Emerging AI Capabilities and KCS?

April 2018

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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

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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

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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