This document discusses how the field of marketing analytics is evolving due to the explosion of available data and increased use of analytics. It describes how companies now use analytics throughout their operations to gain insights from data and make better decisions. The document also outlines some common areas where companies apply analytics, such as customer acquisition, pricing, and forecasting. It cautions that analytics must be implemented carefully and should be guided by the data rather than preconceptions to avoid bias.

2. Module 1: The World of Marketing Is
Changing - Are You Being Left Behind?
1.1 Introduction
1.2 Competing On Analytics
1.3 The Data Explosion
1.4 Evolving Context of Marketing Analytics & Research
1.5 Questions

3. • 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)

4. Module 1: The World of Marketing Is
Changing - Are You Being Left Behind?
1.1 Introduction
1.2 Competing On Analytics
1.3 The Data Explosion
1.4 Evolving Context of Marketing Analytics & Research
1.5 Questions

5. „Old Spice‟ Campaign Case Study

6. Achieving Success With Business Analytics
What’s the best that can happen?
Optimization
What will happen next?
Predictive
Modeling
What if these trends continue?
Forecasting
Why is this happening? Statistical
Analysis
Alerts
Query What actions are needed?
Drilldown
Ad hoc Where exactly is the problem?
Reports
Std. How many, how often, where?
Reports
What happened?

7. 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

8. Data Deluge

9. 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.
...

10. 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.
...

11. 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.
...

12. 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.

13. The Business Analytics Challenge
Getting anything useful out of tons and tons of data

14. Hope For The Data Deluge
+ analytical tools
= actionable knowledge

15. Changes In The Analytical Landscape
Historically…
Models
Analytical Modelers Management
Historically, analytics have
typically been handled in
the “back office,” and
information was shared
only by a few individuals.

16. 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

17. 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

18. 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

19. Changes In The Analytical Landscape
Now… Operations Targeting
Proliferation of
Models
Customer Customers
Analytical Modelers
Service
Retail Suppliers
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. Promotions Employees

20. 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.

21. The Data
Experimental Opportunistic
Purpose Research Operational
Value Scientific Commercial
Generation Actively controlled Passively observed
Size Small Massive
Hygiene Clean Dirty
State Static Dynamic

22. The Data: Disparate Business Units
Marketing Invoicing Risk
Acquisitions Operations Sales

23. 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

24. 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

25. 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

26. 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?

27. 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?

28. 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

29. 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

30. Areas Where Analytics Are Often Used
• New customer acquisition Which residents in a ZIP
• Customer loyalty code should receive a
• Cross-sell / up-sell coupon in the mail for a
new store location?
• Pricing tolerance
• Supply optimization
• Staffing optimization
• Financial forecasting
• Product placement
• Churn
• Insurance rate setting
• Fraud detection
• …

31. Areas Where Analytics Are Often Used
• New customer acquisition
• Customer loyalty What advertising strategy
• Cross-sell / up-sell best elicits positive
sentiment toward the
• Pricing tolerance
brand?
• Supply optimization
• Staffing optimization
• Financial forecasting
• Product placement
• Churn
• Insurance rate setting
• Fraud detection
• …

32. Areas Where Analytics Are Often Used
• New customer acquisition
• Customer loyalty
• Cross-sell / up-sell What is the best next
• Pricing tolerance product for this customer?
• Supply optimization
• Staffing optimization
• Financial forecasting
• Product placement
• Churn
• Insurance rate setting
• Fraud detection
• …

33. Areas Where Analytics Are Often Used
• New customer acquisition
• Customer loyalty
• Cross-sell / up-sell
• Pricing tolerance What is the highest price
• Supply optimization that the market will bear
• Staffing optimization without substantial loss of
demand?
• Financial forecasting
• Product placement
• Churn
• Insurance rate setting
• Fraud detection
• …

34. Areas Where Analytics Are Often Used
• New customer acquisition
• Customer loyalty
• Cross-sell / up-sell
• Pricing tolerance
• Supply optimization How many 60-inch HDTVs
• Staffing optimization should be in stock?
• Financial forecasting
• Product placement
• Churn
• Insurance rate setting
• Fraud detection
• …

35. Areas Where Analytics Are Often Used
• New customer acquisition
• Customer loyalty
• Cross-sell / up-sell
• Pricing tolerance
• Supply optimization
• Staffing optimization What are the best times
• Financial forecasting and best days to have
technical experts on the
• Product placement
showroom floor?
• Churn
• Insurance rate setting
• Fraud detection
• …

36. Areas Where Analytics Are Often Used
• New customer acquisition
• Customer loyalty
• Cross-sell / up-sell
• Pricing tolerance
• Supply optimization
• Staffing optimization
• Financial forecasting What weekly revenue
• Product placement increase can be expected
• Churn after the Mother’s Day
sale?
• Insurance rate setting
• Fraud detection
• …

37. 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 Will oatmeal sell better
• Churn near granola bars or near
• Insurance rate setting baby food?
• Fraud detection
• …

38. 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 Which customers are most
• Insurance rate setting likely to switch to a
• Fraud detection different wireless provider
in the next six months?
• …

39. 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 How likely is it that this
• Fraud detection individual will have a claim?
• …

40. 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?

41. When Analytics Are Not Helpful
• Snap decisions required Deciding when to run
• Novel approach (no previous from danger
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

42. When Analytics Are Not Helpful
• Snap decisions required
• Novel approach (no previous Predicting the adoption of
data possible) a new technology
• 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

43. When Analytics Are Not Helpful
• Snap decisions required
• Novel approach (no previous
data possible)
• Most salient factors are rare Planning contingencies
(making decisions to work for employees winning
around unlikely obstacles or the lottery
miracles)
• Expert analysis suggests a
particular path
• Metrics are inappropriate
• Naïve implementation of
analytics
• Confirming what you already
know

44. 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 The seasoned art critic
particular path can recognize a fake
• Metrics are inappropriate
• Naïve implementation of
analytics
• Confirming what you already
know

45. 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 Predicting athletes’
• Metrics are inappropriate salaries or quantifying
• Naïve implementation of love
analytics
• Confirming what you already
know

46. 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 Only looking at one
analytics variable at a time
• Confirming what you already
know

47. 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 Ignoring variables that
know might be important

48. The Fallacy Of Univariate Thinking
What is the most important cause of churn?
Prob(churn)
International Daytime
Usage Usage

49. 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

50. 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

51. 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

52. 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

53. Customer Lifecycle – Touch Points

54. Obtaining 360 Degree View Of The Customer
Activ-
Social ation Firmo-
Network graphics
Usage Demo-
graphics
Care Point of
360 Sale
Degree
Customer
Hard- View Service,
ware Repair
VOD,
Network
Games
Commu-
Billing ni-
Collect- cations
ions

55. Large Telco With Industry-leading Churn Rate
Churn Churn
Reduction By Reduction Value ($)
Reason
Equipment 9 bps $121M
Usage 16 bps $163M
Network 15 bps $158M
Active Issue
Resolution 11 bps $110M
Contract
Renewal 25 bps $273M
Sales Channel /
Credit & 6 bps $87M
Collections
Total 82 bps $912M

56. Case Study
US Telco
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

57. Module 1: The World of Marketing Is
Changing - Are You Being Left Behind?
1.1 Introduction
1.2 Competing On Analytics
1.3 The Data Explosion
1.4 Evolving Context of Marketing Analytics & Research
1.5 Questions

58. 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

59. 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

60. A Broadened Definition of “The Customer”
 The Consumer
 The Citizen
 The Subscriber
 The Plan Member
 The Patient
 The Patron
...applicable across B2C & B2B

61. Customer Intelligence Is Relevant Across Industries
Financial Services Insurance Retail
Hospitality &
Telco & Cable Manufacturing
Gaming
Government Marketing Service Health &
Providers Life Sciences
Utilities

62. The Marketer Has An Evolving Mandate
Expectation
Expectation Deliver a branded
Integrated, multi-channel customer experience
in/outbound conversations in and outside of
in real-time marketing
The Marketing
Campaign
The
The Customer
Brand Experience
Responsibilities
Expectation
Sustain brand health
in a rapidly changing
virtual world Insights
and
Analytics
Expectation
Unearth and dynamically
manage insights to drive action

63. Key Forces Affecting Marketers
Huge Online and
Social Consumer
Adoption
2B people online,100B monthly searches and
600MM people on social networks globally

64. Key Forces Affecting Marketers
Huge Online and Social
Consumer Adoption Ever-Growing and
Converging Marketing
Channels
Technology advances and consumer preferences
driving new channels at unprecedented rates

65. Key Forces Affecting Marketers
Huge Online and Social Ever-Growing and Converging
Consumer Adoption Marketing Channels Information
Explosion
Business information doubling every 18
months with unstructured data
representing 70% of it.

66. Key Forces Affecting Marketers
Huge Online and Social Ever-Growing and Converging Information Explosion
Consumer Adoption Marketing Channels
The Speed of
Business
Information traveling at unprecedented
rates, compounded by rising consumer
expectations.

67. Key Forces Affecting Marketers
Huge Online and Social Ever-Growing and Converging Information Explosion
Consumer Adoption Marketing Channels
The Speed of Business
Accountability and
Need to do More
with Less
Economic and competitive pressures putting
focus on marketing budgets and returns.

68. Key Forces Affecting Marketers
Huge Online and Social Ever-Growing and Converging Information Explosion
Consumer Adoption Marketing Channels
The Speed of Business
Increasingly
Competitive &
Converging Markets Accountability and Need to do
Parity markets with limited differentiation . More with Less
Fight for share of wallet.

69. Key Forces Affecting Marketers
Huge Online and Social Ever-Growing and Converging Information Explosion
Consumer Adoption Marketing Channels
Brand Health
Less corporate trust compounded by The Speed of Business
brands being publicly scrutinized.
Traditional mass marketing proving
less impactful.
Increasingly Competitive &
Converging Markets Accountability and Need to do
More with Less

70. The Marketing Process
Mobile Online Finance Risk
Call Customer
Center Service
In Person Merchandising
Social Corporate
Affairs
Direct Mail Marketing Operations
Optimization
Marketing Marketing Marketing
Strategy Processes Campaigns
Analytics
Data Integration
ERP CRM EDW Online Social Campaign

71. The Data Integration & Management Challenge

72. 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.”

73. 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
25%
70% 5%
Unstructured data Semistructured data

74. “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

75. “Big Data” - Why Now?
Three Vs?
Complex, Unstructured 1. Volume
2. Velocity
3. Variety
Relational
The primary driver is Value…
Source: IDC
.

76. “Big Data” - Why Now?
• Cost of storage dropping

77. 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”
• Structure
• Uniqueness
• Integration
• Quality
• Access
• Privacy
• Governance
Davenport, Harris, Morison (2010)
Analytics at Work:
Smarter Decision Better Results

78. 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”

79. 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)

80. 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

81. Module 1: The World of Marketing Is
Changing - Are You Being Left Behind?
1.1 Introduction
1.2 Competing On Analytics
1.3 The Data Explosion
1.4 Evolving Context of Marketing Analytics & Research
1.5 Questions

82. „The Greatest Job In The World‟

83. 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

84. Reactive Business Analytics
What’s the best that can happen?
Optimization
What will happen next?
Predictive
Modeling
What if these trends continue?
Forecasting
Why is this happening? Statistical
Analysis
Alerts
Query What actions are needed?
Drilldown
Ad hoc Where exactly is the problem?
Reports
Std. How many, how often, where?
Reports
What happened?

85. Proactive Business Analytics
What’s the best that can happen?
Optimization
What will happen next?
Predictive
Modeling
What if these trends continue?
Forecasting
Why is this happening? Statistical
Analysis
Alerts
Query What actions are needed?
Drilldown
Ad hoc Where exactly is the problem?
Reports
Std. How many, how often, where?
Reports
What happened?

86. 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

87. The Marketing Process
Mobile Online Finance Risk
Call Customer
Center Service
In Person Merchandising
Social Corporate
Affairs
Direct Mail Marketing Operations
Optimization
Marketing Marketing Marketing
Strategy Processes Campaigns
Analytics
Data Integration
ERP CRM EDW Online Social Campaign

88. Module 1: The World of Marketing Is
Changing - Are You Being Left Behind?
1.1 Introduction
1.2 Competing On Analytics
1.3 The Data Explosion
1.4 Evolving Context of Marketing Analytics & Research
1.5 Questions