Contenu connexe Similaire à Meaningful Metrics - Aligning Operational Metrics with Marketing & Customer Experience (20) Plus de Earley Information Science (15) Meaningful Metrics - Aligning Operational Metrics with Marketing & Customer Experience1. Copyright © 2016 Earley Information Science1
Meaningful Metrics: Aligning
Operational Metrics with Marketing
and Customer Experience
Copyright © 2016 Earley Information Science
Dino Eliopulos, Managing Director, Earley Information Science
Seth Earley, CEO, Earley Information Science
John Walker, Principal, SemanticClarity
Gerry McGovern, CEO & Founder, Customer Carewords
March, 2016
2. Copyright © 2016 Earley Information Science2
Today’s Agenda
• Welcome & Housekeeping
• Dino Eliopulos, Managing Director, Earley Information Science
(@deliopulos)
• Session duration & questions
• Session recording & materials
• Take the polls & the survey!
• The Panelist Point of View
• Seth Earley, CEO, Earley Information Science (@sethearley)
• John Walker, Principal, SemanticClarity
• Gerry McGovern, CEO & Founder, Customer Carewords (@gerrymcgovern)
• Expert Panel Discussion
• Questions & Answers
• Join the conversation: #earleyroundtable
3. Copyright © 2016 Earley Information Science3
Dino Eliopulos - Biography
Dino Eliopulos
Managing Director
Earley Information
Science
Experienced leader and innovator in industry and high-end professional IT
consulting with deep specialization in user experience and highly complex business
applications.
Areas of expertise include: strategy, planning, forecasting, budgeting,
measurement, sales, talent acquisition / management and retention, career
stewardship, program management and service delivery.
Industry experience in
• Financial Services
• Retail / CPG
• Telecommunications
• Travel and Entertainment
• Healthcare
• Pharmaceuticals
• Hi-Tech Manufacturing and Energy
4. Copyright © 2016 Earley Information Science4 Copyright © 2016 Earley Information Science
Meaningful Metrics
Aligning Operational Metrics with Marketing and Customer Experience
5. Copyright © 2016 Earley Information Science5
Making Sense of Data “Vapor Trails”
• All customer interactions leave traces throughout multiple systems
• We interpret these trails and track data in order to make decisions about what to
do differently
– Change a promotion
– Improve performance
– Adjust a user experience
– Modify a product offering
– Etc.
• Metrics provide signals to the organization about what to do and when to do it
6. Copyright © 2016 Earley Information Science6
What are the Most Important Metrics?
• Customer Behaviour: buy, buy more/less, recommend, abandon,
defection
• Customer Feedback: effort, dissatisfaction , complaints , social media
• Employee Behavior: tenure, knowledge, customer feedback
• Employee Feedback: stories, suggestions, process, tools, systems, policies
• Process performance: cycle time & right first time, system uptime
• Total Cost to Serve: across budgets & domains
• Product Performance: returns , quality, contacts per sale, support calls
Go-to-market and competitive strategies
determines areas of focus
7. Copyright © 2016 Earley Information Science7
Strategy Examples
Strategy Approach Measure Mechanism
Customer Intelligence Leadership We will invest in best-in-class
capabilities for sensing, acquiring,
analyzing and responding to
customer feedback, intelligence
and behaviours
Sales and Customer
Engagement
• E commerce sales analysis
• Customer relationship management
• Survey text analytics
• Digital Marketing
Ubiquitous & Effortless Access We will invest in channels that
enable customer access to
expertise, help, services according
to need, efficiency and
effectiveness
Dissatisfaction • Knowledge bases
• Unsupervised Support
• Call center support
• Community development
• Optimized multi channel experience
Next Generation Customer Journey
Management
We will invest in customer
empowering technologies that
reduce effort and provide control.
Total Cost to Serve • Customer self service
• Mobile experience optimization
• Hyper local services
• Dynamic, personalized content
Equipping our People We will build the industry’s highest
performing workforce that inspires
trust
Internal Engagement • Internal collaboration tools and programs
• Knowledge management capabilities
• Search Based Applications and Unified
Information access for improved productivity
8. Copyright © 2016 Earley Information Science8 Copyright © 2016 Earley Information Science
Poll Question #1
What classes of metric are most important to you?
9. Copyright © 2016 Earley Information Science9
Customer engagement (behaviors and feedback)
Optimizing support (call center and self service)
Employee engagement (improved internal processes)
None of the above
What classes of metric are most important to you?
a
b
c
d
10. Copyright © 2016 Earley Information Science10
Seth Earley - Biography
Seth Earley
CEO and Founder
Earley Information
Science
Over 20 years experience
Current work
Co-author
Editor
Member
Former Co-Chair
Founder
Former adjunct professor
Guest speaker
AIIM Master Trainer
Course Developer & Master Instructor
Data science and technology, content and knowledge
management systems, background in sciences (chemistry)
Enterprise IA and Semantic Search
Information Organization and Access
US Strategic Command briefing on knowledge networks
Northeastern University
Boston Knowledge Management Forum
Long history of industry
education and research in
emerging fields
Academy of Motion Picture Arts and Sciences, Science and
Technology Council Metadata Project Committee
Editorial Journal of Applied Marketing Analytics
Data Analytics Department IEEE IT Professional Magazine
Practical Knowledge Management from IBM Press
Cognitive computing, knowledge and data management
systems, taxonomy, ontology and metadata governance
strategies
11. Copyright © 2016 Earley Information Science11
Marketing Technology ROI
• Many organizations are driving operationalization of marketing analytics
deeper into the organization
• The challenge lies in improving business fluency with analytics
• If specialized knowledge and expertise is required to perform analysis,
analytics is not part of the day to day operational process
• In many cases, highly skilled analysts produce reports that are not widely
shared and therefore are recreated by others
Big Investments in Marketing Technologies
…but fluency is lacking
12. We accurately measure the return on
marketing investment (ROMI) of
campaigns
Marketing pros self assessments are
lukewarm…
Less than…
1 in 5
…analytics professionals
say their efficiency,
effectiveness or
acquisition…
metrics are
completely effective
Source: Forrester / Burtch Works survey of 170 analytic and measurement professionals
13. Source: Q1 2014 Global
Cross-Channel Campaign
Management Forrester
Wave™ Customer Survey
…and analytics is an area of need for
campaign managers…
“Which of the following are the three most important areas that your
vendor could improve upon?”
14. Source: The CMO Survey, cmosurvey.org, August 2013, Top Line Results
Does your company have the right
talent to fully leverage marketing
analytics?
…and analytics often fails senior
marketers
14%
say “yes” with
confidence
To what degree is your company
leveraging marketing analytics to
answer its most challenging
marketing questions?
15. Copyright © 2016 Earley Information Science15
For example, “high bounce rate” can have multiple causes
– Content does not meet the needs of the user
– The path to the content was not clear
– The user was in the wrong place
– The user experience led them down the wrong path
– Navigational labeling is misleading
– Search returned the incorrect results
– The user was not clear on what they wanted
Separating Signals
Metrics are signals that tell us to do something. The
challenge is determining what the signals are telling us.
16. Copyright © 2016 Earley Information Science16
• Different audiences will
interpret metrics differently
• Stakeholders require metrics
across the application
ecosystem
Signals need to be surfaced in context
• Different systems will describe
products, content, data and
customers using attributes that
have to be normalized
• Ownership and clock speed of
tools and processes are sources
of contention
17. Copyright © 2016 Earley Information Science17
Contextualize Analytics through Customer Lifecycle
Profile customers
Evaluate leads
Target prospects
Analyze on site behaviors
Optimize search
Improve user experience
Evaluate promotions
Create cross sell relationships
Personalize offers
Optimize self service
Improve knowledge retrieval
Evaluate product usage
Analyze sentiment
Measure community engagement
Understand loyalty drivers
Stakeholders each have a specific perspective, tools, metrics, priorities
18. Copyright © 2016 Earley Information Science18
Contextualize Analytics through Customer Lifecycle
Profile customers
Evaluate leads
Target prospects
Analyze on site behaviors
Optimize search
Improve user experience
Evaluate promotions
Create cross sell relationships
Personalize offers
Optimize self service
Improve knowledge retrieval
Evaluate product usage
Analyze sentiment
Measure community engagement
Understand loyalty drivers
Customer journey challenges can cause
friction due to conflicting drivers
19. Source: June 19, 2014, “How Analytics Drives Customer Life-Cycle Management” Forrester report
According to Forrester,
greatest opportunity lies in
latter stages of lifecycle
These are less well
understood by many
marketing organizations
20. Copyright © 2016 Earley Information Science20
Good governance leads to greater ROI.
– Search metrics
– Behavior metrics
– Utilization metrics
– Content metrics
– Response metrics
Effective Marketing Metrics Requires Governance…
21. Copyright © 2016 Earley Information Science21
Marketing Metrics, KPI’s and Integration
Content
Marketing
Email
Hubspot
SalesforceTwitter
Webinars
Conferences
Executive
outreach
LinkedIn
22. Copyright © 2016 Earley Information Science22 Copyright © 2016 Earley Information Science
Poll Question #2
What are your biggest metrics challenges?
23. Copyright © 2016 Earley Information Science23
Data quality and consistency
Technical integration across systems
Clarity regarding what metrics are most important
None of the above
What are your biggest metrics challenges?
a
b
c
d
24. Copyright © 2016 Earley Information Science24
Works with clients and partners to extend their enterprise data architectures
to support the challenges of Big Data, and deliver solutions for product
recommendations, customer segmentation, and product MDM
opportunities.
• 15 years experience driving large-scale content, MDM and BI initiatives
• SCRUM-Certified Senior Engagement Leader
• Solution Domains:
– Omnichannel Retail
– Biotech
– High-Tech
– Publishing
John Walker - Biography
John Walker
Principal
SemanticClarity
25. Copyright © 2016 Earley Information Science25
• The collection of customer touch point data must be aligned with business
goals
• Does data collection support prioritized business metrics?
• Apply governance and data quality checks
• Test the ‘Veracity’ of less trusted data - safely
• Ingest data at full fidelity
– >Filter and Persist
• Leverage “Big Data” technologies and techniques to address the ‘Volume’,
‘Variety’, and ‘Velocity’
• Exploring Data Lake and EDW data will provide insights
Big Data / Data Lake Architectures – Collecting the Digital
Customer Experience
“Digital initiatives should complement existing customer journeys. Many
companies clumsily add digital components to customer journeys that don’t
directly benefit the customer or are superfluous to the company’s value
proposition. “
What a Great Digital Customer Experience Actually Looks Like , HBR Nov 2015
26. Copyright © 2016 Earley Information Science26
Data Lake Architecture – a framework for collecting and
processing the digital signal
The Data Lake – the data repository
• Governed and Compliant
• Support for data at “full fidelity”
• Controlled access to less trusted data sources
• Collection aligned to feed metrics
• Storage support for semi and unstructured data
Key framework features
• Process visibility and control
• Alignment with key metrics will focus technical
options – simplify
• Support new and extensible schemas
• Processing “Elasticity”
• Data pipeline capability
– Ingest Analysis & Mining
• Support data discovery and visualization
Big Data / Data Lake Architectures – Collecting the Digital
Customer Experience
Public or
Hybrid
Cloud
Data Lake
Enterprise
Application
Portfolio
Enterprise
Data
Warehouse
Data
Exploration
BI and Legacy
Reporting
Enterprise Network
Data
Center
27. Copyright © 2016 Earley Information Science27
Support metric tracking
• Data lineage x Source
• Cleanse / Enhance x Source
• Keep ML meta data
• Active data management
Big Data / Data Lake Architectures – Collecting the
Digital Customer Experience
Ingest
Prepare /
Schematize
Process /
Analyze
Persist
Streaming
Source Transform and
Analyze
Cleanse and
Enhance
Batch
Source
Machine
Learning
Explore and Visualize
Data
Store
Data Protection
Authentification / Authorization
28. Copyright © 2016 Earley Information Science28 Copyright © 2016 Earley Information Science
Poll Question #3
How well is your architecture supporting your metrics?
29. Copyright © 2016 Earley Information Science29
Very well, architecture is well built and tuned
Getting by with what we have, could be better
Struggling to manage our data, not serving the need
What data architecture?!
How well is your architecture supporting your metrics?
a
b
c
d
30. Copyright © 2016 Earley Information Science30
• Gerry helps large organizations become more customer
centric on the Web. His commercial clients include Microsoft,
Cisco, NetApp, VMware, and IBM. He has also consulted with
the US, UK, Dutch, Canadian, Norwegian and Irish
governments.
• He is the founder and CEO of Customer Carewords, a
company that has developed a set of tools and methods to
help large organizations identify and optimize their customers’
top online tasks.
• He has written five books on how the Web has facilitated the
rise of customer power. The Irish Times described Gerry as
one of five visionaries who have had a major impact on the
development of the Web. In 2015, he was shortlisted for a
Webby for his writings.
•
Gerry McGovern - Biography
Gerry McGovern
CEO & Founder
Customer Carewords
33. Copyright © 2016 Earley Information Science33
GOT THE HOUSING BLUES?
Start
Saving!
34. Copyright © 2016 Earley Information Science34
✓Traffic to product
pages increased by
520%
35. Copyright © 2016 Earley Information Science35
Cisco’s Digital Presence
355M
visits to Cisco.com
11.5M
social reach
3.9M
paid search click-
throughs
112M
organic search
referrals
40% 34%
71%
4%
10.7M
mobile web visits
1.4M
social referrers
to Cisco.com
3M
social media mentions
5.7M
customer app
downloads
2.5M
video views on
Cisco.com
Social Media MobileWeb Video
7.5M
video views on Cisco
YouTube
47%
[Date Range: Q1FY14 – Q4FY14]
36. Copyright © 2016 Earley Information Science36 36
Create a new guest account to access the Cisco.com website
and log in with this new account.
37. Copyright © 2016 Earley Information Science37
Jun 2015Dec 2014
22 mandatory and
optional fields
11 mandatory
fields
38. Copyright © 2016 Earley Information Science38 Copyright © 2016 Earley Information Science
39. Copyright © 2016 Earley Information Science39 Copyright © 2016 Earley Information Science
Panel Discussion
40. Copyright © 2016 Earley Information Science40
Roundtable Discussion
Seth Earley
CEO
Earley Information
Science
@sethearley
www.linkedin.com/i
n/sethearley
John Walker
Principal
SemanticClarity
www.linkedin.com/in
/john-walker-
32b40117
Gerry McGovern
CEO & Founder
Customer Carewords
@gerrymcgovern
https://ie.linkedin.com/i
n/gerry-mcgovern-
07876469
Dino Eliopulos
Managing Director
Earley Information
Science
@deliopulos
www.linkedin.com/in/
deliopulo
Moderator
41. Copyright © 2016 Earley Information Science41
Beckon White Papers & Reports
• http://pages.beckon.com/rs/beckoninc/images/The-Art-
of-the-Marketing-Scorecard-Beckon-White-Paper.pdf
• http://pages.beckon.com/rs/beckoninc/images/Marketin
g-Funnel-Metrics-Beckon-White-Paper.pdf
• http://pages.beckon.com/rs/976-IET-
418/images/Marketing-Measurement-Metamorphoses-
James-Nail-Forrester-Report.pdf
• http://pages.beckon.com/rs/beckoninc/images/Forrester
_Adopt_A_Staged_Approach_For_Marketing_Mix.pdf
EIS Blog
• http://www.earley.com/blog/enhancing-your-digital-
customer-experience-analytics-and-insights
References
42. Copyright © 2016 Earley Information Science42
Next ERT Topics
OK so Enterprise Search is
"Janky" - Now what?
Predictive Analytics, AI and the
Promise of Personalization
April 20th @ 1:00PM ET May 25th @ 1:00PM ET
43. Copyright © 2016 Earley Information Science43
Earley Information Science helps organizations
establish a strong information architecture
and content management foundation
Realize your digital transformation
vision with EIS.
Earley Information Science (EIS)
Information Architects for the Digital Age
Founded – 1994
Headquarters – Boston, MA
www.earley.com
For more info contact:
info@earley.com
careers@earley.com