Customer Intelligence & Analytics - Part I
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Transcript of Customer Intelligence & Analytics - Part I
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1.1 Introduction
1.2 Competing On Analytics
1.3 The Data Explosion
1.4 Evolving Context of Marketing Analytics & Research
1.5 Questions
Module 1: The World of Marketing Is
Changing - Are You Being Left Behind?
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• Debbie Mayville – Sr. Solutions Architect, Communications & Marketing
Analytics, SAS
• David Kelley – Sr. Solutions Architect, Customer Intelligence, SAS
• Suneel Grover – Solutions Architect, Integrated Marketing Analytics, SAS
– Adjunct Professor, Integrated Marketing Analytics,
New York University (NYU)
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1.1 Introduction
1.2 Competing On Analytics
1.3 The Data Explosion
1.4 Evolving Context of Marketing Analytics & Research
1.5 Questions
Module 1: The World of Marketing Is
Changing - Are You Being Left Behind?
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„Old Spice‟ Campaign Case Study
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Achieving Success With Business Analytics
What happened?
How many, how often, where?
Where exactly is the problem?
What actions are needed?
Why is this happening?
What if these trends continue?
What will happen next?
What’s the best that can happen?
Optimization
Predictive Modeling
Forecasting
Statistical Analysis
Alerts
Query Drilldown
Ad hoc Reports
Std. Reports
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Business Analytics
“The extensive use of data, statistical and quantitative
analysis, explanatory and predictive models, and fact-
based management to drive decisions and actions.”
Davenport and Harris (2007)
Competing on Analytics:
The New Science of Winning
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Data Deluge
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Three Consequences Of The Data Deluge
1. Every problem will generate data eventually.
2. Every company will need analytics eventually.
3. Everyone will need analytics eventually.
...
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Three Consequences Of The Data Deluge
1. Every problem will generate data eventually.
Proactively defining a data collection protocol will
result in more useful information, leading to more
useful analytics.
2. Every company will need analytics eventually.
3. Everyone will need analytics eventually.
...
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Three Consequences Of The Data Deluge
1. Every problem will generate data eventually.
Proactively defining a data collection protocol will
result in more useful information, leading to more
useful analytics.
2. Every company will need analytics eventually.
Proactively analytical companies will compete more
effectively.
3. Everyone will need analytics eventually.
...
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Three Consequences Of The Data Deluge
1. Every problem will generate data eventually.
Proactively defining a data collection protocol will
result in more useful information, leading to more
useful analytics.
2. Every company will need analytics eventually.
Proactively analytical companies will compete more
effectively.
3. Everyone will need analytics eventually.
Proactively analytical people will be more
marketable and more successful in their work.
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The Business Analytics Challenge Getting anything useful out of tons and tons of data
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Hope For The Data Deluge
= actionable knowledge
+ analytical tools
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Changes In The Analytical Landscape
Analytical Modelers Management
Historically…
Historically, analytics have
typically been handled in
the “back office,” and
information was shared
only by a few individuals.
Models
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Changes In The Analytical Landscape
Historical Changes
– Executive Dashboards
• Static reports about business processes
– Customer Relationship Management (CRM)
• The right offer to the right person at the right time
– 360-degree customer view
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Changes In The Analytical Landscape
Relational Databases
Enterprise Resource Planning (ERP)
Point of Sale (POS) Systems
Decision Support Systems
– Reporting and Ad Hoc Queries
– Online Analytical Processing (OLAP)
Performance Management Systems
– Executive Information Systems (EIS)
– Balanced Scorecard
Business Intelligence
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CRM Evolution
• Total Quality Management (TQM)
– Product-Centric
• Quality: Six Sigma
• Total Customer Satisfaction
• Mass Marketing
• One-to-One Marketing
– Customer Relationship
• Wallet Share of Customer
• Customer Retention
• Customer Relationship Management (CRM)
– Customer-Centric
• Strategy
• Process
• Technology
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Customer Service
Retail
Promotions
Operations
Changes In The Analytical Landscape
Analytical Modelers
Targeting
Customers
Suppliers
Employees
Now…
Now analytics are being pushed out
to the “front office”. There are clear,
tangible benefits that management
will track. Data mining is a critical part
of business analytics.
Proliferation of
Models
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Idiosyncrasies Of Business Analytics
1. The Data
- Massive, operational, and opportunistic
2. The Users and Sponsors
- Business decision support
3. The Methodology
- Computer-intensive adhockery
- Multidisciplinary
Data mining can be defined as
advanced methods for exploring
and modeling relationships in
large amounts of data.
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The Data
Experimental Opportunistic
Purpose Research Operational
Value Scientific Commercial
Generation Actively controlled Passively observed
Size Small Massive
Hygiene Clean Dirty
State Static Dynamic
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The Data: Disparate Business Units
Marketing Invoicing Risk
Acquisitions Operations Sales
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Opportunistic Data
– Operational data
• Typically not collected with data analysis in mind
– Multiple business units
• Silo-based data environment
This makes business analytics different from
experimental statistics and especially challenging
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The Methodology: What We Learned Not to Do
• Prediction is more important than inference
1. Metrics are used “because they work”
2. p-values are directional guides
3. Interpretation of a model might be irrelevant
4. The preliminary value of a model is determined by
its ability to predict a holdout sample
5. The long-term value of a model is determined by
its ability to continue to perform well over time
6. Models are retired as behavior and trends shifts
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Using Analytics Intelligently
• Intelligent use of analytics
1. Understanding of how
marketplace shifts affect
business performance
2. Ability to distinguish between
effective and ineffective
interventions
3. Efficient use of assets, reduced waste
4. Risk reduction via measurable outcomes
5. Early detection of trends hidden in massive data
6. Continuous improvement in decision making
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Simple Reporting
Examples: OLAP, RFM, descriptive statistics, extrapolation
Answer questions such as:
1. Where are my key indicators now?
2. Where were my key indicators last week?
3. Is the current process behaving like normal?
4. What’s likely to happen tomorrow?
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Proactive Analytical Investigation
Examples: Data mining, experimentation, empirical
validation, predictive modeling, optimization
Answer questions such as:
1. What does a change in the market mean for my targets?
2. What do other factors tell me about my target?
3. What is the best combination of factors for maximum profit?
4. What is the highest price the market will tolerate?
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Data Stalemate
• Many companies have data that they do not use or sell
to third parties. These third parties might even resell the
data and any derived metrics back to the original
company!
• Story: Retail grocery POS card
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Every Little Bit…
Taking an analytical approach to only a few key business
problems with reliable metrics tangible benefit
The benefits and savings derived from early analytical
successes managerial support for more analytics
1. Everyone has data
2. Analytics can connect data to
smart decisions
3. Proactively analytical companies
outpace competition
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Areas Where Analytics Are Often Used
• New customer acquisition
• Customer loyalty
• Cross-sell / up-sell
• Pricing tolerance
• Supply optimization
• Staffing optimization
• Financial forecasting
• Product placement
• Churn
• Insurance rate setting
• Fraud detection
• …
Which residents in a ZIP
code should receive a
coupon in the mail for a
new store location?
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Areas Where Analytics Are Often Used
• New customer acquisition
• Customer loyalty
• Cross-sell / up-sell
• Pricing tolerance
• Supply optimization
• Staffing optimization
• Financial forecasting
• Product placement
• Churn
• Insurance rate setting
• Fraud detection
• …
What advertising strategy
best elicits positive
sentiment toward the
brand?
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Areas Where Analytics Are Often Used
• New customer acquisition
• Customer loyalty
• Cross-sell / up-sell
• Pricing tolerance
• Supply optimization
• Staffing optimization
• Financial forecasting
• Product placement
• Churn
• Insurance rate setting
• Fraud detection
• …
What is the best next
product for this customer?
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Areas Where Analytics Are Often Used
• New customer acquisition
• Customer loyalty
• Cross-sell / up-sell
• Pricing tolerance
• Supply optimization
• Staffing optimization
• Financial forecasting
• Product placement
• Churn
• Insurance rate setting
• Fraud detection
• …
What is the highest price
that the market will bear
without substantial loss of
demand?
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Areas Where Analytics Are Often Used
• New customer acquisition
• Customer loyalty
• Cross-sell / up-sell
• Pricing tolerance
• Supply optimization
• Staffing optimization
• Financial forecasting
• Product placement
• Churn
• Insurance rate setting
• Fraud detection
• …
How many 60-inch HDTVs
should be in stock?
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Areas Where Analytics Are Often Used
• New customer acquisition
• Customer loyalty
• Cross-sell / up-sell
• Pricing tolerance
• Supply optimization
• Staffing optimization
• Financial forecasting
• Product placement
• Churn
• Insurance rate setting
• Fraud detection
• …
What are the best times
and best days to have
technical experts on the
showroom floor?
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Areas Where Analytics Are Often Used • New customer acquisition
• Customer loyalty
• Cross-sell / up-sell
• Pricing tolerance
• Supply optimization
• Staffing optimization
• Financial forecasting
• Product placement
• Churn
• Insurance rate setting
• Fraud detection
• …
What weekly revenue
increase can be expected
after the Mother’s Day
sale?
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Areas Where Analytics Are Often Used • New customer acquisition
• Customer loyalty
• Cross-sell / up-sell
• Pricing tolerance
• Supply optimization
• Staffing optimization
• Financial forecasting
• Product placement
• Churn
• Insurance rate setting
• Fraud detection
• …
Will oatmeal sell better
near granola bars or near
baby food?
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Areas Where Analytics Are Often Used • New customer acquisition
• Customer loyalty
• Cross-sell / up-sell
• Pricing tolerance
• Supply optimization
• Staffing optimization
• Financial forecasting
• Product placement
• Churn
• Insurance rate setting
• Fraud detection
• …
Which customers are most
likely to switch to a
different wireless provider
in the next six months?
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Areas Where Analytics Are Often Used • New customer acquisition
• Customer loyalty
• Cross-sell / up-sell
• Pricing tolerance
• Supply optimization
• Staffing optimization
• Financial forecasting
• Product placement
• Churn
• Insurance rate setting
• Fraud detection
• …
How likely is it that this
individual will have a claim?
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Areas Where Analytics Are Often Used • New customer acquisition
• Customer loyalty
• Cross-sell / up-sell
• Pricing tolerance
• Supply optimization
• Staffing optimization
• Financial forecasting
• Product placement
• Churn
• Insurance rate setting
• Fraud detection
• …
How can I identify a fraudulent
purchase?
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When Analytics Are Not Helpful
• Snap decisions required
• Novel approach (no previous
data possible)
• Most salient factors are rare
(making decisions to work
around unlikely obstacles or
miracles)
• Expert analysis suggests a
particular path
• Metrics are inappropriate
• Naïve implementation of
analytics
• Confirming what you already
know
Deciding when to run
from danger
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When Analytics Are Not Helpful
• Snap decisions required
• Novel approach (no previous
data possible)
• Most salient factors are rare
(making decisions to work
around unlikely obstacles or
miracles)
• Expert analysis suggests a
particular path
• Metrics are inappropriate
• Naïve implementation of
analytics
• Confirming what you already
know
Predicting the adoption of
a new technology
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When Analytics Are Not Helpful
• Snap decisions required
• Novel approach (no previous
data possible)
• Most salient factors are rare
(making decisions to work
around unlikely obstacles or
miracles)
• Expert analysis suggests a
particular path
• Metrics are inappropriate
• Naïve implementation of
analytics
• Confirming what you already
know
Planning contingencies
for employees winning
the lottery
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When Analytics Are Not Helpful • Snap decisions required
• Novel approach (no previous
data possible)
• Most salient factors are rare
(making decisions to work
around unlikely obstacles or
miracles)
• Expert analysis suggests a
particular path
• Metrics are inappropriate
• Naïve implementation of
analytics
• Confirming what you already
know
The seasoned art critic
can recognize a fake
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When Analytics Are Not Helpful
• Snap decisions required
• Novel approach (no previous
data possible)
• Most salient factors are rare
(making decisions to work
around unlikely obstacles or
miracles)
• Expert analysis suggests a
particular path
• Metrics are inappropriate
• Naïve implementation of
analytics
• Confirming what you already
know
Predicting athletes’
salaries or quantifying
love
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When Analytics Are Not Helpful • Snap decisions required
• Novel approach (no previous
data possible)
• Most salient factors are rare
(making decisions to work
around unlikely obstacles or
miracles)
• Expert analysis suggests a
particular path
• Metrics are inappropriate
• Naïve implementation of
analytics
• Confirming what you already
know
Only looking at one
variable at a time
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When Analytics Are Not Helpful
• Snap-decisions required
• Novel approach (no previous
data possible)
• Most salient factors are rare
(making decisions to work
around unlikely obstacles or
miracles)
• Expert analysis suggests a
particular path
• Metrics are inappropriate
• Naïve implementation of
analytics
• Confirming what you already
know
Ignoring variables that
might be important
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The Fallacy Of Univariate Thinking
What is the most important cause of churn?
Prob(churn)
International
Usage
Daytime
Usage
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Expectations Leading The Analysis
• Sophisticated analytics are not immune to personal bias
– Selectively fitting models because they place an opinion or
agenda in a positive light
– Ignoring information that might disprove a hypothesis
• Personal bias, whether intentional or not, can diminish
the usefulness of analytics
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Trustworthy Analytics
Let the data guide your conclusions
– Are my assumptions about the causes
of the data patterns warranted?
– Should I be trying something different?
Assign a cynic to the analytical team whose purpose is
to question the assumptions
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Idea Exchange
Identify several business problems that you could
address with analytics
Describe the goal, whether the variables can be
measured, how the data could be obtained, and what
types of specific questions you would like to address
with analytics
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Case Study – US Telco
• Data Deluge: Just Get Started
– Low hanging fruit
– Continue to build and get smarter
– 360 degree view of the customer
• Tools: Efficiency & Effectiveness
– Data management tools
– Analytic tools
• Move to data driven insights versus gut reactions
• Establish measurement system
– Test & Learn Environment
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Customer Lifecycle – Touch Points
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Obtaining 360 Degree View Of The Customer
360 Degree
Customer View
Activ-ation Firmo-
graphics
Demo-graphics
Point of Sale
Service, Repair
Network
Commu-ni-
cations Collect-ions
Billing
VOD, Games
Hard-ware
Care
Usage
Social Network
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Large Telco With Industry-leading Churn Rate
Churn Reduction By Reason
Churn Reduction Value ($)
Total 82 bps $912M
Equipment $121M 9 bps
16 bps $163M Usage
15 bps $158M Network
11 bps $110M Active Issue
Resolution
25 bps $273M Contract Renewal
Sales Channel / Credit &
Collections 6 bps $87M
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Business Issue • Company-wide initiative to lower the churn rate among customers
• Focus on “high value” or “high value potential” customers
• Improve treatment strategy and relevance
Solution • Data management • Advanced analytics
Results/Benefits • Reduced churn by 40% • Increased customer loyalty and lifetime value • Increase of operational revenues by $1B over 3 years • Ability to uncover dissatisfaction drivers and tailor proactive churn
treatments
US Telco
Case Study
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1.1 Introduction
1.2 Competing On Analytics
1.3 The Data Explosion
1.4 Evolving Context of Marketing Analytics & Research
1.5 Questions
Module 1: The World of Marketing Is
Changing - Are You Being Left Behind?
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Key BUSINESS Trends Affecting Marketing
From Product to Customer
• Customer-centric business strategy
• The customer experience
• 360-degree customer view
Finding the Next Origin of Business Growth
• Consolidation/mergers/acquisitions
• Market expansion
• Efficiency & optimization
The Regulatory Rise
• Increased disclosure and transparency
• Privacy and information sharing
• Consumer contact rules
• Regulatory reform
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Key CONSUMER Forces Affecting Marketing
Consumer in Charge
• Rising expectations and more choice
• From right time to “real time”
• Demographic divide
Channel Adoption
• Mobile devices and consumer adoption
• Web 2.0 and the digital age
• Cross-channel usage
Huge Online and Social Adoption
• Social networking
• Consumer-controlled content and channels
• Consumer engagement
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A Broadened Definition of “The Customer”
The Consumer
The Citizen
The Subscriber
The Plan Member
The Patient
The Patron
...applicable across B2C & B2B
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Customer Intelligence Is Relevant Across Industries
Financial Services Insurance Retail
Hospitality &
Gaming Manufacturing
Government Marketing Service
Providers
Health &
Life Sciences
Utilities
Telco & Cable
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The Marketer Has An Evolving Mandate
Responsibilities
The Brand
The Marketing Campaign
Insights and
Analytics
The Customer
Experience
Integrated, multi-channel in/outbound conversations in real-time
Expectation
Sustain brand health in a rapidly changing virtual world
Expectation
Unearth and dynamically manage insights to drive action
Expectation
Deliver a branded customer experience in and outside of marketing
Expectation
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Huge Online and Social Consumer
Adoption 2B people online,100B monthly searches and 600MM people on social networks globally
Key Forces Affecting Marketers
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Ever-Growing and Converging Marketing
Channels Technology advances and consumer preferences driving new channels at unprecedented rates
Huge Online and Social Consumer Adoption
Key Forces Affecting Marketers
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Ever-Growing and Converging Marketing Channels
Huge Online and Social Consumer Adoption
Information Explosion
Business information doubling every 18 months with unstructured data representing 70% of it.
Key Forces Affecting Marketers
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Ever-Growing and Converging Marketing Channels
Huge Online and Social Consumer Adoption
The Speed of Business
Information traveling at unprecedented rates, compounded by rising consumer expectations.
Information Explosion
Key Forces Affecting Marketers
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Ever-Growing and Converging Marketing Channels
Huge Online and Social Consumer Adoption
The Speed of Business
Information Explosion
Key Forces Affecting Marketers
Accountability and
Need to do More
with Less Economic and competitive pressures putting
focus on marketing budgets and returns.
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Accountability and Need to do
More with Less
Ever-Growing and Converging Marketing Channels
Huge Online and Social Consumer Adoption
Increasingly Competitive &
Converging Markets Parity markets with limited differentiation . Fight for share of wallet.
The Speed of Business
Information Explosion
Key Forces Affecting Marketers
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Accountability and Need to do
More with Less
Ever-Growing and Converging Marketing Channels
Huge Online and Social Consumer Adoption
Increasingly Competitive & Converging Markets
The Speed of Business
Brand Health Less corporate trust compounded by brands being publicly scrutinized. Traditional mass marketing proving less impactful.
Information Explosion
Key Forces Affecting Marketers
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The Marketing Process
Risk Customer Service
Corporate Affairs
Merchandising
Finance
Operations
Online Mobile
In Person
Call Center
Direct Mail
Social
Campaign ERP Social CRM EDW Online
Optimization
Marketing Strategy
Marketing
Marketing Processes
Marketing Campaigns
Analytics
Data Integration
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The Data Integration & Management Challenge
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The Flood Of Data
• Customer data continues to flood the
organization exponentially
• Progressing from functional to strategic
– Namely how to capture, integrate, manage, analyze,
and apply knowledge/insight about customers
– Google Executive Chairman Eric Schmidt:
“We create as much information in two days
now as we did from the dawn of man through
2003.”
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Structured & Unstructured Data
• Company data: billing, usage, collections, set-top box,
customer, web interactions, campaign, and more!
• Consumer-generated data: Social media, blogs,
product reviews, and more!
Structured data
Semistructured data Unstructured data
70%
25%
5%
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“Big Data” Myths
• Data Volumes are “Exploding”
– Did Wal-Mart suddenly sell more stuff?
– Did NYSE suddenly do more stock trades?
– Did Netflix suddenly rent more movies?
– Did Amazon suddenly sell more books?
• This is existing data that
previously went un-analyzed:
A. Too large to manage
B. Too costly to store
C. Lack of “analytic chops” to capitalize
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“Big Data” - Why Now?
Three Vs?
1. Volume
2. Velocity
3. Variety
Relational
Complex, Unstructured
Source: IDC
.
The primary driver is Value…
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• Cost of storage dropping
“Big Data” - Why Now?
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Data - Prerequisite For Everything Analytical
“You can’t be analytical without data, and you can’t be
really good at analytics without really good data”
Davenport, Harris, Morison (2010)
Analytics at Work:
Smarter Decision Better Results
• Structure
• Uniqueness
• Integration
• Quality
• Access
• Privacy
• Governance
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Data Structure / Uniqueness / Integration
Structure
• Data structure affects analysis performance
• Transaction systems (tables), data cubes (limitations)
• Data arrays
• Unstructured data
Uniqueness
• Data only your company has access – proprietary
• Commercially available data – be the industry 1st
• Create new metrics and data fields
Integration
• Aggregate data from inside/outside your organization
• Consolidate silos across departments
• Data has to be sourced, cleaned, integrated
• Evolve to “one version of the truth”
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Data Quality / Access / Privacy
Quality
• Flawed data causes misleading results
• To fix problems - look at the data source
• Continuous process – data will never be perfect
• Start based on business objectives
Access
• Source data and load in a form for analytics
• Size or complexities can cause user issues
• Speed needs require data warehouse appliances
• Sample populations
Privacy
• Guard the information collected
• Well defined policies
• Privacy laws within territories or industries
• Don’t sell information without permission (opt-in)
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Data Governance
Governance
• Ensure data is useful for analysis
• Consistent, defined, sufficient quality, standardized, integrated, accessible
• Standard definitions and terminology
• Decide on investments
• Owners and stewards
• Analytical data advocates
• Business intelligence competency centers, analytical data advocate group, information management
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1.1 Introduction
1.2 Competing On Analytics
1.3 The Data Explosion
1.4 Evolving Context of Marketing Analytics & Research
1.5 Questions
Module 1: The World of Marketing Is
Changing - Are You Being Left Behind?
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„The Greatest Job In The World‟
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The Challenge of Digital Marketing
• As digital marketing continues to grow more
significant, new channels add complexity to the
design of a successful integrated campaign.
– It’s both a blessing and a curse for when an
integrated campaign goes viral
– Key Challenge: How do we do it again?
– No repeatable formulas
or clear attribution metrics
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Reactive Business Analytics
What happened?
How many, how often, where?
Where exactly is the problem?
What actions are needed?
Why is this happening?
What if these trends continue?
What will happen next?
What’s the best that can happen?
Optimization
Predictive Modeling
Forecasting
Statistical Analysis
Alerts
Query Drilldown
Ad hoc Reports
Std. Reports
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Proactive Business Analytics
What happened?
How many, how often, where?
Where exactly is the problem?
What actions are needed?
Why is this happening?
What if these trends continue?
What will happen next?
What’s the best that can happen?
Optimization
Predictive Modeling
Forecasting
Statistical Analysis
Alerts
Query Drilldown
Ad hoc Reports
Std. Reports
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Afternoon Workshop Preview
• What if I could?
– Automate the measurement of sentiment relevant to my
business goals from digital channels
– Capitalize on the hidden value in vast amounts of available
structured/unstructured data associated with my brand
– Become strategically more proactive to shifting (dynamic)
consumer trends
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The Marketing Process
Risk Customer Service
Corporate Affairs
Merchandising
Finance
Operations
Online Mobile
In Person
Call Center
Direct Mail
Social
Campaign ERP Social CRM EDW Online
Optimization
Marketing Strategy
Marketing
Marketing Processes
Marketing Campaigns
Analytics
Data Integration
![Page 88: Customer Intelligence & Analytics - Part I](https://reader036.fdocuments.net/reader036/viewer/2022062511/54bd7c4c4a795933698b457d/html5/thumbnails/88.jpg)
1.1 Introduction
1.2 Competing On Analytics
1.3 The Data Explosion
1.4 Evolving Context of Marketing Analytics & Research
1.5 Questions
Module 1: The World of Marketing Is
Changing - Are You Being Left Behind?