Applying Analytics with Big

Data for Customer Intelligence

Seven Steps to Success

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Sponsors:

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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

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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

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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 dstodder@tdwi.org; @dbstodder

Hannah Smalltree, Treasure Data

hannah@treasure-data.com

Sri Raghavan, Teradata

Sri.Raghavan@teradata.com

Tamara Dull, SAS

Tamara.Dull@sas.com

Dan Potter, Datawatch Dan_Potter@datawatch.com