
Search
Test
Cases
Processes enable
objectives
L
I
N
K
A
G
E
Search Quality
Grow top line Allstate revenue
Data supports
(and measures)
processes
Objectives align
with strategy
Data Sources
Working here
(search, content,
IA, taxonomy,
metadata, etc.)
Measuring here
(Process
measures)
Measuring here
(ABI KPIs –
Results
Measures)
Aligning enterprise strategy with virtual assistant metrics
CEO: “Show me how will this project increase our revenue.”
ABIe Utilization
HYBRID LEARNING &
CONTINUOUS
IMPROVEMENT MODEL
CREATION
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Search
Analysis
• Identify new taxonomy terms
• Identify new auto-completes
• Identify missing content
Behavior
Analysis
• Analyze & Remediate Top Best Bets
• Analyze & Remediate Top Searches
• Identify missing content
Utilization
Analysis
• Correlate ABIe usage to call center volumes
• Gather and analyze VOC
• Identify action plans
Content
Analysis
• Measure compliance with editorial guidelines
• Measure compliance with tagging guidelines
• Remediate issues
Metrics-driven Governance Processes
HYBRID LEARNING &
CONTINUOUS
IMPROVEMENT MODEL
CREATION
Copyright © 2019 Earley Information Science, Inc. All Rights Reserved.
www.earley.com
Contextual customer
information
Customer/Bot/Agent
interaction
Performance
Metrics/Trends
42
Copyright © 2019 Earley Information Science, Inc. All Rights Reserved.
www.earley.com 43
Copyright © 2019 Earley Information Science, Inc. All Rights Reserved.
www.earley.com
Copyright © 2019 Earley Information Science, Inc. All Rights Reserved.
www.earley.com 45
Customer question
Bot interpretation of
customer intent
46
Human agent ability to
correct intent
classification
47
Bot candidate response
with confidence score
Human agent accepts bot
answer
48
Customer receives
response
49
Customer’s follow on
question
Bot’s lower confidence
score for answer
Bot’s recommended
content
51
Human agent
correction
52
Content sent to
customer
53
Human agent selects
bot response for edit
54
Human agent updates
response
55
Updated response sent
to customer
Update becomes
part of bot
learning
56
57
Copyright © 2019 Earley Information Science, Inc. All Rights Reserved.
www.earley.com
TRAINING
AIDS
CALL LOGS CUSTOMER
PROFILE DATA
ANALYTICS
& ACTIVITY
SOCIAL
NETWORKS
DEMOGRAPHIC &
ETHNOGRAPHIC DATA
SENTIMENT
ANALYSIS
SERVICES &
OFFERS
CUSTOMER EXPERIENCE ENRICHED
BY KNOWLEDGE
BOT MATURITY &
SCALABILITY
Combining Platform Independent Knowledge with
Agent-Bot Collaboration for Scalability & Customer Experience
58
58
www.earley.com
www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved.
Diving Deeper: Enabling A Digital Architecture
59
CONTEXTUALIZED USER EXPERIENCE
Context Aware Information Architecture
Content Model Taxonomy Metadata
Structured
(Operational) Data
Unstructured
(Big) Data
Information Infrastructure
Marketing
Data
User
Data
Product
Data
Historical
Data
Operating
Content
Information Management Platforms
PIM DAM CMS ECM CRM ERP
Customer
Personalization
Content
Publishing
Site
Merchandizing
Product Info.
Management
Digital Commerce
Business
Intelligence
Knowledge
Management
Enterprise Search
Content
Management
Digital
Workplace
Copyright © 2019 Earley Information Science, Inc. All Rights Reserved.
www.earley.com
Always remember – “There is no AI without IA”
• It’s only AI if we don’t know how it works
• Simplicity is hidden complexity
• Clean data is the price of admission
• Identify user journeys, data sources and data owners
• Define governance, curation, and scalable processes
60
www.earley.com
www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved.
Suggested Resources
61
Allstate’s ABIe project case study
http://www.earley.com/knowledge/case-
studies/allstate%E2%80%99s-intelligent-agent-reduces-call-
center-traffic-and-provides-help
Cognitive Computing Consortium
http://www.cognitivecomputingconsortium.com/
Enterprise Search: 14 Industry Experts Predict the Future of
Search http://www.docurated.com/enterprise-
search/enterprise-search-14-industry-experts-predict-
future-search
Evaluating Enterprise Virtual Assistants
http://info.intelliresponse.com/rs/intelliresponse/images/O
pus_EvaluatingEnterpriseVirtualAssistants_Jan2014%20(2).p
df
Characteristics of Highly Effective Enterprise Virtual
Assistants
http://www.slideshare.net/intelligentfactors/characteristics
-of-highly-effective-enterprise-virtual-assistants
The Knowledge Graph and Its Importance for Intelligent
Assistance
http://opusresearch.net/wordpress/2016/01/12/the-
knowledge-graph-and-its-importance-for-intelligent-
assistance/
Making Intelligent Virtual Assistants a Reality
http://info.earley.com/make-intelligent-virtual-assistant-
reality-whitepaper
Cognitive Search – The Next Generation of Information
Access http://www.earley.com/blog/cognitive-search-
next-generation-information-access
Earley Executive Roundtable - Training the Robots:
Evolving Virtual Assistants and the Human Machine
Partnership http://info.earley.com/roundtable-virtual-
assistant-human-machine-partnership
Earley Executive Roundtable Understanding virtual agents
– what's needed to make them a reality?
http://info.earley.com/roundtable-intelligent-virtual-
agents-reality
Vendor Landscape: Knowledge Management For Customer
Engagement
https://www.forrester.com/report/Vendor+Landscape+Kn
owledge+Management+For+Customer+Engagement/-/E-
RES119672
Copyright © 2019 Earley Information Science, Inc. All Rights Reserved.
Thank You!
CONTACT US
Seth Earley
Founder & CEO
Earley Information Science
seth@earley.com
781-820-8080
62

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There's No AI Without IA (Information Architecture)

  • 1. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com GILBANE'S DIGITAL EXPERIENCE CONFERENCE TUESDAY, APRIL 30TH, 11:45 A.M. - 12:30 P.M. There's No AI Without Information Architecture (IA) Seth Earley, Founder & CEO, Earley Information Science seth@earley.com 781-820-8080 www.earley.com www.linkedin.com/in/sethearley
  • 2. Copyright © 2017 Earley Information Science, Inc. All Rights Reserved. 2 1994 YEAR FOUNDED. Boston HEADQUARTERED. 50+ SPECIALISTS & GROWING. Earley Information Science is a specialized information agency. We support measurable business outcomes by organizing your data, content and knowledge assets. Our proven methodologies are designed specifically to address product data, content assets, customer data, and corporate knowledge bases. We deliver scalable governance- driven solutions to the world’s leading brands, driving measurable business results. We make information more useable, findable, and valuable.
  • 3. Copyright © 2017 Earley Information Science, Inc. All Rights Reserved. 3 AWARDS & RECOGNITION 2018 100 Companies that Matter in KM 2017 100 Companies that Matter in KM 2016 100 Companies that Matter in KM 2015 100 Companies that Matter in KM 2014 100 Companies that Matter in KM 2014 Trend-Setting Products Award 2013 Trend-Setting Products Award 2008 Trend-Setting Products Award (Wordmap) 2013 Applied Materials’ added to InformationWeek 500 List of Business Innovators • “Cognitive Search Is Ready To Rev Up Your Enterprise’s IQ” • “Google-ize Your Site-Search Experience” • “Polishing Up Your Products — Why PIM Really Matters” • “Artificial Intelligence Solution Landscape” ANALYST MENTIONS • “Unlocking the Hidden Value of Information (Applied Materials)” 2015 KM Reality Award (Allstate Business Insurance, ABIe project)
  • 4. Copyright © 2017 Earley Information Science, Inc. All Rights Reserved. Further Reading/Future Reading The Problem With AI There’s No AI Without IA February 2020
  • 5. Copyright © 2017 Earley Information Science, Inc. All Rights Reserved. Three take aways 1. Artificial Intelligence is an evolution of technologies that have been around for decades 2. Artificial Intelligence requires a solid foundation in information management principles 3. Knowledge engineering can produce ROI today while preparing for an AI future 5
  • 6. Copyright © 2017 Earley Information Science, Inc. All Rights Reserved. AI as Evolution 6 Principles of machine learning have been embedded in software we have used for years
  • 7. 7 “ When [AI] finally works, it gets co-opted by some other part of the field. So, by definition, no AI ever works; if it works, it’s not AI. Source: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-825- techniques-in-artificial-intelligence-sma-5504-fall-2002/lecture-notes/Lecture1Final.pdf “
  • 8. 8 Spell check Speech recognition
  • 9. • Mitigation of compliance risks • Removal of Personally Identifiable Information (PII) • Removal of Redundant, Outdated and Trivial (ROT) content • Protection of intellectual property • Identification of patterns of fraud • Detection of customer sentiment • Prediction of credit risks • Feature extraction from product data Text Analytics, a long time staple of content management, is now called “AI”
  • 10. Copyright © 2017 Earley Information Science, Inc. All Rights Reserved. 10 combined with a sophisticated UX” Source: https://www.theregister.co.uk/2017/01/02/ai_was_the_fake_news_of_2016/ “The definition of “AI” has been stretched so that it generously encompasses pretty much anything with an algorithm” vast knowledge “What seems to be AI, is ,
  • 11. Copyright © 2017 Earley Information Science, Inc. All Rights Reserved. Managing vast amounts of knowledge requires a Knowledge Engineering approach 11
  • 12. Copyright © 2017 Earley Information Science, Inc. All Rights Reserved. The Customer Experience and Personalization
  • 13. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com How customer- oriented are we? 13 Personalization has been the big promise for the past 15 years. The problem is that this vision is still a long way from reality. What Customers Want… • Answers to their problems • The right product • Options and meaningful choices • Help with their goal • Assistance with a task • Recommendations for a solution • Expertise that they trust • Responses that move them forward • A “rewarding” experience • Convenience, speed, efficiency • Ease of doing business What They Actually Get… • Convoluted navigation • Search results that are difficult to filter • Too many choices • Endless phone menus • Call center reps w/o requisite knowledge • Confusing content • Marketing language that tries to sell them • Incomplete information • Frustrating, disconnected interactions • Impediments, lack of responsiveness • High friction processes
  • 14. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Promise of Personalization 14 Personalization has been the big promise for the past 15 years. The problem is that this vision is still a long way from reality. • High fidelity journey models reflecting audience goals based on buying stage • A multi dimensional audience model that describes their intent in data terms along their journey • Product data models aligned with user buying criteria • A messaging architecture to allow for refactoring of offering components • Componentized content that can be reassembled to test offer and message variations • Knowledge and insights from across the organization to articulate customer needs and company solutions Meaningful personalization requires
  • 15. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Product Information, Content and the Customer Journey Internal audiences need to easily find, share and reuse content, data and insights to support the external customer experience Merchandizers Product managers Category owners MARKETING PROMOTION / PLANNING PRODUCT DEVELOPMENT Product Data/Content Product Content / Product Assets PIM PRODUCT ONBOARDING PIM Manager Catalog Manager Merchandizer Product Information Management Campaigns Email Marketing Social media Promotions DEMAND GENERATION $ Marketing managers Marketing analysts CONTENT STRATEGY Editorial manager Content manager Category manager Product content Product assets Marketing plans ECOMMERCE PERSONALIZATION STRATEGIES Purchase history Demographics Interest profile Buyer persona CUSTOMER SUPPORT Call Center Agents Documentation Warranty Knowledgebase Content/data source Person/role Collaboration PROCESS Support managers K-base owner CUSTOMER SELF SERVICE Reviews Manuals Knowledgebase Regional managers Market Analyst Merchandizer Market data Regional demographics Store sales PROMOTIONS Collaboration, Insights and Knowledge Sharing Content Optimization Customer Journey
  • 16. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Intelligent Content Lifecycle Internal audiences need to easily find, share and reuse content, data and insights to support the external customer experience Merchandizers Product managers Category owners MARKETING PROMOTION / PLANNING PRODUCT DEVELOPMENT Product Data/Content Product Content / Product Assets PIM PRODUCT ONBOARDING PIM Manager Catalog Manager Merchandizer Product Information Management Campaigns Email Marketing Social media Promotions DEMAND GENERATION $ Marketing managers Marketing analysts CONTENT STRATEGY Editorial manager Content manager Category manager Product content Product assets Marketing plans ECOMMERCE PERSONALIZATION STRATEGIES Purchase history Demographics Interest profile Buyer persona CUSTOMER SUPPORT Call Center Agents Documentation Warranty Knowledgebase Content/data source Person/role Collaboration PROCESS Support managers K-base owner CUSTOMER SELF SERVICE Reviews Manuals Knowledgebase Regional managers Market Analyst Merchandizer Market data Regional demographics Store sales PROMOTIONS Collaboration, Insights and Knowledge Sharing Content Optimization Customer Journey Product Data Maturity Content Optimization Maturity Knowledge Engineering Maturity Customer Experience Maturity Monitored by Metrics and Governance Playbook to Track Progress, ROI and Course Corrections
  • 17. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. 17 Multiple Components of Personalization Content Customer Product Data Knowledge
  • 18. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Maturity of Capabilities Personalization programs based on machine learning and intelligent content are part of a larger ecosystem of tools, processes, methodologies and collaboration. 18 KNOWLEDGE ties together all of the other pieces – it is the human element of judgement, expertise and creativity that is harvested from experts and embedded in data models and processes. CONTENT to engage the customer must be findable, and it must be relevant in the moments that matter. Content components and snippets are the building blocks for machine optimized offerings CUSTOMER DATA needs to be consistent, harmonized from different systems and modeled to provide and respond to signals from interactions both upstream and downstream. PRODUCT DATA models must be complete and aligned with attributes and details that are important to the customer’s decision making criteria. METRICS DRIVEN GOVERNANCE measures ROI of projects and provides feedback for course corrections and fine tuning of user experience, product data, and content decisions and is the foundation for automation Maturity is required across all dimensions for long term success
  • 19. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Modeling the Customer Static Customer Data Dynamic Customer Data
  • 20. PRODUCT ARCHITECTURE COMPONENTIZED CONTENT CUSTOMER DATA FOUNDATION DELIVERING PERSONALIZED CUSTOMER EXPERIENCES AT SCALE What does it take to do this right? advanced services & expertise unified models standardized platforms & processes 20 • enriched customer journeys • product attribute model & corresponding taxonomies • data intake, clean-up, aggregation. • analysis, recommendation & decision making (ML, data science, human judgment) • process setup (continuous or periodic) • standard pipeline for insight delivery to marketing teams KNOWLEDGE & INSIGHTS • product data with e-catalog and display hierarchies optimized for customer journeys • back end product information onboarding process aligned with customer experience practices • metrics driven decision making • merchandizer collaboration with product and solution experts • configure price quote and recommendation tools aligned with user personas and pain points • product information management ecosystem aligned with rich media • cross sell and upsell relationships • merchandizing and solution bundles • optimized content structure • component architecture aligned with messaging architecture • content attribute model & corresponding taxonomies • omnichannel offer recommender • dynamic offer generator • content assembly based on offering architecture and baseline hypotheses tested against target outcomes • recombination tested continuously using changing messaging architecture • component content management system • content production workflows • content standards & governance • high fidelity customer journeys with augmentation and automation opportunities • customer attribute model & corresponding multi-dimensional audience taxonomies • profile standardization • pattern recognition • customer signal reconciliation across upstream platforms • machine learning development & training • customer data platform • customer data modeling • cross system normalization • metrics aligned data governance decision making
  • 21. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Using a “High Fidelity” Journey Map 21 I RENEW I INSTALL & I USE I SHOP & I BUY I’M AWARE Understand the customer journey Identify details of the customer Define content needed White Paper Product compare tool Installation guide Static Customer Data Dynamic Customer Data
  • 22. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. high-fidelity customer journey model What does it take to do this right? customer model I RENEW I INSTALL & I USE I SHOP & I BUY I’M AWARE 22 INTELLIGENT PERSONALIZATION Component content model User journey/customer model Product data model Knowledge architecture Static customer data: industry, role, interests Dynamic metadata identifies changing, real time. signals about customer goals and intent while they go through their journey Customer Data Platform Download white paper Product compare & purchase Download installation guide Open email & click offer Dynamic customer data Dynamic customer data: campaign responses, click through, recent purchases Delivering Personalized Customer Experiences – At Scale
  • 23. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. high-fidelity customer journey model Dynamic customer data customer model CMS and PIM I RENEW I INSTALL & I USE I SHOP & I BUY I’M AWARE Content 23 INTELLIGENT PERSONALIZATION Component content model User journey/customer model Product data model Knowledge architecture Static customer data: industry, role, interests Dynamic customer data: campaign responses, click through, recent purchases Customer Data Platform Top of funnel content White Paper Basic Widget Product New customer offer Download white paper 1 Delivering Personalized Customer Experiences – At Scale What does it take to do this right? Dynamic metadata identifies changing, real time. signals about customer goals and intent while they go through their journey
  • 24. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. high-fidelity customer journey model Dynamic customer data customer model CMS and PIM I RENEW I INSTALL & I USE I SHOP & I BUY I’M AWARE Content 24 INTELLIGENT PERSONALIZATION Component content model User journey/customer model Product data model Knowledge architecture Static customer data: industry, role, interests Dynamic customer data: campaign responses, click through, recent purchases Customer Data Platform Top of funnel content White Paper Basic Widget Product Middle of funnel content Product compare tool Deluxe Widget New customer offer New customer offer Download white paper Product compare & purchase 2 What does it take to do this right? Dynamic metadata identifies changing, real time. signals about customer goals and intent while they go through their journey Delivering Personalized Customer Experiences – At Scale
  • 25. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. high-fidelity customer journey model Dynamic customer data customer model CMS and PIM I RENEW I INSTALL & I USE I SHOP & I BUY I’M AWARE Content 25 INTELLIGENT PERSONALIZATION Component content model User journey/customer model Product data model Knowledge architecture Static customer data: industry, role, interests Dynamic customer data: campaign responses, click through, recent purchases Customer Data Platform Top of funnel content White Paper Basic Widget Product Middle of funnel content Product compare tool Deluxe Widget Post purchase content Installation guide New customer offer New customer offer Download white paper Product compare & purchase Download installation guide 3 Delivering Personalized Customer Experiences – At Scale What does it take to do this right? Dynamic metadata identifies changing, real time. signals about customer goals and intent while they go through their journey
  • 26. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. high-fidelity customer journey model Dynamic customer data customer model CMS and PIM I RENEW I INSTALL & I USE I SHOP & I BUY I’M AWARE Content 26 INTELLIGENT PERSONALIZATION Component content model User journey/customer model Product data model Knowledge architecture Static customer data: industry, role, interests Dynamic customer data: campaign responses, click through, recent purchases Customer Data Platform Top of funnel content White Paper Basic Widget Product Middle of funnel content Product compare tool Deluxe Widget Post purchase content Installation guide Super Widget Post purchase nurture content User tips Promotion for upsell New customer offer Existing customer offer New customer offer Download white paper Product compare & purchase Download installation guide Open email & click offer 4 What does it take to do this right? Dynamic metadata identifies changing, real time. signals about customer goals and intent while they go through their journey Delivering Personalized Customer Experiences – At Scale
  • 27. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Personalization Example: Cross/Up-sell 27
  • 28. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 28 AI-driven personalization requires • meaningful knowledge and content assets • analytics to understand and model customer behaviors • consistent architecture across product information, customer and content models • knowledge of problems to anticipate needs AI promises to help businesses meet / anticipate customers’ needs Product relationships based on knowledge of solutions Browse history and other constraints (products available in a region) Configurations and answers to questions Product – review relationships: what to show, sorting rules CHATBOTS REVIEWS CUSTOMER ALSO VIEWED RELATED PRODUCTS
  • 29. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Watson Intelligent Assistant
  • 30. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Example: Amazon Alexa Skills Skills are tuned for specific use cases. They use AI, but are backed up by a vast set of APIs and knowledge. Skills surround a set of limited use cases (e.g., buy a movie ticket, order a pizza, etc.), and all the AI, content and engineering are tuned for those specific interactions. 30
  • 31. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com Complex Advisory/ Diagnosis Product Support Product Configuration Judgment Based Domain Complexity Transaction Support Knowledge Retrieval Information/ status inquiries/ order processing Task/dialogue Complexity 31 31 Task Complexity versus Domain Complexity “Helper bots” “Configuration bots” “Transaction bots” Don’t start here High domain complexity/ High task complexity
  • 32. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Basic Search Engine Knowledge Portal Virtual Agent Intelligent Assistant Knowledge Base Any text Multiple sources Multiple sources, separate Ontologies and schemas Domain specific Highly curated sources Dynamic. Info enrichment improves with interaction Search Interaction Keyword or full text query Full text query or Faceted Exploration Query, explore facets Offers related info Implicit query / recommends based on users’ history Information Architecture None necessary, but Improves with metadata Ontologies, clustering, classification Ontologies, clustering, classification, NLP Ontologies, clustering, classification, NLP, personalization User Experience Search box, documents list Role-Based Conversational Conversational, personalized, contextual Enabling Technology Search box, documents list Search, classification, databases NLP, search, classification Process engines NLP, search, classification Machine Learning IA of Increasing Importance Moving to AI Increasing Functionality / Increasing Information Richness 32
  • 33. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com 1-Unpredictable 2-Aware 3-Competent 4-Synchronized 5-Choreographed Metadata Processes Chaotic tagging practices with no taxonomy Business metadata in use, with controlled vocabularies in isolation Enterprise taxonomy is replicated across sites and platforms, and metadata use is manual but consistent Content schemas with clear metadata requirements are used effectively to group, aggregate, sort, and filter assets in the interface Contextualized and/or personalized metadata with auto-population of values and ongoing quality audits IA / User Experience Haphazard creation of content and sites with inconsistent content models and poor site experience More consistency across sites and platforms, but with little cross-site structure Consistency across sites in how content is displayed, particular in regards to metadata relevant to specific groups and audiences Enforced metadata standards provide a context-dependent user experience for content management and receipt, in a Web-page-like experience Integration of structured and unstructured information from multiple systems dynamically presented to support user tasks in an application-like experience Search Integration Random document generator Some tuning of search with individualized content tagging Scope-able search with consistent but uncontrolled tagging Faceted search with taxonomy- governed tagging; metadata available for use within the search experience Search-based application with associative relationships (related search), tuned algorithms, facets, and metrics-driven use cases User Proficiency & Content Practices Poor or minimal usage, lack of awareness of capabilities or content practices Early adopters and power users using out-of-box features, little control of content Departmental collaboration with basic content control Cross-team project-oriented collaboration with information lifecycle management Automated workflows and reporting for compliance with enterprise content standards Governance Information sprawl, lack of vision, no intentional decision making, minimal centralized architectural support Awareness of challenges; activity is monitored but not constrained; analytics data exist but are not reviewed and/or managed consistently Assigned responsibilities, oversight and communications infrastructure in place, best practice documentation is published and available Intentional decision making, resource allocation, change controls in effect; quality metrics are regularly reviewed Agenda-driven business leadership and stakeholder engagement to effect continuous process improvement through measurement and collaboration Technology Business groups use their own organically developed approaches; business requirements are undocumented. No global search. Business groups follow improvised processes within established tools; multiple work-arounds used to improve information findability Global systems processes are supported by home-grown or external systems for tracking, managing, and finding content Dedicated, managed systems enable multiple distinct workflows and processes that business groups understand, follow, and find information Systems are architected to share information within and across teams and divisions, to find content with high relevancy Building Maturity current target priorities 33
  • 34. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com The Knowledge Management challenge is usually put into language that confuses the issue: Vendors say that they need to “train the AI” What do you “train the AI” with? …high value knowledge assets (quality data and curated content) The Knowledge Management Challenge 34
  • 35. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com Copyright © 2018 Earley Information Science, Inc. All Rights Reserved. THE BOTTOM LINE “Training data” is the biggest challenge to artificial intelligence program development The knowledge that artificial intelligence driven bots need is the same knowledge that humans need By building knowledge management programs to support employees, the organization is preparing for improved access through emerging AI and Bot technologies 35
  • 36. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com Copyright © 2017 Earley Information Science, Inc. All Rights Reserved. EIS Bot Factory workstreams • Content type and variable definitions • Classification schema design • Feature engineering • Vocabulary development • Associative relationship mapping • Deconstruction and componentization of FAQ’s, troubleshooting guides, reference materials, e-learning modules, etc. • Content refactoring and component tagging • Integration of component models with user experience • Crowdsourcing of phrase variations for intent triggers (utterances) • Classification of intent using customer issue and query data • Entity extraction training and tuning • Escalation and handoff model • Feedback workflow design: utterance, intent and knowledge • Success metric design • Governance and accountability model • Speech to text conversion • Text mining/ analytics on call logs / support content • Search analytics • User journey mapping • Scenarios and use cases • Identification of repeatable, unambiguous processes • Deconstruction of user journeys into dialogue components • Precoordinated intent design • Disambiguation models • Intent entity extraction • dialogue context tagging model PROCESS ANALYSIS DIALOGUE DESIGN & INTENT CLASSIFICATION CONTENT ANALYSIS, DOMAIN MODELING & ONTOLOGY DESIGN COMPONENTIZATION OF KNOWLEDGE CONTENT TRAINING DATA CORPUS DEVELOPMENT HYBRID LEARNING & CONTINUOUS IMPROVEMENT MODEL CREATION 36
  • 37. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com Standardized/Normalized Content is Portable and Reusable 37 Standardized domain specific schemas for reuse Field 1 Field 2 Field n … Field 1 Field 2 Field 3 Field n … ELearning, FAQ’s, Troubleshooting charts, support articles Componentized content Tagging for ingestion Componentized content can be repurposed across tools and technologies Improved CSR Information Access Faster time to value for all information access scenarios Portability across AI and Chatbot systems Improved customer self service Metrics aligned with specific content performance COMPONENTIZATION OF KNOWLEDGE CONTENT
  • 38. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Agent: “I need to determine liability coverages for employee actions for a collection agency in Massachusetts” Agent: “Hi, I need some help with a policy” Semantic deconstruction of utterance Bot: “OK. Can you tell me what kind of policy?” Topic = “liability coverage” Product = “employee practices liability” Nature of business = “collection agency” Region = “Massachusetts” Content type = “Guideline” Entity derivation Context derivation Audience = “Certified agent” Topic Product Nature of business Region Content type Audience Faceted retrieval from knowledge base Returns content tagged with appropriate metadata 38
  • 39. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com Domain modeling, knowledge mapping ABI manual maps – Concept Topics Job Aid maps - Process Topics FAQ maps - QA Pairs 39 CONTENT ANALYSIS, DOMAIN MODELING & ONTOLOGY DESIGN
  • 40. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com Allstate Strategy ABI Objectives ABI Revenue Call Center Traffic Agent Book of Business Business Processes ABIe Effectiveness Content Quality DITA Content & Taxonomy ABIe Stats, Google Analytics Search Test Cases Processes enable objectives L I N K A G E Search Quality Grow top line Allstate revenue Data supports (and measures) processes Objectives align with strategy Data Sources Working here (search, content, IA, taxonomy, metadata, etc.) Measuring here (Process measures) Measuring here (ABI KPIs – Results Measures) Aligning enterprise strategy with virtual assistant metrics CEO: “Show me how will this project increase our revenue.” ABIe Utilization HYBRID LEARNING & CONTINUOUS IMPROVEMENT MODEL CREATION
  • 41. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com Search Analysis • Identify new taxonomy terms • Identify new auto-completes • Identify missing content Behavior Analysis • Analyze & Remediate Top Best Bets • Analyze & Remediate Top Searches • Identify missing content Utilization Analysis • Correlate ABIe usage to call center volumes • Gather and analyze VOC • Identify action plans Content Analysis • Measure compliance with editorial guidelines • Measure compliance with tagging guidelines • Remediate issues Metrics-driven Governance Processes HYBRID LEARNING & CONTINUOUS IMPROVEMENT MODEL CREATION
  • 42. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com Contextual customer information Customer/Bot/Agent interaction Performance Metrics/Trends 42
  • 43. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com 43
  • 44. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com
  • 45. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com 45
  • 46. Customer question Bot interpretation of customer intent 46
  • 47. Human agent ability to correct intent classification 47
  • 48. Bot candidate response with confidence score Human agent accepts bot answer 48
  • 50. Customer’s follow on question Bot’s lower confidence score for answer
  • 54. Human agent selects bot response for edit 54
  • 56. Updated response sent to customer Update becomes part of bot learning 56
  • 57. 57
  • 58. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com TRAINING AIDS CALL LOGS CUSTOMER PROFILE DATA ANALYTICS & ACTIVITY SOCIAL NETWORKS DEMOGRAPHIC & ETHNOGRAPHIC DATA SENTIMENT ANALYSIS SERVICES & OFFERS CUSTOMER EXPERIENCE ENRICHED BY KNOWLEDGE BOT MATURITY & SCALABILITY Combining Platform Independent Knowledge with Agent-Bot Collaboration for Scalability & Customer Experience 58 58
  • 59. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Diving Deeper: Enabling A Digital Architecture 59 CONTEXTUALIZED USER EXPERIENCE Context Aware Information Architecture Content Model Taxonomy Metadata Structured (Operational) Data Unstructured (Big) Data Information Infrastructure Marketing Data User Data Product Data Historical Data Operating Content Information Management Platforms PIM DAM CMS ECM CRM ERP Customer Personalization Content Publishing Site Merchandizing Product Info. Management Digital Commerce Business Intelligence Knowledge Management Enterprise Search Content Management Digital Workplace
  • 60. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. www.earley.com Always remember – “There is no AI without IA” • It’s only AI if we don’t know how it works • Simplicity is hidden complexity • Clean data is the price of admission • Identify user journeys, data sources and data owners • Define governance, curation, and scalable processes 60
  • 61. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Suggested Resources 61 Allstate’s ABIe project case study http://www.earley.com/knowledge/case- studies/allstate%E2%80%99s-intelligent-agent-reduces-call- center-traffic-and-provides-help Cognitive Computing Consortium http://www.cognitivecomputingconsortium.com/ Enterprise Search: 14 Industry Experts Predict the Future of Search http://www.docurated.com/enterprise- search/enterprise-search-14-industry-experts-predict- future-search Evaluating Enterprise Virtual Assistants http://info.intelliresponse.com/rs/intelliresponse/images/O pus_EvaluatingEnterpriseVirtualAssistants_Jan2014%20(2).p df Characteristics of Highly Effective Enterprise Virtual Assistants http://www.slideshare.net/intelligentfactors/characteristics -of-highly-effective-enterprise-virtual-assistants The Knowledge Graph and Its Importance for Intelligent Assistance http://opusresearch.net/wordpress/2016/01/12/the- knowledge-graph-and-its-importance-for-intelligent- assistance/ Making Intelligent Virtual Assistants a Reality http://info.earley.com/make-intelligent-virtual-assistant- reality-whitepaper Cognitive Search – The Next Generation of Information Access http://www.earley.com/blog/cognitive-search- next-generation-information-access Earley Executive Roundtable - Training the Robots: Evolving Virtual Assistants and the Human Machine Partnership http://info.earley.com/roundtable-virtual- assistant-human-machine-partnership Earley Executive Roundtable Understanding virtual agents – what's needed to make them a reality? http://info.earley.com/roundtable-intelligent-virtual- agents-reality Vendor Landscape: Knowledge Management For Customer Engagement https://www.forrester.com/report/Vendor+Landscape+Kn owledge+Management+For+Customer+Engagement/-/E- RES119672
  • 62. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Thank You! CONTACT US Seth Earley Founder & CEO Earley Information Science seth@earley.com 781-820-8080 62