Predictive Customer Engagement
Overview
A Work in Progress
September 2016
The Opportunity?
How can we provide information
that we have …
that customers would value…
but don’t know to ask for?
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Data Driven
Predictive Customer
Engagement
• Know a lot about the customer:
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Where are they in the life cycle (intent)
Business objectives
What do they have: products, solutions, services
Individuals associated with them (people)
• Know a lot about us:
– Our products, solutions, services
– Individuals
• Past and current work, engagements, interactions
• Based on that make relevant, timely recommendations or
actions
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Data Assets
Vendor
Offerings
Work or
Tasks
People
Profiles
Knowledg
e
Articles
Customer
Entity
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Events from Everywhere
People
Devices, Systems (IoT)
Environment
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The Event Loop
(A Loop)
Events
Listening Posts
Communication
Mechanisms
Product
/
service
s
Work
Proactive
Output
People
Knowle
dge
articles
Compa
ny/orga
nization
Discovery
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Four Layers
Presentation/Notification
Analysis, Rules, actions
Product/
services
Work
People
Knowledg
e articles
Company/o
rganization
Associations
Data Lake
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Modeling, Rules, and
Recommendations?
• Our ability to relate the five data assets to
each other is a critical enabler to creating:
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Pattern detection
Predictive models
Rules-based recommendations
The “know me factor”
Intelligent matching (people)
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Views and Associations
• Vendor Offerings: Product and services
– What customers have it, who in the account is associated with it, what
work (cases or web sessions) have been initiated about it?
• People Profiles
– What is this person’s role(s), what are their preferences, what
customer(s) are they related to, what products are they associated
with, what work (cases, articles, web sessions) are they involved with?
• Knowledge Articles
– What are the units of knowledge?
– For a given unit of knowledge, what products/services are associated
with it, what people are associated with it, what customers are the
people associated with?
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Views and Associations
• Customer Entity
– What do they have installed, how is it configured, who in the account is
associated with it, what are their preferences for notification, what type
of messages and channel?
• Work or Tasks
– What are the units of work?
– For a given unit of work, what product/service are associated with it,
what customer are they associated with, what people are associated
with the work units, what is the role/persona of the people associated
with the work?
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An Architecture
Managers
Customers
CSMs
Executives
Presentation
Analysis, Rules
Internet of
Things
Four
Layers
Associations
Data Lake
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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 – Collection and storage of data elements from
lots of different places, data warehouse, “data lake”
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Methods and Technologies?
What are members using? (partial list)
Presentation – Tableau, QlikView, Sharepoint,
D3JS/AngularJS
Analysis, Rules – Tableau, QlikView, Pega, R, Coveo
Associations – Wordstat, IBM Watson, Coveo
Data –Hadoop, SQL
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The Improve Loop
(B Loop)
Event Detection
Effectiveness
Impact
Assessment
Event
Product
/
services
Work
Engagement
Effectiveness
Asset
Quality
People
Knowle
dge
articles
Compa
ny/orga
nization
Action
Rules
Effectiveness
Analysis
Effectiveness
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The Role of the Data Scientist
in Service Innovation
Objective: predict or anticipate value co-creation
opportunities
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Cross functional perspective
Sources of data
Data collection
Data organization and classification
Analysis techniques
Modeling and rules development
Assessment of relevance and accuracy of rules outcomes
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