2. Module 2: Exploring the Idea & Value of
Marketing Analytic Techniques
2.1 Introduction
2.2 Data Mining Techniques For Marketing, Sales, & CRM
2.3 The Power of Analyzing Structured & Unstructured Data
2.4 The Competitive Advantage of an Integrated, Analytic
Marketing Platform
2.5 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 2: Exploring the Idea & Value of
Marketing Analytic Techniques
2.1 Introduction
2.2 Data Mining Techniques For Marketing, Sales, & CRM
2.3 The Power of Analyzing Structured & Unstructured Data
2.4 The Competitive Advantage of an Integrated, Analytic
Marketing Platform
2.5 Questions

5. 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

6. The Customer Lifecycle
• The business relationship with a customer
evolves over time
• Five phases
1. Prospects
2. Responders
3. New customers
4. Established customers
5. Former customers

7. Event-Based Relationships
• Primarily based on transactions
• Customer may or may not return
– Tracking customers over time may be
difficult or impossible
• Prospect communications
focused on message broadcasting
– Advertising
– Web ads
– Viral marketing
• Targeted, 1:1 messaging is challenging
• Analytic work focused on product, geography, and time

8. Subscription-Based Relationships
• Provide more natural opportunities
for understanding customers
– Offers opportunity for future cash flow
and customer interactions
• Can take many forms
– Billing relationships
– Affinity cards
– Website registrations
• The beginning and end of the relationship are two key events
 When these events are well-defined, survival analysis is a
good candidate for understanding the relationship duration

9. Customer Acquisition
• The process of attracting prospects and turning them into
customers
– Advertising
– Word-of-mouth
– Targeted marketing
• Data mining can play an important role
– Three questions
1. Who are the prospects?
2. When is a customer acquired?
3. What is the role of data mining?

10. Who Are the Prospects?
• Understanding prospects is important because messages
should be targeted to the appropriate audience
• Challenges
 Geographic expansion
 Changes to products, services,
and pricing
 Competition
• Will the past be a good predictor of the future?
– In most cases, the answer is “yes”
– The past has to be used intelligently

11. Prospecting Incorrectly
• NYC-based direct marketing company
– Large customer base in Manhattan
• Looking to expand into the suburbs
• DM campaigns have always been targeted to Manhattan
– Data mining model built from campaign responders
• Manhattan - high concentration of wealthy residents (model bias)
• Responders wealthier than most prospects in surrounding areas
• When the model was extended to areas outside of Manhattan,
what areas did the model choose?

12. Prospecting: What Is The Role Of Data Mining?
• Available data limits the role that data mining can play
• The goal is to target prospects that are:
– More likely to respond
– Become good customers
• Data availability falls into three categories
1. Source of prospect
2. Appended individual/household data
3. Appended demographic data at a geographic level
• Challenge: The echo (“halo”) effect

13. Prospecting: What Is The Role Of Data Mining?
• Identifying good prospects
– The need to define what it means
to be a “good prospect”
– Identify rules that allow for this type
of targeting
• Example: Response modeling
• Choosing a communications channel
– Mass media vs. direct-response media?
• Picking appropriate messages for different segments
– Price vs. convenience?

14. Customer Activation
• Provides a view of new customers at the point when they start
– This perspective is an important data source
– Often a useful predictor of long-term customer behavior
• The activation funnel
1. The sales lead
2. The order
3. The subscription
4. The paid subscription
• Data mining can play a role in understanding
whether or not customers are migrating
the way they should be

15. Customer Relationship Management
• The primary goal of CRM is to increase
the customer’s value
1. Up-selling
2. Cross-selling
3. Usage stimulation
4. Customer value calculation
• CRM is successful when customer messaging is highly relevant
– Data mining plays a key role in identifying relevant affinities
• Potentially, the single most important part of CRM is retaining
customers
– Predictive modeling is heavily applicable

16. Using Current Customers To
Learn About Future Prospects
• How to identify your best customers
– Start tracking customers before they become customers
• Marketing campaign data
• Cookie data
– Gather information from new customers at time of acquirement
• Golden opportunity - prospect to customer transition
• Geographic and demographic
– Model the relationship
• Customer longevity
• Customer value
• Default risk

17. CRM: What Is The Role Of Data Mining?
• Customers provide the richest source of data for mining
• Behavioral data provides the following opportunities:
1. Matching campaigns to customers
2. Reducing exposure to risk
3. Determining customer value
4. Cross-selling, up-selling, and making recommendations

18. Retention
• Attrition is a major application of data mining
• Challenges
1. Recognition
 What it is & when it occurs
2. Why it matters
3. Different kinds of attrition
 Two approaches
 Predicting who will leave
 Predicting how long customers
will stay

19. Win-back
• Even after customers have left, they can still
be lured back
– Data mining can explain why customers left
• Case Study: Media product boycott
– What do you do when the unexpected happens?
– Consumer backlash to end customer subscriptions
• How many stops can be attributed to the boycott?
• Who is stopping?
• Are they coming back?
– Challenges in tracking
– Manual investigation vs. text mining

20. Why Operationalize Analytics?
• Increase customer lifetime value with relevancy
• Maintain customer satisfaction proactively
• Interact with precise offers, messages, and communications
• Current/recent interaction may be the tipping point to
negative sentiment
• Significant events: sentiment/social media, interaction
points

21. Operationalizing Analytics – The Life Cycle
Acquisition Development Retention Churn/ Win-
back
Net Margin

22. Operationalizing Analytics – The Life Cycle
Acquisition Development Retention Churn/ Win-
back
Net Margin
Decisions points during acquisition:
• Looking at products and offers
• Comparing pricing
• Company can be scoring - credit worthiness

23. Operationalizing Analytics – The Life Cycle
Acquisition Development Retention Churn/ Win-
back
Decisions points during relationship
development:
Net Margin
• Service & product usage
• Customer user experience
• Cross & up-sell
• Bad debt detection and collection
• Customer service

24. Operationalizing Analytics – The Life Cycle
Acquisition Development Retention Churn/ Win-
back
Net Margin
Decisions points during retention:
• Targeted retention activities
• Complaint handling
• Renewal pricing, discounting & bundling
• Reactive retention

25. Operationalizing Analytics – The Life Cycle
Acquisition Development Retention Churn/ Win-
back
Net Margin
Decisions points during churn/win-back:
• Win-back discount and bundle pricing
• Trigger campaigns for future reacquisition

26. Execute on Actionable Insights

27. Applying Predictive Models to Marketing
Strategy

28. Proactively Manage the Customer Experience
Preventive Actions Predictive Actions Reactive Actions
Action is
identified

29. Define Customer Value
A smaller percentage of your customer base is driving the
majority of the profit.
Migrate /
Spend Keep &
Shift to
to keep migrate
lower cost
May be some of
your largest
customers
Source: Gartner

30. 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?

31. Module 2: Exploring the Idea & Value of
Marketing Analytic Techniques
2.1 Introduction
2.2 Data Mining Techniques For Marketing, Sales, & CRM
2.3 The Power of Analyzing Structured & Unstructured Data
2.4 The Competitive Advantage of an Integrated, Analytic
Marketing Platform
2.5 Questions

32. Set-top Box Analytics Situation Slide
• Marketing: How can I increase revenue and lower churn?
Critical • Programming: How do I know viewership across
Business programs?
Issue • Advertising: How can I drive up better yields on my ad
units?
Current • Using 3rd party data
Capabilities • Difficulty mining vast amount of viewing information
• Capabilities for sourcing and preparing the set-top box
New data
Capabilities • Analytics for uncovering insight and unknown patterns
• Interactive dashboard solution for executive decisioning

33. Your Set-top Box Data
Who is What & when What kind of
watching? are they customer are they?
watching?
Valuable Resource
Augmenting Existing Data
Smarter, More Accurate, Timely, Control

34. Set-top Box Analytics Benefits
Analytic Insights Provide Value for Multiple Departments
Set-top Box Analytics
Audience Intelligence
Marketing Programming Advertising
1. Churn prevention 1. Insights for 1. Higher ROI on
2. Up-sell / cross-sell program addressable
3. Optimize negotiations advertising
packaging 2. Uncover 2. Uncover unknown
4. Drive engagement replacement targets for
across channels programming addressable
3. Identify new advertising
program targets 3. Optimize
4. Produce Tier 2 advertising
viewer insights inventory

35. Audience Intelligence
Audience
Viewership
Media Audience
Planning Forecasting
Likelihood to Audience
Watch Behavior
Audience
Discovery

36. Audience Intelligence
Audience
Viewership
Media When To Audience
Planning Target Forecasting
What To Who To
Target Target
Likelihood to How To
Audience
Watch Behavior
Target
Audience
Discovery

37. Data Process

38. Set-top Box Data
Raw Set-top box Data
duration
+ Transform Data
duration
+ Incorporate Other Data
HH_ID device_id Timeframe channel program (secs) Timeframe (min) Income LOB Plan
123 4567 5/2/11 9:00 17 22 1200 week1_9-9:30am 20 150000 3 Triple-play bundle A
123 4567 5/2/11 9:20 15 45 300 week1_9-9:30am 5 150000 3 Triple-play bundle A
123 4567 5/2/11 9:25 3 55 300 week1_9-9:30am 5 150000 3 Triple-play bundle A
123 4567 5/2/11 9:30 17 66 900 week1_9:30-10am 15 150000 3 Triple-play bundle A
123 4567 5/2/11 9:45 15 77 900 week1_9:30-10am 15 150000 3 Triple-play bundle A
1. Source Set-top Box Data 2. Append Data
• HD vs. Non-HD • Billing
• Weekday vs. Weekend • Account
• Time of Day (Morning, Night) • Calls to Care
• Day of the Week (Mon, Tues, etc.) • 3rd Party (demographic, Axciom,
• Channels Experian)
• Channel Category • Tribune
• Programs • Social Media (Twitter, Facebook)
• Program Category (Genre, 1st
run/2nd run)
• Series Usage Levels (Avid 3. Aggregate & Build Viewing
Watchers, Fly-bys) Categories
• Last Tuning Event • Daily, Weekly, Monthly, Series
• Combination of Watching • Sums & Averages of Durations
• Tune-aways • Viewing rates & Change in
• Time Slot Viewing rates
• Geographic

39. Marketing Segmentation
Premium Couch
Price Conscious
Potatoes
Families
Family Viewers with
Premiums
Stay Home
Moms & Kids
Price is not
an Object
Multi-cultural
Programming

40. Programming Segmentation
Only a Network A
Weekend Watcher Network A
Weekend News Crazy Network A
Network A
Sampler Movie
Watcher
7% 12% Network
22% 8%
Not a Network A A Fly-
7%
Watcher bys
12%
13%
6%
2% 11%
Network A
Sports Fan
Network A Weekday Fan
Weekday
Network A Network A
Devoted Fan Frequent Watcher

41. STB Data - Advertising Segmentation
Automotive Wireless
7% 5%
40%
25%
Movie Studios Financial Services
23%
Healthcare

42. Audience Insight

43. Dashboards

44. Segmentation

45. Segmentation Details

46. Network Analysis

47. Mapping Visualization

48. Path Analysis

49. What-if Analysis

50. Content Analytics Situation Slide
Critical • As a digital publisher, do you provide the most
engaging, relevant content possible?
Business • Is your content management strategy driven by a deep
Issue understanding of your audience’s evolving behavior?
• Lose audience share to competitors
Importance • Reduced halo effect around other revenue streams
• Flat or decreasing marketing performance metrics
• How to organize content for dynamic categorization?
Challenges • How to analyze the data for actionable insight?
• How to become more proactive vs. reactive?

51. Case Study: Tribune Company
• Business Issue: To accurately define and categorize
content efficiently to deliver highly relevant information to
its online readership
• Outcome: Analytic approaches enabled the ability to
define, apply and push the right content, in the right
context, to the right audience in the most optimized way
• Usage Examples
– Repurposing content
– Driving ad revenue
– Improving search performance

52. Text Analytics
Text Mining
Natural
Ontology Sentiment
Language
Management Analysis
Processing
Content
Categorization

53. Text Analytics
Natural Language Processing (NLP)
 Support for multiple languages
 Stemming to locate the various forms of
an input
 Part-of-speech recognition and tagging
Natural
Language
to recognize nouns, verbs, adjectives,
Processing etc.
 Word and sentence tokenization:
Identify distinct words or expressions
 Information extraction: Facts and
events, people, dates, places,
sentiment, emotion, etc…

54. Text Analytics
Text Mining
Natural
Ontology Sentiment
Language
Management Analysis
Processing
Content
Categorization

55. Text Analytics
Insight
Text
Discovery
Mining
Top Up
Natural
Ontology Sentiment
Language
Management Analysis
Processing
Down Bottom
Content
Information Categorization
Organization

56. Usage Example: Chicago Tribune

57. Usage Example: New York Times
Real-Time
Deployment
Topics
Automatic
Entities
Extraction Automatic
Categorization

58. Text Analytics
Insight
Text
Discovery
Mining
Top Up
Natural
Ontology Sentiment
Language
Management Analysis
Processing
Down Bottom
Content
Information Categorization
Organization

59. Potential Data Sources
Data
Management

60. Data Cleansing
• Unstructured data, in the form of text, when captured, presents
unique challenges
– Correctly structure the data and clean it is a priority
– Technology needs to have the ability to:
» Eliminate irrelevant information
» Quantity ≠ Quality
» Miss-spelings
» Treat acronyms and abbreviations (e.g. “LOL”)
» Pr☺f@nity
» *Punctuation*

61. Sentiment Analysis
• The action of identifying the expressed sentiments by customers,
partners, suppliers and employees
• Three levels
– Polarity indicator: Positive, negative, neutral
• Why is it important to measure sentiment?
• Public perception
• Traditional methodologies
• Statistical and rules-based
• Typically use one or the other
– Common issues with measuring
polarity accurately
– Hybrid approach advantages
• Overall vs. granular/feature-level sentiment

62. Overall vs. Granular/Feature-level Sentiment
 Good, but a little outdated. I bought the Nikon Coolpix L10 as my
first digital compact P&S camera. I had it for a couple of weeks,
until mine had a 'lens error' that basically made the camera
inoperable (it was stuck open). It might've been due to batteries
running low, but I tried another set.
 The picture quality from the L10 was very good, a bit of barrel
distortion was noticed in the wide angle and shooting tall
skyscrapers (noticed by the curve along the side of the frame
where the buildings are supposed to be straight).Another gripe I
had with the camera was how slow the auto-focus was. It would
basically go through the whole range of focus every time I
pressed the shutter half-way and then some
 Eventually a lot of my pictures came out blurry, including outdoor
overcast days with 3x optical zoom. Basically anytime there's
zoom & less than ideal lighting, I would have to have rock steady
hands to get non-blurry pictures. Overall it's a good camera if you
can overlook the issues I mentioned.
Product: Nikon Coolpix L10, Polarity: mixed
Feature: Picture Quality, Polarity: positive
Feature: Autofocus, Polarity: negative

63. Case Study: Yogurt Brand
• Business Issue: Search sources of consumer-generated
content and social media activity to find and analyze
opinions about brand and products
• Outcome: Sentiment analysis technology enabled the
ability to:
– Take targeted measures based on Web feedback
– Align with customers' needs by analyzing indicators that
reveal strengths and weaknesses
– Define new products
– Discover innovative uses
for existing products

64. Text Analytics
Insight
Text
Discovery
Mining
Top Up
Natural
Ontology Sentiment
Language
Management Analysis
Processing
Down Bottom
Content
Information Categorization
Organization

65. Discover vs. Define
• How does an organization proactively identify new
topics, new terms, and new information being
generated by the target consumer?
– Text mining: Let the data speak for itself!
UP TOP
BOTTOM DOWN

66. Text Mining
• The process of discovering and extracting
meaningful patterns and relationships from
text collections
Text Data Natural
Language
Mining Mining Processing
• Text Mining is not searching, but the
concepts are related
Mine
Discover Search

67. Case Study: University of Louisville
• Business Issue: Analyze text-based medical records
and healthcare reporting
• Outcome:
– Extract and explore information from thousands of medical
records - improving patient outcomes
– Examine relationships between physician practices and
patient outcome records
– Pull relevant information from patient charts and easily
look at patterns in patient treatments and patient
outcomes

68. Web Analytics Situation Slide
Critical • How do I increase my understanding of anonymous,
digital visitation?
Business • How can I increase the value of my digital property’s
Issue advertising inventory?
• Inability to accurately segment digital visitation
Importance • Lose advertiser share to competitors
• Lose revenue
• How do I improve targeting strategies at anonymous
visitors?
Challenges • How do I improve my ad inventory performance?
• How to analyze the data for proactive insight?

69. Advancing Web Analytics
Pull Web analytic data (e.g. Create Customer State Develop “look-a-like” models to
1 Omniture) and load into 2 Vector to record customer 3 gain intelligence on registered
advanced analytic platform web behavior across time visitors, and apply insights to the
unregistered
CSV
Customers
Dimensions
Utilize “look-a-like” model
Perform analysis of results 4 results to offer demographic
5
and reiterate the process and behavioral ad targeting to
all digital visitors

70. Module 2: Exploring the Idea & Value of
Marketing Analytic Techniques
2.1 Introduction
2.2 Data Mining Techniques For Marketing, Sales, & CRM
2.3 The Power of Analyzing Structured & Unstructured Data
2.4 The Competitive Advantage of an Integrated, Analytic
Marketing Platform
2.5 Questions

71. 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

72. The Marketing Process
Mobile Online Finance Risk
Call Customer
Center Service
In Person Merchandising
Social Corporate
Affairs
Direct Mail Marketing Operations
Marketing Mix Real-Time Campaign
Optimization Management
Analysis Decisioning
Marketing Marketing
Performance Operations Online Customer Social Media
Management Management Behaviour
Data Mining & Sentiment & Customer
Customer Analytics
Unstructured Profitability
Analytics Data Analysis & Forecasting
Data Integration
ERP CRM EDW Online Social Campaign

73. Module 2: Exploring the Idea & Value of
Marketing Analytic Techniques
2.1 Introduction
2.2 Data Mining Techniques For Marketing, Sales, & CRM
2.3 The Power of Analyzing Structured & Unstructured Data
2.4 The Competitive Advantage of an Integrated, Analytic
Marketing Platform
2.5 Questions