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Emerging AI Capabilities and KCS?
April 2018
2
A Good Outcome
• A little context – Definition of AI
– Overview of capabilities
• Review member activities and experience with AI
• Discuss potential areas of applicability for KCS
3
Definition of AI – All The Things
Deep Learning
Natural Language Processing
Text Analytics
Facial Recognition Cognitive
Computing
Neural Networks
Machine Learning
Computer Vision
Autonomous Machines
“AI is whatever hasn’t been done yet” wikipedia
Speech Recognition
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An Architecture
Presentation
Analysis, Rules
Associations
Data Lake
CSMs Executives
Managers
Customers
Four Layers
Internet of Things
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An Architecture
Presentation
Analysis, Rules
Associations
Data Lake
CSMs Executives
Managers
Customers
Data Scientists • Techniques • Tools Internet of
Things
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Four Layers
Presentation – Data visualization for humans, executables for machines Analysis, Rules – Pattern recognition and recommendations, business rules engine Associations – Ability to relate: people, knowledge, company, work, products/services Data Lake – Collection and storage of data elements from lots of different places, data warehouse, “data lake”
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Methods and Technologies?
Presentation – Tableau, QlikView, Sharepoint, D3JS/AngularJS Analysis, Rules – Tableau, QlikView, Pega, R, Coveo Associations – Wordstat, IBM Watson, Coveo Data Lake –Hadoop, SQL
What are people using (partial list)?
Big Data Landscape
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AI Capabilities – The Big 4
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Clustering and Classification “Which content type matters most to our different cohorts?”
Prediction “Based on what I know about this customer and associated
cohorts, which content should be presented next in this session?”
Recommendation “Each engagement activity is an opportunity to inform,
engage, grow or transact with our customers – which option is the right one to prosecute at this time?”
Optimization How can we reduce the effort of the customer and the steps
needed to realize successful outcomes?
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Classification And Clustering
Applications: • Customer segment
identification • Document classification • Customer journey
identification • Sentiment detection • Image recognition • Speech to text
Given what I know about this customer and their interactions, a trained algorithm tells me what categories (customer segment or cohort) they belong to.
From Ryan Barrymore
10
Prediction
Applications: • Customer lifetime value
prediction. • Revenue forecasting. • Predict retention rate. • Predict time to failure for
machine maintenance.
Given a large set of multi-dimensional, rapidly changing data, generate a function which will predict an unknown numerical result, given a set of inputs.
From Ryan Barrymore
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Recommendation
Applications: • Product
recommendations • Offer selection • Media
recommendations • Content curation • Knowledge base
assistance
Given a large set of purchase or affinity data for many customers, generate a function which will recommend the most likely product a given customer would purchase, based on their historical activity.
From Ryan Barrymore
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Optimization Recommendation
Applications: • Identify best offers to maximize
conversion rate. • Choose best outbound interaction
to maximize retention rate. • Select curated content which
maximizes customer satisfaction. • Given media costs, select offers
and factors which minimize cost to acquire new members.
Among a set of possible choices, indicate which set of choices maximizes or minimizes a particular effect.
From Ryan Barrymore
Member Experiences
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PTC’s Use Cases
1. Intelligent Case logger – ‘Dylan’ - Peter Case presented on this in Monterey
2. Linking Knowledge Articles to training Classes 3. Personalized Knowledge recommendation 4. Unsupervised Knowledge Extraction from Text 5. Creation of an Enterprise Knowledge Graph (tied to #4) 6. Red Account Escalation Prediction 7. Churn Prediction
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Oracle’s Case Closure Wizard
• Article to case relevance indicator
• Recommend phrases/text to add to article
based on the case content (modify)
Weak Strong
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DELL/EMC
• Service analytics for account management – Visualization
• Proactive system monitoring
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The Opportunity?
• AI’s applicability to KCS?
• Can we automate certain tasks?
• Can we make it easier for the knowledge worker to do the right thing?
• What are the most valuable areas to apply AI?
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Artificial Intelligence and KCS
• KCS – Automated link accuracy assessment
– Creating structured knowledge articles from freeform text
– Sophisticated Evolve Loop analysis: patterns, trends, velocity
– Intelligent search (based on who is searching)
– Bots as first point of contact
– Self-service/automated assistance based on user click stream or command sequence in the application (SaaS or instrumentation in the product)
Solve Loop
Evolve Loop
Knowledge
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Where Can We Apply AI In KCS?
Capture
Structure
Reuse
Improve
Solve Loop Leadership &
Communication
Performance Assessment
Process Integration
Content Health Evolve
Loop
Buddy Valastro, President & CEO, Carlo’s Bakery
• Upcoming Consortium Events (www.serviceinnovation.org)
• Annual Member Summit, Napa, CA, March 19-21, 2018
Creating Space to Think
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