Earley Executive Roundtable on Data Analytics - Metrics for Measuring the Customer Experience and Digital Marketing Success

Copyright © 2015 Earley Information Science1 Copyright © 2015 Earley Information Science
Earley Executive Roundtable
Series on Data Analytics
Metrics for Measuring the Customer
Experience and Digital Marketing Success
June 10, 2 015
Presented by
Seth Earley
CEO
Earley Information ScienceClick to watch a recording
of this presentation

Copyright © 2015 Earley Information Science2
Today’s Agenda
• Welcome & Housekeeping
– Session duration & questions
– Session recording & materials
– Take the survey!
• Introduction
– Seth Earley (@sethearley)
• Panelist Introductions
– Dr. Asuman Suenbuel, Senior Technical Advisor, Global Startup Focus Program, SAP
– Gary Parilis, Chief Research Officer, Greenwich Associates
– Pratibha Vuppuluri, Founder & Principal, KeyInsite
– Stuart Williams, Vice President, TBR @s2_Williams
• Panel Discussion
• Questions & Answers
• Join the conversation: #earleyroundtable

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

Metrics for Measuring the Customer
Experience and Digital Marketing Success
Core Concepts

Marketing Technology Ecosystem - 2014
5

Marketing Technology Ecosystem - 2015
6

Fast Evolving Landscape
7
January 2014:
947 Companies
January 2015:
1876 Companies

Making Sense of Digital Marketing Technology
• The purpose of marketing technology is to engage with users no matter
what stage of their lifecycle they may be in
• They may not know anything about you
• They may not know they have a need or want that you can satisfy
• They may be considering making a purchase but not know where to start
• They may know what they want and simply researching their options
8
How do we engage with prospects and customers?
How do we measure engagement?

Measuring engagement
Engagement is emotional
Engagement is logical
Engagement is contextual
Engagement depends on perspective
Engagement is about meeting needs throughout the
customer journey
How do we support the customer throughout their journey?
With blog posts, reviews, FAQ’s, how to videos, testimonials, product
specifications, user stories, events, communities, comparisons, instructions,
articles, account inquiries, customer support, service requests, etc.
9

Measuring engagement
• Analytics can measure the level of engagement at each step of the journey
• We can measure the effectiveness of digital content as well as internal
processes that support the journey
DIGITAL BODY LANGUAGE

The Customer Journey and Supporting
Enterprise Processes
Learn Buy Get Use Pay Support
Marketing Sales Distribution Service Finance Support
Customer Journey
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
Key Concept: Each system and process will have application metrics, quality metrics, process metrics and
financial metrics. However differences in attributes, data models and taxonomies will impact the ease of linking
internal process with external customer metrics.

Measuring here
(macro level -
outcomes)
Measuring here
(micro level -
effects)
Data Sources
Working here
(product data,
taxonomy, search,
on-boarding
workflow, etc.)
Enterprise Strategy
Business Unit Objectives
Market Share
Time to market Wallet Share
Business Processes
Attrition programs Customer Satisfaction
Web
Analytics
CRM
Data
Search
Metrics
Processes enable
objectives
LINKAGE
New accounts
Revenue Growth
Data supports (and
measures)
processes
Objectives align
with strategy
Linking “Digital Body Language” to outcomes
CEO: “Show me how web content will increase revenue.”
Conversions
Data Scorecards
Process Scorecards
Business Outcome Scorecards

Today’s Panel of Experts
Asuman Suenbuel, Gary Parilis, Pratibha Vuppuluri, Stuart
Williams

Dr. Asuman Suenbuel
Senior Technical
Advisor, Global Startup
Focus Program
SAP
• Asuman has been with SAP since 2004 in Palo Alto. As part of
her R&D activities, she held in 2009/10 a Guest Professor
position at the University of Potsdam, Germany teaching
Concurrency in Enterprise Systems.
• Prior to her current role, she served as Senior Director &
Technology Advisor in SAP’s Office of the CTO, where she led
innovative research and development in a wide range of topics
including IoT, big data analytics, NLP, multi-core programming
and more.
• Asuman holds a doctorate degree in computer science from the
Technical University Berlin, she studied at the University of
Passau, Technical University Berlin and did her Master’s Thesis
in ICSI and UC Berkeley in 1998.
• She holds more than a dozen patents, is co-author and author
of numerous publications, keynote speaker at conferences,
session chair at various conferences, past head of Industry
Chair Formal Methods Europe. She also received numerous
awards for scientific contributions e.g. in 2002, from the
European Association of Software Technology.
• She speaks several languages German, Turkish, French,
English and she is a passionate researcher & IT professional.

Metrics for Customer Experience
• Derived from the technical beauty and
complexity of a product.
• Simplicity: The complexity needs to be
well hidden from the user (the product must
be presented to the user in the simplest
way and solve the users problem);
– No “Umberto Eco’s Library”
– SAP’s new term “running simple”. QUEST
example
• Satisfaction: does the product solve the
user’s problem, keeps data save. Does it
do what it promises.
• Differentiation: State of the art versus
trendsetting aspect.

Metrics for measuring
• Time spent with a particular product to solve a
particular problem P .
• A positive experience with a certain product
will lead to solving a customer’s problem or
perform the task quickly (without rephrasing
or defining analogies).
• Consequently, a customer will have to spend
less time with the tool/product to solve a
particular task. Example: one click travel
reimbursement that will automatically pull all
receipts and prepare it for review versus
individual import of receipts.
• The repetitive use of a product for solving
another problem/task is a positive indicator

Metrics for Digital Marketing Success
• Digital product presentation:
– What problem is being solved and how? What makes me special in
this domain? Trendsetting: how is it different from the rest?
– Technical set up:
• Average page views per visit
• Scientific reputation
• How is a product presented, is it self explaining?
• Average cost per page view
• Instructions/guidelines/implementation steps require time, not everyone
is willing to spent time
• How much time to spent to digest the guidelines?
– Careful consideration of of review page; may negatively contribute
to a product; users usually go to a review site to share negative or
extremely positive emotions.
– Aggressive marketing might not always work, considered as
nuisance (filter out ad).

Gary Parilis
• 19 years in the market research/analytics and customer loyalty
field both on the client and provider side
• Currently Chief Research Officer at Greenwich Associates, the
market leader in research-based consulting for the financial
industry. Leads R&D initiatives and heads the analytics &
modeling function, as well as providing methodological
guidance across the firm
• Concentrating largely on development of offerings
designed to maximize ROI
• Also focused on advancements in segmentation for the
financial industry
• Ten years at TNS, one of the world’s largest custom market
intelligence firms, including 5+ years Senior Vice President,
leading the Marketing Science function in North America
• Previously specialized in market research and customer
experience analytics, on the provider side (four years dedicated
to IBM) and on the client side (five years at Lucent Technologies)
• Earned an M.S. and Ph.D. in Social Psychology from Rutgers
University, and held hybrid academic/administrative positions
Chief Research Officer
Greenwich Associates

POV – Gary Parilis
Don’t lose sight of this: the objective is not to optimize
your models; it’s to improve the customer experience
Modeling is only useful if it leads to action

POV – Gary Parilis
Don’t overcomplicate
– Paraphrasing George E.P. Box: All models are wrong; some are
useful
– An obsessive focus on model fit can make your models more wrong
and less useful; find the middle ground
Which of these models should you trust?
More
Generalizable
Better
Fit

21
Culture
Change
Behavior
Change
Unified
Vision
Coordinated
Reporting &
Analysis
Basic
Measurement
• Siloed research
• No connection
between data and
tangible action steps
• Standardized
reporting/analysis
across divisions &
functions
• Still no CEM strategy
• Dedicated CEM
leadership
• Consistent
communication
• Common metrics
• Specific action
planning
• Front line staff
engaged
• ROI case solidifies
• CEM is a way of doing
business
• Daily behaviors
change culture
• Technology is an
enabler
1
5
4
3
2
The CEM Maturity Path
Customer Experience Management
becomes more effective as programs
mature
Copyright © 2015 Greenwich Associates

Pratibha Vuppuluri
• Over ten years experience in the financial services industry
including seven years in the Healthcare, Private Equity Secondary
Market and Technology Investment Banking space at both UBS and
Deutsche Bank.
• Founder & Principal at KeyInsite Inc, a marketing analytics
company, and a Co-Founder of NariNetwork, a woman-focused
digital media company.
• Currently serves on the Board of Screenpro (a Consumer
electronics accessories company) and mindHive (Marketing mobile
app company). She is also a guide at the Resolution Project.
• Authored and published an economic thesis on “The Impact of
Negative Economic News Coverage on Consumer Confidence”.
• Graduated from Cornell University with a B.S. in Applied
Economics and Management with Distinction in Honors Research
and completed her Masters in International Affairs (International
Finance and Economic Policy) at Columbia University.
Founder & Principal
KeyInsite

23
Analytics approach: (1) Predict information attention/popularity (2) Data mine conversations /
networks (web / social) (3) Optimize target audience
Point of View: Trend - Digital Customer
Optimization
Acquire
Activate
Retain
Identifying the buyer persona.
Map and measure relationships and flows between interactions with the customer, URLs and other
connected information/knowledge entities.
Analyze and understand networks and the participants in relation to location, clusters, connectors, leaders
bridges, etc.
Take insights from the analysis performed to identify an accurate/close enough buyer persona .
Develop a strong call to action for achieving the desired preliminary objectives.
Metrics: new signups, application downloads, signups to newsletters/blogs, etc.
Create the identified call to action from the acquisition step based on target demographics tastes and
lifestyle.
Identify open rates for email marketing initiatives, click through rates etc to derive customer’s experience
in terms of their affinity towards the product/service.
Use A/B testing, Propensity models, Correlations, Multi-Variate testing, Collaborative filtering etc to derive
the significance of the call to action phase and also to derive the effectiveness of the campaign.
Recommended products through previous purchase history or segment attribution.
Discount programs, where participants receive instant discounts/benefits at the
point of sale.
Based on activity pattern, segment loyal consumers and target messages to
promote the brand online for active offers.

Stuart Williams
• Serves as vice president of research, engaging TBR clients to
promote the value of business insight that helps them improve
business performance, capture opportunity and increase customer
engagement.
• He leads a team that translates client needs into actionable expert
analysis, sets the overall research and syndicated agenda, and
crafts effective deliverables with TBR executives, directors and
senior analysts.
• Brings more than 20 years of experience as an IT decision maker
and business adviser to IT vendors.
• An expert on technology commercialization, business strategy and
competitive analysis Stuart’s research focuses on IT business
models and innovation at the intersection of vendor go-to-market
strategies and enterprise customer behavior, budgeting and
satisfaction.
• Widely quoted in the press, including The Wall Street Journal,
Fortune, USA Today, The Street, ZDNet and CNET.
• Received a B.A. in communication at the University of New
Hampshire and an M.S. in management of technology from the
Whittemore School of Business & Economics.
Vice President
Technology Business
Research

POV – Stuart Williams
• Businesses need CEO commitment, focus on maturity
• First order metrics:
– Productivity measures (e.g. time saved, efficiency)
– Volume measures (leads generated, pages viewed, impressions)
– Financial measures (e.g. opportunities created, revenue generated,
ROI)
– Customer measures (e.g. NPS, Walletshare, TLV, cost-to-acquire)
• Second order metrics
– IT maturity and change management
– Business process change
– Internal cultural change

Discussion

Thank you to our sponsors/producers
www.computer.org/itpro
www.informationdevelopmentworld.com
www.thecontentwrangler.com
http://www.tbri.com

http://www.henrystewartpublications.com/ama
Applied Marketing Analytics is the major new professional journal publishing in-depth, peer-
reviewed articles on all aspects of marketing analytics. Guided by an expert Editorial Board
each quarterly 100-page issue – published both in print and online – features detailed,
practical articles written by and for marketing analytics professionals on innovative thinking,
strategies, techniques, software and applied research showing how major brands are
collecting, interpreting and acting on marketing analytics, both around the world and across
varied digital and non-digital marketing channels.
10% off - use code “Earley” when you subscribe.
To subscribe with the discount, either
Email:
Simon Beckett simon@hspublications.co.uk
Or call:
800-633-4931 (in the US/Canada)
+44 207 092 3465 (in the rest of the world)

For more information
• IT Professional Magazine - www.computer.org/itpro Next issue focuses on Analytics
• Chiefmartech.com blog: http://chiefmartec.com/2012/07/agile-marketing-in-a-single-
whiteboard-sketch/
• Example marketing stacks http://chiefmartec.com/2015/05/marketers-really-passionate-
marketing-stacks/
• Beckon (marketing dashboard provider) white papers http://www.beckon.com/resources/
• Agile digital marketing at enterprise scale http://www.earley.com/knowledge/white-paper/8-
principles-agile-digital-marketing-enterprise-scale
• Applying science to the art of digital merchandizing http://www.earley.com/knowledge/white-
paper/applying-science-art-digital-merchandising
• Marketing analytics resources http://blog.hubspot.com/marketing/marketing-analytics-
resources (KissMetrics, Occam’s Razor, Google Analytics Academy, online courses)

Next Session: June 17th 1pm EDT
Using Business Analytics to Drive Higher ROI and
0rganizational Change
Joanna Schloss
Business Intelligence and Analytics
Evangelist, Dell Software
Geoff Woollacott
Principal Analyst/Practice
Manager, Technology Business
Research
Phillip Kemelor
Senior Manager, Advanced Analytics –
Digital Analytics, Ernst & Young LLP
Bruce Daley
Principal Analyst,
Tractica

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
DIGITAL BUSINESS SOLUTIONS
B2C Digital Commerce
• Product Curation for a World-Class
Product Catalog
• Site Merchandising Taxonomy & Attribute
Design
• Information Architecture for Shopper
Context
B2B Digital Commerce
• Product Search & Findability
• Product Information Management
• Product Knowledge Management
Digital Workplace
• Enterprise Content & Records
Management
• Information Architecture
• Enterprise Knowledge Management

EIS Reference Architecture

The Customer Journey and Digital
Technology
Inbound H H M L M L
Outbound H H L M M H
Analytics H H H H M H
Integration H H H M M H
Transaction N/A N/A H N/A N/A N/A
Learn Choose Purchase Use Maintain Recommend

Outbound
Campaign Mgt
(ABC)
(DEF)
(GHI)
H H H H M H
Email marketing
(JKL)
H H M H M H
Social media
(MNO)
(PQR)
(STU)
H H L H L H
Soc media listening
(VWX)
(YZ
H H L H H H
GREENLegend Green
Yellow
Red
Tan
Appropriate functionality given current maturity and business objectives
Acceptable but not optimal functionality
Below required capabilities
Less critical functionality given current state and business strategy
YELLOW
RED
TAN
YELLOW
RED
TAN
REDRED
YELLOW YELLOW YELLOW
GREENYELLOW
RED RED
YELLOW
GREEN
Current Technology Stack
Maturity and capability leveraging current technology stack*
* Source: Interviews and Maturity Model
YELLOW YELLOWYELLOW
GREEN GREEN GREEN
YELLOWYELLOW
Learn Choose Purchase Use Maintain Recommend

Technologies Supporting the Digital
Journey
Stage Goals Technologies Example Stack
Learn Build awareness through
advertising, campaigns, word
of mouth, web site content
Content management,
campaign management, SEO,
email, social media
Adobe AEM, Bronto Mail,
Ion, Google ads, FB, Twitter,
Instagram, DigiMind,
Luminoso
Choose Help customers learn about
the products and try to get
them into the store to try
All of above plus more
personalized content, on site
search
Above plus Salesforce.com,
Hubspot, Act-On
Purchase Transact with the customer
or get them to retail store.
Ecommerce tools (catalog,
shopping cart, order
management etc.)
Demandware, Hybris, Digital
River
Use Provide more personalized
content based on the
purchase, engage in social
media, answer questions
Chat, personalized email
messages and web content,
knowledge base, social media
tools
Adobe AEM, Bronto,
Hubspot, DigiMind,
Luminoso
Maintain Same goals as above plus
troubleshooting
Same as above with heavier
emphasis on reminders,
troubleshooting
Endeca, Hana, Magento,
Hubspot
Recommend Further support a positive
experience with events,
promotions, community
development
Greater use of email,
campaigns and heavy
emphasis on social media
listening and participation
Adobe AEM, Bronto, Google
ads, FB, Twitter, Instagram,
DigiMind, Luminoso
36

Digital Maturity
Stage
Capability
Stage 1
Unmanaged
Stage 2
Nascent
Stage 3
Evolving
Stage 4
Harmonized
Stage 5
Choreographed
Commerce
Experience
No connection of
promotions to on site
experience. Inability to
select and filter
Mobile friendly search,
browse and purchase,
Promotional content
surrounding targeted
customer through paid
and earned media
Shopping cart retrieval
with targeted just in time
offers based on past
behaviors, cross sell and
up sell driven by data
relationships and
merchandiser strategy
Agile promotions,
bundles, personalized
recommendations based
on customer data and
behavior
Custom vehicle design
and pricing with order
flowing to manufacturing
with flexible financial
models to compensate
dealer
Digital Asset
Management
Static & fragmented
content, poor digital asset
control, hand-crafted
channels
Content ownership
defined, aggregation from
multiple sources, lifecycle
monitored
Content tagging/reuse
moderate, digital assets
managed, editorial
guidelines in place
Content & assets
coordinated across
enterprise, channels, by
audience
Dynamic content
presented according to
device, context, geo-
location & segment
Product Information
Management
Poor data quality, manual
validation, limited
transformation
Category hierarchies &
attributes identified, semi-
automated data quality
processes
Business rules built into
product data quality,
manual hierarchy
mapping to downstream
systems
Data quality feedback to
upstream sources, docs to
data engineering
workflows and integration
Consistent cross-channel,
cross enterprise
mapping, integration with
domain analytics
Content and Site
Architecture
Poor site navigation, no
ability to search,
confusing content or
selection, content not
aligned with user needs,
disconnected from
shopping function
Integration of content with
ecommerce functionality,
faceted search baaed on
customer needs and
driven by personas and
use cases, content
strategy specifically
designed to assist
selection of vehicle and
accessories
Semantic search for
curated video assets and
knowledge base access,
configuration of custom
vehicle, tuned attributes
for faceted search
Adaptive content based
on attribute model that
considers demographic,
psychographic, social
graph and web site
behaviors to provide just
in time content., Avatar
interface to structured
content to answer
questions
Predictive analytics driven
personalized offers and
experience. Real time
integration with dealer
network, social media,
social graph data, third
party data, web site click
streams, single view of
customer data
Site Search
Basic site search without
filtering, manual dictionary
updates, no coordination
with SEO
Curated site search
categories, aligned with
navigation, SEO-friendly
landing pages
Increased precision, SEO
synonyms in taxonomy,
search dimensions
optimized
Attributes drive
comparisons & cross-sell,
associative relationships
drive ways to shop & up-
sell
Multi-device search,
mobile geo-location, 3rd
party site search is
competitive advantage
Governance None in place
Brand, merchandiser
print & ecommerce
fiefdoms
Centralized, managed &
funded
Competencies support
maintenance processes &
workflow
Integrated practices
operationalized, impact
analysis is proactive37

Evolving Digital Maturity
Stage
Capability
Stage 1
Unmanaged
Stage 2
Nascent
Stage 3
Evolving
Stage 4
Harmonized
Stage 5
Choreographed
Commerce
Experience
No connection of
promotions to on site
experience. Inability to
select and filter
Mobile friendly search,
browse and purchase,
Promotional content
surrounding targeted
customer through paid
and earned media
Shopping cart retrieval
with targeted just in time
offers based on past
behaviors, cross sell and
up sell driven by data
relationships and
merchandiser strategy
Agile promotions,
bundles, personalized
recommendations based
on customer data and
behavior
Custom vehicle design
and pricing with order
flowing to manufacturing
with flexible financial
models to compensate
dealer
Digital Asset
Management
Static & fragmented
content, poor digital asset
control, hand-crafted
channels
Content ownership
defined, aggregation from
multiple sources, lifecycle
monitored
Content tagging/reuse
moderate, digital assets
managed, editorial
guidelines in place
Content & assets
coordinated across
enterprise, channels, by
audience
Dynamic content
presented according to
device, context, geo-
location & segment
Product Information
Management
Poor data quality, manual
validation, limited
transformation
Category hierarchies &
attributes identified, semi-
automated data quality
processes
Business rules built into
product data quality,
manual hierarchy
mapping to downstream
systems
Data quality feedback to
upstream sources, docs to
data engineering
workflows and integration
Consistent cross-channel,
cross enterprise
mapping, integration with
domain analytics
Content and Site
Architecture
Poor site navigation, no
ability to search,
confusing content or
selection, content not
aligned with user needs,
disconnected from
shopping function
Integration of content with
ecommerce functionality,
faceted search baaed on
customer needs and
driven by personas and
use cases, content
strategy specifically
designed to assist
selection of vehicle and
accessories
Semantic search for
curated video assets and
knowledge base access,
configuration of custom
vehicle, tuned attributes
for faceted search
Adaptive content based
on attribute model that
considers demographic,
psychographic, social
graph and web site
behaviors to provide just
in time content., Avatar
interface to structured
content to answer
questions
Predictive analytics driven
personalized offers and
experience. Real time
integration with dealer
network, social media,
social graph data, third
party data, web site click
streams, single view of
customer data
Site Search
Basic site search without
filtering, manual dictionary
updates, no coordination
with SEO
Curated site search
categories, aligned with
navigation, SEO-friendly
landing pages
Increased precision, SEO
synonyms in taxonomy,
search dimensions
optimized
Attributes drive
comparisons & cross-sell,
associative relationships
drive ways to shop & up-
sell
Multi-device search,
mobile geo-location, 3rd
party site search is
competitive advantage
Governance None in place
Brand, merchandiser
print & ecommerce
fiefdoms
Centralized, managed &
funded
Competencies support
maintenance processes &
workflow
Integrated practices
operationalized, impact
analysis is proactive
38

Earley Executive Roundtable on Data Analytics - Metrics for Measuring the Customer Experience and Digital Marketing Success

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