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TRANSCRIPT
Applying Analytics with Big
Data for Customer Intelligence
Seven Steps to Success
2
Sponsors:
3
Speakers
David Stodder
Research Director,
Business Intelligence
TDWI
Tamara Dull
Director,
Emerging Technologies,
SAS
Sri Raghavan
Senior Global
Marketing Manager,
Teradata
Dan Potter
VP,
Product Marketing,
Datawatch
Hannah Smalltree
Director,
Treasure Data
4
Agenda
• The big trend: data-driven customer intelligence
• Gaining a comprehensive view of data
– Roundtable discussion with guest speakers
• Customer analytics strategies and practices
– Roundtable discussion
• Speed to insight, visualization, and governance
– Roundtable discussion
• Seven steps: Concluding thoughts
• Your Questions
Data-Driven: Key to Customer Intelligence
• Customers are empowered:
more opportunities to learn before
buying; competitors a click away
– Companies seek clues to increasing
loyalty, stickiness, and attraction
• Not just efficiency but
intelligence: Firms don’t want to
waste money, but more important
is not to waste opportunities
– Data analysis critical to targeting and
personalization
• Customers are influencers:
what is social influence on brand,
marketing effectiveness?
Customer Insight & Engagement:
CMOs Move Past “Gut Feel” • Decision-makers want data: 75%
in TDWI Research indicate
acceptance of data-driven insights
over “gut feel”
– Data insights help those with fresh
ideas to challenge authority
• Seeking speed to insight:
Leaders can’t wait out long cycles
to understand market behavior
– Budgets grow for customer intelligence
solutions that are easier to deploy
– Research still suggests that greater
success comes with CMO/CIO
collaboration
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Big Data: About Going Beyond… • Beyond relational: flow of
semi- or unstructured click
streams, sensor, machine data
• Beyond structure: interest in
raw and/or complex data
streams, not transformed info
• Beyond BI and DW: Demand
for Hadoop & NoSQL; is
transformation necessary?
• Not just for big companies:
Even small and midsize firms
confront big data issues
– Can also tap external big data
#1: Gain a Comprehensive View • Perfection: A complete, 360
degree view of customers
across channels
– Integrating transactional,
behavioral, and demographic
views of customer data
– Sharing insights with business
partner networks
• Develop an strategy to enable
analytical depth and breadth
– Silo consolidation into DW
– Data virtualization/federated
access
– Appliances and cloud solutions
Hybrid: Integrating Big Data, EDW • Emerging “hybrid”
architectures: supporting a
variety of BI and analytics
processes
– Addressing demand for different
types of reports, visualizations,
and complex analysis
– In-memory computing as part of
the arsenal; moving more data
closer to users
– Integrating cloud and on-
premises solutions
• Strategy for agility and
scalability
Credit: Fotalia
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Integrating Views of Big Data:
Discussion with Guest Speakers
David Stodder
TDWI
What strategies should organizations take to support greater depth and
breath of BI and analytics on big data sources for customer intelligence?
Tamara Dull
SAS
Dan Potter
Datawatch
Sri Raghavan
Teradata
Hannah Smalltree
Treasure Data
#2: Implement Predictive Analytics
• Customer analytics:
learning…
– More about customers
– Their paths to purchase
– What increases loyalty
among the most valuable
• The goal: To derive
accurate insights from
integrated transaction,
service, behavioral, … data
– To better attract, retain,
interact with, and expand
customer relationships
• Statistical analysis
– Why this is happening?
• Forecasting or
extrapolation
– What if trends continue?
• Predictive
analytics/modeling:
– What will happen next?
(E.g., churn analysis)
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Customer Analytics: Which Business
Functions Benefit?
• Marketing: Pursuit of efficiency
and achievement of measurable
results; testing hypotheses
• Sales: Improve forecasting based
on deeper knowledge of priority
opportunities
• Finance: 2/3 in TDWI Research
said that customer analytics
important to finance function
• Service/Order Management:
Gain view of what actions most
impact customer satisfaction; tune
agent performance
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Varied Benefits of Customer Analytics
From “Customer Analytics in the Age of Social Media,” TDWI Best Practices Report, Third Quarter 2012
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Tools and Techniques Applied
From “Customer Analytics in the Age of Social Media,” TDWI Best Practices Report, Third Quarter 2012
#3: Deploy Analytics for Personalized
Marketing & Engagement
• Goal: Increasing intimacy
and knowledge through
data-driven insight
• Predictive view: Modeling
and understanding
segments’ propensity to buy
to improve targeting of
upsell and cross-sell offers
• Big data: Capturing
behavioral data, including
from real time event
streams, from first contact
to successive engagements
• Behavior-based
segmentation: Analytics for
getting beyond simple
demographics to one based
on actions and preferences
recorded over time
#4: Leverage Big Data Analytics
for Social Media Strategies • External perspective:
Firms can gain a valuable
outside-in views of brands,
operations, and competitors
• Customers influence each
other by commenting on
brands, reviewing products,
reacting to marketing
campaigns, and revealing
shared interests
– Analytics can help spot
influencers and measure
impact on social networks
• Filtering out the noise:
But not too much; “noise”
could be important signals
• Predictive analytics to
discover patterns, anticipate
product/service issues
• Metrics: Measuring share
of voice, brand reputation
• Understanding sentiment
drivers
• Determining marketing
effectiveness
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Social Media Analysis Objectives
From “Customer Analytics in the Age of Social Media,” TDWI Best Practices Report, Third Quarter 2012
Text Analytics: Deriving Value
From Big Social Media Data • Text analytics: umbrella
term for natural language
processing,
entity/relationship
extraction, modeling, and
taxonomy/classification
• Big data scalability: key
for text and social media
analytics
– Applying data science, often
requiring many passes
through the data; testing,
modeling, and testing again
• What’s beyond the words:
understanding polarity of
sentiment
• Not exact science:
Analytics should focus on
“intangibles” of improving
interaction, building
reputation, and influencing
the influencers
Big Data Variety: More to Come
• Location data analysis:
Learning about customers by
integrating data with maps
– Mobile computing adding new
dimension to customer data
• Speech analytics: search
and analysis for contact
centers, field sales/service
• Machine data: sensors
producing data for tracking
human behavior
– Internet of things; wearable
computing
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Analytics and Big Data:
Discussion with Guest Speakers What should organizations be thinking about as they expand their use of
analytics and big data for customer intelligence?
What can they do to make sure their journey delivers return on
investment?
David Stodder
TDWI
Tamara Dull
SAS
Dan Potter
Datawatch
Sri Raghavan
Teradata
Hannah Smalltree
Treasure Data
#5: Reduce Latency to Improve
Real-Time Insight
• Customer service and
interaction
• Adjusting automated
response to customers’
self-directed actions
• Responding to events in
markets, supply chains,
processes
• Using information to
guide product and
service development
• Monitoring and tracking
developing patterns and
situations
• Delivering fresh data to
decision makers
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Smart use of data and information can help reduce, if
not eliminate inefficiencies caused by delays in:
Real Time: What Does it Mean?
• Strong interest: Setting
expectations and defining
what “real time” means in
terms of data currency and
quality is critical to user
satisfaction
• Capturing data in real
time to run analytics:
Models and algorithms my
then run on a daily or hourly
basis
– Hadoop and NoSQL stores of
interest to hold the data
• Streaming analytics:
Applying predictive models
and scoring algorithms to
observe and interpret
patterns in real-time data
and event streams
– Common sources: online
behavior, gaming, mobile
device use, machine data
#6: Improve Data Visualization
and Analysis for All Users • Visual presentation of
customer intelligence:
– Spotting patterns, trends, or
anomalies that are critical to
understanding customer and
market behavior
– Enabling nontechnical SMEs
to consume and share insights
– “Storytelling” with data
visualizations for colleagues,
partners, and customers
• Operational alerting
– Event notification and
actionable intelligence
• Visual discovery and
analysis
– Moving beyond BI reporting to
answer “why” questions
– Dynamic, on-demand data
interaction (often supported by
in-memory computing)
– Visualizations to fit the
analysis: growing libraries of
possible visual expressions
• Beware of clutter
– Poor visualizations can
mislead users make the data
tsunami worse; guidance key
Visualizations to Meet Users’ Needs
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Source: “Data Visualization & Discovery for Better Business Outcomes,” TDWI Best Practices Report, Third Quarter 2013.
#7: Balance Flexibility with
Governance • Customer data is often
sensitive: data breaches
are commonplace; firms
must carefully oversee how
they manage and analyze it
– Center of Excellence or
governance committee of
business and IT management
can help
– A committee can help ensure
a good balance between
privacy and regulatory
adherence and meeting
business user needs
• Emerging hybrid data
architectures: enabling
firms to address volume,
variety, and velocity of big
data customer intelligence
– Integrating EDW, Hadoop, and
cloud into one architecture
– Alternative to governance
chaos
Applying Analytics with Big Data
for Customer Intelligence
1. Gain a comprehensive
view
2. Implement predictive
analytics
3. Deploy analytics for
personalized marketing
and engagement
4. Leverage big data
analytics for social
media strategies
5. Reduce latency to improve
real-time insight
6. Improve data visualization
and analysis for all users
7. Balance flexibility with
governance
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Closing Thoughts: Discussion
with Guest Speakers What can organizations do to improve “speed to insight” for customer
intelligence?
What strategies are best for balancing analytics flexibility with governance
and privacy requirements?
David Stodder
TDWI
Tamara Dull
SAS
Dan Potter
Datawatch
Sri Raghavan
Teradata
Hannah Smalltree
Treasure Data
28
Your Questions
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Thank You to Our Sponsors:
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Contact Information
• If you have further questions or comments:
David Stodder, TDWI [email protected]; @dbstodder
Hannah Smalltree, Treasure Data
Sri Raghavan, Teradata
Tamara Dull, SAS
Dan Potter, Datawatch [email protected]