2. Module 4: An Evolutionary Process - Moving
Toward Analytically Driven Marketing
3.1 Introduction
3.2 Marketing optimization
3.3 The art and science of the marketing mix
3.4 Real-world, success case studies
3.5 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 4: An Evolutionary Process - Moving
Toward Analytically Driven Marketing
3.1 Introduction
3.2 Marketing optimization
3.3 The art and science of the marketing mix
3.4 Real-world, success case studies
3.5 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 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
Analytics
Data Integration
ERP CRM EDW Online Social Campaign

7. Optimization Defined
Optimization
A computational problem in
which the objective is to
find the best of all
feasible solutions

8. The Relationship Marketing Context
• Many customers, offers, channels
• Managing the contact strategy
• Looking ahead and behind
• How do you allocate offers effectively
to maximize return?
• Many constraints impact decisions
 Budgets, resources, policies
• How to respect constraints?
• How to reconcile competing goals?
• How to plan effectively for change?

9. Marketing Optimization
Marketing Optimization “What should I do to achieve the best
results?“
Marketing Simulation “What would happen?"
Business Value
Predictive Modeling “How likely are my customers to respond to
an offer?”
Marketing Dashboard “How many new customers did we get last
Data Quality, month? How much customer attrition?"
Integration “How can we trust analysis if we don’t trust the data?”
Data Access “What measures are available to better understand our
business?”
Reactive Proactive Predictive Strategic
Intelligence

10. Massive Problem - Potential Choices
Product A
Product B
Product C

11. Marketing Optimization Applications
• Financial Services
– Insurance policy offers
– Credit line increase/decrease
– APR to offer on balance transfer offers
• Telecom
– Complex cell phone plan offers
– Bundled services
– Cross channel offers with different execution costs
• Hospitality (Hotels, Casinos)
• Loyalty offers
• Retail
• Personalized coupons (POS)
• Offer prioritization and collisions
• Contact stream optimization

12. Do All Marketing Approaches
Yield The Same Results?
10–100+ %
Optimization
- Solves by holistic
5-10 % approach
- Factors all constraints
Customer Rules - Determines the best
result
- First In, First Out
? - Prioritized by
Customer/Campaign
Prioritization - Fails in the face of
constraints
- First In, First Out
- Prioritized by
Campaign
- Does not provide
best combination

13. Optimization Techniques Example
• Lines of business = 3
• Return = expected value (probability*expected revenue)
• Business objective = maximise value
• Constraints: Each customer is assigned to at most 1 campaign
Each campaign can have at most 3 customers
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75
Campaign C
3 60 75 65
4 55 80 75
5 75 60 50
6 75 65 60
7 80 70 75 Campaign B Campaign A
8 65 60 60
9 80 110 75

14. Optimization Techniques –
Campaign Prioritization
• Campaigns assigned a priority
• Customers allocated to campaigns by expected customer value
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75
3 60 75 65
Campaign C
4 55 80 75
5 75 60 50
6 75 65 60
7 80 70 75
Campaign B Campaign A
8 65 60 60
9 80 110 75

15. Cross-channel Optimisation
Campaign Prioritization
Constraints: Expected Return: 260
???
1 customer - 1 campaign
1 campaign - 3 customers
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75 Campaign C
3 60 75 65
4 55 80 75
5 75 60 50
6 75 65 60
Campaign B Campaign A
7 80 70 75
8 65 60 60
9 80 110 75

16. Campaign Prioritization
Constraints: Expected 485
260
1 customer - 1 campaign Return:
1 campaign - 3 customers
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75
Campaign C
3 60 75 65
4 55 80 75
5 75 60 50
6 75 65 60
Campaign B Campaign A
7 80 70 75
8 65 60 60
9 80 110 75

17. Campaign Prioritization
Constraints: Expected Return: 655
485
1 customer - 1 campaign
1 campaign - 3 customers
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75
Campaign C
3 60 75 65
4 55 80 75
5 75 60 50
6 75 65 60
Campaign B Campaign A
7 80 70 75
8 65 60 60
9 80 110 75

18. Optimization Techniques - Customer Rules
• Customers assigned a priority
• Campaigns allocated to customers by expected customer value
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75
Campaign C
3 60 75 65
4 55 80 75
5 75 60 50
6 75 65 60
Campaign B Campaign A
7 80 70 75
8 65 60 60
9 80 110 75

19. Customer Rules
Constraints: Expected Return: 120
???
1 customer - 1 campaign
1 campaign - 3 customers
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75
Campaign C
3 60 75 65
4 55 80 75
5 75 60 50
6 75 65 60
Campaign B Campaign A
7 80 70 75
8 65 60 60
9 80 110 75

20. Customer Rules
Constraints: Expected Return: 195
1 customer - 1 campaign
1 campaign - 3 customers
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75
Campaign C
3 60 75 65
4 55 80 75
5 75 60 50
6 75 65 60
7 80 70 75
Campaign B Campaign A
8 65 60 60
9 80 110 75

21. Customer Rules
Constraints: Expected Return: 195
270
1 customer - 1 campaign
1 campaign - 3 customers
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75
Campaign C
3 60 75 65
4 55 80 75
5 75 60 50
6 75 65 60
Campaign B Campaign A
7 80 70 75
8 65 60 60
9 80 110 75

22. Customer Rules
Constraints: Expected Return: 270
350
1 customer - 1 campaign
1 campaign - 3 customers
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75
Campaign C
3 60 75 65
4 55 80 75
5 75 60 50
6 75 65 60
7 80 70 75
Campaign B Campaign A
8 65 60 60
9 80 110 75

23. Customer Rules
Constraints: Expected Return: 425
1 customer - 1 campaign
1 campaign - 3 customers
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75
Campaign C
3 60 75 65
4 55 80 75
5 75 60 50
6 75 65 60
7 80 70 75
Campaign B Campaign A
8 65 60 60
9 80 110 75

24. Customer Rules
Constraints: Expected Return: 500
425
1 customer - 1 campaign
1 campaign - 3 customers
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75
Campaign C
3 60 75 65
4 55 80 75
5 75 60 50
6 75 65 60
7 80 70 75
Campaign B Campaign A
8 65 60 60
9 80 110 75

25. Customer Rules
Constraints: Expected Return: 500
580
1 customer - 1 campaign
1 campaign - 3 customers
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75
Campaign C
3 60 75 65
4 55 80 75
5 75 60 50
6 75 65 60
7 80 70 75
Campaign B Campaign A
8 65 60 60
9 80 110 75

26. Customer Rules
Constraints: Expected Return: 640
1 customer - 1 campaign
1 campaign - 3 customers
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75
Campaign C
3 60 75 65
4 55 80 75
5 75 60 50
6 75 65 60
7 80 70 75
Campaign B Campaign A
8 65 60 60
9 80 110 75

27. Customer Rules
Constraints: Expected Return: 715 +60
1 customer - 1 campaign
1 campaign - 3 customers
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75
Campaign C
3 60 75 65
4 55 80 75
5 75 60 50
6 75 65 60
7 80 70 75
Campaign B Campaign A
8 65 60 60
9 80 110 75

28. Optimization Techniques - Optimization
 Business objectives, constraints, contact policies define ‘priority’
 Optimization decides allocation
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75
3 60 75 65 Campaign C
4 55 80 75
5 75 60 50
6 75 65 60
7 80 70 75 Campaign B Campaign A
8 65 60 60
9 80 110 75

29. Optimization
Constraints: Expected Return: 745 +30
1 customer - 1 campaign
1 campaign - 3 customers
Client Camp’ A Camp’ B Camp’ C
1 100 120 90
2 50 70 75
Campaign C
3 60 75 65
4 55 80 75
5 75 60 50
6 75 65 60
7 80 70 75
Campaign B Campaign A
8 65 60 60
9 80 110 75

30. Marketing Optimization: Process Flow
Planned
Campaigns
Eligible
Customers Marketing Optimization
Identify &
Define Review
Model Execute
Optimization Optimization
Scores Optimal
Scenarios Results
Outcome
Contact “What-If Analysis”
Policy
Optimization Parameters:
• Objective
• Suppression Rules
• Constraints:
• Budget
• Capacity
• Contact / Blocking Policies

31. Case Study: Commerzbank, Germany
Challenges Business Impact
• 4 million customers, 20 offer types • POV: Up to 80% ROI improvement
• Optimize utilization of consultants • Production: 50% yield with the
same budget
• Optimize Yield vs. Budget
• ROI increased by 407%
• Optimize Marketing ROI (revenue /
cost)
"We have compared SAS intensively with other manufacturers offerings. The result was
impressive: SAS Marketing Optimization is exactly the solution we were looking for. We are +407%
setting an industry Benchmark” ROI
Heiko Güthenke, Department Director Customer & Business Analysis

32. More Case Studies…
Client Name Benefits
Vodafone (Australia) • 3-10x Response Rate increase
• Improve campaign ROI by 4x
• 30% reduction in campaign costs
Scotiabank • 50% Campaign ROI improvement
Major Insurer • 12% increase in revenue; 52% in earnings
• Savings of >$4 million per year
U.S. Regional Telco • $6 million incremental LTV in the 1st month
Global Telco • Reduced call center contacts by 25% without
decreasing effectiveness
#1 Market Share European • Individualized targeting of monthly coupon
Retailer mailers
• Increased offer response rates
• Decrease mailing costs

33. Module 4: An Evolutionary Process - Moving
Toward Analytically Driven Marketing
3.1 Introduction
3.2 Marketing optimization
3.3 The art and science of the marketing mix
3.4 Real-world, success case studies
3.5 Questions

34. 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
Analytics
Data Integration
ERP CRM EDW Online Social Campaign

35. Increased Complexity With Marketing
How do you decide the right mix across all channels?
Web Web Social Media & Direct Word of Customer
Sales
(Corp) (eCommerce) Media Ads Mail Mouth Service
Advertising
Email & Social Retail
Interactive Direct 1:1 &
Mobile Marketing Marketing
Promotions

36. Above The Line…Below The Line…
» Above the Line » Below the Line

37. Above the Line…Below the Line…
Media Planner/Buyer • How did we perform across products, geographies, campaign types?
Brand Manager • What marketing activities drove our new sales?
• What if we move funds from traditional to online marketing?
Interactive Marketing • What actions/decisions to we make for various scenarios?
• MOST of my marketing data is in silos…can I leverage it for analysis?
Marketing Planning
» Above the Line
» Below the Line
IT
• How can I get the right offer, to the right person via
the right channel? Interactive Marketing
• Can I coordinate my multi-channel campaign efforts? Director of Database Mkt
• Can I be relevant with EVERY interaction, every
time? Campaign Planner/Designer
Marketing Operations

38. Marketing Challenge: Financial Pressures
• Aggressive corporate goals & objectives
• Increased accountability and scrutiny
into marketing budgets
• Reductions in budgets

39. Questions Marketing Mix can Address
• How can I still achieve my marketing goals while facing
budget cuts?
• I am below target, how do I re-allocate my marketing
budget to hit targets?
• How do I decide where to invest my marketing budget to
support a product portfolio?
• How and where do I invest in social media to maximize
business impacts?
• Where do I increase marketing investments to achieve
higher returns?

40. What is Marketing Mix Modeling?
A data driven analytic process that quantifies the
relationship between drivers/influencers of sales and the
resulting sales across channels
• Understand the past performance of sales & marketing activities
• Analyze and assess average ROI and marginal ROI
• Evaluate marketing investment among ever increasing media options
• Compare and assess different future marketing spending plans

41. Marketing Mix Technology
Analytic Dashboards

42. Technology Capabilities
Analytic dashboards
• Analytic data warehouse surfaced through
interactive dashboards
• All media and promotions display in one location
with prebuilt reports delivering summary and Analytic Dashboards
detailed results
Powerful analytic tools
• Understand the impact of advertising on sales
and incorporate into response models
Adstock Analysis
• Ability to explore product interactions to
understand and uncover halo and
cannibalization effects across your product
portfolio
Halo / Cannibalization Analysis

43. Technology Capabilities
Econometric response models
• Build and test time series and causal models
Elasticity reports
Response Model Diagnostics
• Objectively quantify the relative responsiveness
of each driver of sales
• Decompose sales into its various components.
Diminishing returns
• Capture changes in marginal ROI as spending
Elasticity Reports
levels increase through diminishing returns
curve for each channel
• Determine the threshold point beyond which
Sensitivity Report
marketing expenditures would not yield any
additional benefits
Diminishing Returns

44. Technology Capabilities
What‐if analysis & scenario planning
• Ability to simulate expenditures over different Report Dashboard
media and analyze the impact on
products/brands/channels/geo’s Simulate/Forecast
• Compare competing spending plans to
understand the differences in sales
Marketing mix optimization
Decomposition Reports
• Optimal media expense allocation for selected p
roduct, channel & geography combination over a
defined period of time.
• Define different sets of business constraints to Compare scenarios
explore the impact on the optimal solution
Marketing Mix Analytics Optimization
“Leave less up to chance and make data
driven, evidence-based decisions”

45. Case Study: Large Insurance Company
Business Issue Solution Benefits
Quantify effectiveness of Marketing mix analytics Even though they are
all marketing mix elements allows them to share consistently outspent by
• Direct-response assumptions about their competitors, became
• TV marketing analysis across more competitive by
• Direct marketing all types of marketing determining which media
• Web marketing and channels worked the
• Retail channel Data is integrated from best across products and
communications multiple sources and regions.
analyzed to ensure
accurate short-term and
long-term forecasts across
marketing and operations
“The technology help us develop a “strategic” tool that enables
us to lower risk in decision-making as we integrate all
marketing disciplines with an eye toward better forecasting,
budgeting, and collaboration.”
Director of Strategy

46. Module 4: An Evolutionary Process - Moving
Toward Analytically Driven Marketing
3.1 Introduction
3.2 Marketing optimization
3.3 The art and science of the marketing mix
3.4 Real-world, success case studies
3.5 Questions