Earley Executive Roundtable on Customer Analytics

Copyright © 2015 Earley Information Science1 Copyright © 2015 Earley Information Science
Earley Executive Roundtable
Series
Developing a Customer Analytics Roadmap
July 23, 2015
Presented by
Seth Earley
CEO
Earley Information Science
Click to watch the recorded
session of this presentation

Copyright © 2015 Earley Information Science2
Today’s Agenda
• Welcome & Housekeeping
– Session duration & questions
– Session recording & materials
– Take the survey!
• Introduction
– Dave Zwicker
• Panelist Introductions
– Seth Earley, CEO, Earley Information Science (@sethearley)
– Dr. Stephen Andriole, Author, IT Thought-leader, Professor (@SteveAndriole)
– Mark Langsfeld, Co-founder & President, ListenLogic (@Langsfeld)
• Panel Discussion
• Questions & Answers
• Join the conversation: #earleyroundtable

Developing a Customer Analytics Roadmap
Core Concepts

CX Analytics – Why Companies Care
Better customer insights lead to better business outcomes:
• Sustained growth in customer acquisition
• Increases in revenue per customer
• Decreases customer acquisition cost
• Reductions in customer churn
• Enhancements to product offerings
Research findings from Gartner:
• 89% of companies will compete based on customer experience by 2016
• 65% have the equivalent of a chief customer officer (office of the CCO)
• 18% of marketing budgets in 2014 were spent on customer experience
• Customer experience is the top innovation project for 2015

Customer Experience Lifecycle
Buyers become aware of
problems and pain points.
They research products
that address their needs.
They establish buying criteria
and make purchase decisions.
Customers develop ongoing
relationship with the brand.
They request help with
products or purchases.
Customers use products
and develop impressions.
Analytics must provide a 360 degree view of the customer experience
Customer Lifecycle Published by Forrester

Data Sources for CX Analytics
Structured Data:
Transactions, products
sales, customer data,
service histories
Unstructured Data:
Social feeds, click streams,
web logs, call center logs
WHAT? WHY?

Analytics Today and the Road Ahead
• How do most companies use CX analytics today?
– Use cases
– Maturity model
– Predictive analytics
• What are the major challenges to overcome?
– Technological: Complexity and maturity of tools and technologies
– Organizational: People, skillsets, business & IT alignment
• What are the Infrastructure requirements?
– Big data technology (storage, applications, operations and management)
– Information architecture (taxonomy, metadata, data quality, governance)
• How to take action
– Learn more
– Self-assess
– Create a roadmap

Today’s Panel of Experts
Seth Earley, Dr. Stephen Andriole, Mark Langsfeld

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

The Information Ecosystem |
Experience and Enterprise Processes
Learn Buy Get Use Pay Support
Marketing Sales Distribution Service Finance Support
Customer Experience
Enterprise Processes: Departments/Functional Areas
• Event Management
• Webinar tools
• Promotion
management
• Social media
• Marketing resource
management
• Inventory
Management
• Supply chain
• Logistics and
Distribution
• Point of sale
systems
• Ecommerce
• CRM
• Web content
management
• Sales Management
• Marketing resource
management
• Knowledge base/
Unsupervised
support
• On line
documentation/
help systems
• Ecommerce
• CRM
• Billing system
• Web content
management
• ERP/ Accounting
• Credit card
authorizations/ EFT
• CRM
• Knowledgebase/
Unsupervised
support
• On line
documentation/ help
systems
• Call center call
tracking
• Trouble ticketing
The nature of organizational processes leads to islands of information
and fragmentation of systems and an inability to have a 360 degree
view of the customer

Customer Type
• Consumer
• Business
• Non-Profit / Public
Demographics
• Age
• Gender
• Language
• Location
• Income level
Master Data
• Account
• Names
• Address
• Contact phone
Account Details
• Products
• Service Plans
• Billing
• Rate Plans
• Credit Information
Relationship Details
• Trouble Tickets
• Call History
• Account access details
Content
Preferences
• Product
Updates
• Technical
• Communities
• Topics
• Offerings
Equipment
• Devices
• Configurations
How do we describe a customer?
@sethearley

Behavior and subjective attributes
Social Graph
• LinkedIn Profile
• Facebook Information
• Twitter Feeds
Marketing
• Web site score
• Email opens
• Campaign history
Strategic
Segmentation
• High value
• High growth
Dynamic Segmentation
• Historic: Purchase history, web site actions,
aggregate sales
• Predictive: Needs analysis, needs aggregation
/ clustering
Social Media
• Forum discussions
• Participatory marketing
• Social conversations
Loyalty Attributes
• Predicted lifetime
value
• Likelihood to
recommend value
• Length of relationship
Behavioral
Segmentation
• Purchase Behavior
• Motivational
Behavior
@sethearley

@sethearley
A Single View of the Customer (SVoC) is based on:
• Harmonized attribute models
• Integrated data across systems and processes
• Consistent terminology, language and metadata
• Mature data quality and governance
• Advanced BI and analytics capability
Consistent, quality customer data is foundational
to a contextualized user experience:
• personalization
• next best product or action
• cross sell and upsell
• targeted offers
Integrated Digital Marketing means reaching and
engaging with the customer at the right time with
the right information in the right context to move
them through the buyer journey

Social Media Monitoring and Sentiment Analysis
Function Driver Role of Analytics Benefit
Monitor Determine effectiveness of
marketing and value of
participation in social media
through search metrics, blog
mentions, conversations on social
networks
Essential to tailor sentiment
analysis to specific
terminology of your
organization
Capturing the nature of
conversations, forms the
basis for engagement
strategy. Ability to react in
near real time to changes in
marketplace
Engage Need to be active participant in
the conversation. Connecting with
customers through appropriate
channels. Make contribution
based on insight captured
through monitoring activities.
Proper use of key terminology
that will be picked up by
search engines, hash tags on
social networks, manage
categories of interactions for
brands and products
Ability to impact
conversations positively, head
off problems that are
developing in the
marketplace, direct
traditional messaging to
communicate with key
influencers
Measure Develop metrics program and
analytics. Feedback mechanisms
to measure if activities and
interventions are moving toward
desired goals.
Required for metrics program.
If developed correctly for
monitor and engage
activities, will flow through to
metrics
Hard data on effectiveness of
social media. Ability to direct
effort to needed categories of
content and products

Integrated Experience Across Channels | Unifying Access
Search/Tagging/Taxonomy UIA Integration Framework
BI
Integration
Auto categorization/
Clustering
Entity
Extraction
Faceted
Search
Semantic
Search
Customer Relationship Mgt Customer Support
ERP System
Ontology
Navigation
Knowledgebase
ECommerce
Social Media
TextAnalyticsToolsandProcessing
Access Roles
Access Mechanisms
Customer
Service
In Store
Customer
Web Site
Visitor
Telesales
Customer
Prospective
Customer

Structured Data, Structured Content, Unstructured Data,
Unstructured and Semi and Structured Content
Structured Data
• Sourced in relational
databases
• Stored in tables that are
related to one another
Unstructured Data
• Sometimes called semi
structured data
• Can be processed to
parse out specific fields
• Click streams, web logs,
search logs, etc.
• Common form of Big
Data
Unstructured/ Semi
structured Content
• Free form text
• Email, web pages, social
media, wikis, intranet
content, file shares.
Semi structured if
contains metadata
Structured Content
• Componentized content
• Well tagged
• Structured for reuse
• Contracts, help data,
technical documentation

Example Classes of Tool and Integration
Issues
Tool/technology
class
Description Integration Challenge Example
Marketing Resource
Management
Systems that allow asset
reuse and streamline
production workflow
Embedding metadata in assets as
they move from system to system,
consistency of terminology for
component and composite assets
Integration of Digital Asset Management
with Creative Workflow and editing tools
Customer
relationship
management
Applications that provide an
integrated view of customer
interactions
Ability to consolidate and roll up
different activities that are
fundamentally different
CRM systems consolidate information
from a variety of subsystems: Email
management, event management,
customer support, collateral fulfillment .
Document and web
publishing
Moving content from one
application to another,
syndication of external
content
Inconsistent metadata standards,
inconsistent vocabularies, lack of
context for user
Content migration from legacy
applications to new systems or
syndication of content to a channel
partner’s web site
Federated/
Integrated Search
Enterprise systems that
search across structured and
unstructured content and
data
Inconsistent terminology from one
content system to another
Inconsistent terminology between
structured and unstructured
applications
Content management system term = SOW,
Document management system =
Statement of Work, Customer
relationship management system = Work
Order,
Transaction
processes
Registration or commerce
types of applications
Inconsistent description of customer
and processes from one system to
another
Registration in a web application needs to
create a new account profile in another
application
Data Warehousing Extraction/Translation and
Load processes
Format and conversions required to
normalize data across systems
Business intelligence applications to
analyze sales transactions over time

EIS Reference Architecture

Dr. Stephen Andriole
• Expert on the development, application and management of information
technology and analytical methodology to all varieties of problems in
government and industry
• He is currently the Thomas G. Labrecque Professor of Business
Technology at Villanova University in the Villanova School of Business
(VSB)
• Previously, was the Director of the Cybernetics Technology Office of the
Defense Advanced Research Projects Agency (DARPA) where he
supported the development of the ARPANET, spatial data base
management at the MIT Media Lab and AI @ Carnegie-Mellon & Yale
• Also, previously, was the SVP & CTO of both Safeguard Scientifics, Inc.
and Cigna and has been a founder, co-founder and CEO of several
technology consulting companies
• Has published over 30 books and 500 articles, monographs, reports and
book chapters. Most recently -- Ready Technology: Fast-Tracking New
Business Technologies (Taylor & Francis, 2014)
• He received Honorary Doctorate from LaSalle University in October 2000
for his accomplishments in information technology.
Thought-leader, Author,
Professor
Villanova University

Why Data Integration is Important
• The integration of structured and unstructured data enables the most
powerful BI/Analytics solutions: the 360 view
• But without UDA, descriptive and predictive analytics is incomplete and
therefore impossible: the market requires a comprehensive, adaptive and
real-time full-view solution
• Many vendors offer mature structured data analytics (SDA) but unstructured
data analytics (UDA) and integration capabilities that don’t scale, especially
in real-time
For More Information, See:
http://www.forbes.com/sites/steveandriole/2015/03/05/the-other-side-of-analytics/

Worldwide Corporate Data Growth
-
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
2013 2014 2015 2016 2017 2018 2019 2020
Structured Data Unstructured Data
Source: IDC The Digital Universe, Dec 2012
80% of Data Growth is Unstructured
Exabytes

All-Data Analytics
Unstructured Data
Structured Data
Internal Sources
• Email
• Call CenterTransscripts
• Forums, Blogs …
External Sources
• Social Media
• Forums, Blogs
• Open-End Surveys …
Internal Sources
• Products, Sales
• Point of Sale
• Service Tickets …
External Sources
• Industry Research Data
• Financial Market Data
• Competitor Data …
Text Mining &
Human Analysis
Real-Time
Dashboards & Alerts
Reports
Automated Feeds
to Other Systems
Use Cases:
- Business Intelligence
- Predictive Analytics
- Customer Experience
- Risk Sensing
- Innovation
- Market Research
- Customer Engagement
- Competitive Intelligence

Mark Langsfeld
• Expert in unstructured big data analytics and business
intelligence strategy
• Featured in publications like Smart Data Collective, PM360,
Pharmaceutical Executive, AMEX Open Forum and
International Journal of Competitive Intelligence and Business
Strategy
• Advises leading enterprises on risk sensing, consumer
insights, intelligence strategy and data integration
• Prior to co-founding ListenLogic, was Co-Founder of
Mediagistics, a search analytics and arbitrage company
• Previously, was Vice President of New Products at Move, Inc.
and an Analyst at BT Alex Brown in the Real Estate Investment
Banking Group
• Graduated from the University of Rochester and completed
his studies at the Wharton School
Co-founder & President
ListenLogic

Structured/Unstructured Predictive Analytics
SwitchingRiskTargeting
Unstructured
Survey Data
Unstructured
Social Data
Unstructured
Call Center
Taxonomy
Classification
Structured
Sales Data
Customer Data Categorized,
Normalized & Enriched
Structured
Internal Data
INPUTS
OUTPUT
Predictive Analytics
Combine Unstructured Customer Data with Structured Data to Understand
“What” is Happening in the Market & “Why” It Is Happening
PricingRetentionCust Exp
UpsellingR&DSegments
++ + +

Unstructured
Data Themes
vs. Units Sold
Sales
Launch
Pre-launch
Consumer
Interest
Customer Experience
Sales
Launch
Pre-launch
Consumer
Interest
Unstructured data can be correlated with sales data. The following example shows significant correlations
were found between consumer interest in the video game launch vs. units sold.
Unstructured/Structured Analytics

Panel Discussion
Mark Langsfeld
Co-founder & President
ListenLogic
Seth Earley
Founder & CEO, Earley
Information Science
Dr. Stephen Andriole
Thought-leader, Author,
Professor
Villanova University

Suggested Information Resources
• Earley Information Science http://www.earley.com/ideas-insights
• Steve Andriole http://www.forbes.com/sites/steveandriole/
• CMS Wire http://www.cmswire.com/
• ITProfessional http://www.computer.org/web/computingnow/itpro
• Forrester http://blogs.forrester.com/
• Gartner http://blogs.gartner.com/digital-marketing/
• MeasuringU http://www.measuringu.com/
• Online Behavior http://online-behavior.com/
• Breakthrough Analysis http://breakthroughanalysis.com/
• Customer experience report http://www.customerexperiencereport.com/
• MYcustomer.com http://www.mycustomer.com/
• CustomerThink http://customerthink.com/
• Harvard Business Review (articles):
“Lessons from the Leading Edge of Customer Experience Management”
https://hbr.org/resources/pdfs/tools/SAS_Report_April2014_webview.pdf
“Advancing the Customer Experience”
https://hbr.org/resources/pdfs/comm/disney/advancing_the_customer_experience.pdf

Thank you to our sponsors/producers

Earley Information Science helps
organizations establish a strong
information architecture and
content management foundation
Specializing in making information more findable,
useable and valuable to drive digital commerce
innovation, enhance customer experience, and
improve operational efficiency and effectiveness.
Realize your digital transformation vision
with EIS.
Earley Information Science
(EIS)
A trusted information integrator
Founded – 1994
Headquarters – Boston, MA
www.earley.com
Seth Earley, CEO
Email: seth@earley.com
Twitter: @sethearley
LinkedIn: www.linkedin.com/in/sethearley

A Broad Spectrum of Business Solutions
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Context
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Earley Executive Roundtable on Customer Analytics

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