2. • Noah Powers
– Principal Solutions Architect, Customer Intelligence, SAS
• Patty Hager
– Analytics Manager, Content/Communication/Entertainment, SAS
• Suneel Grover
– Solutions Architect, Integrated Marketing Analytics & Visualization, SAS
– Adjunct Professor, Business Analytics & Data Visualization,
New York University (NYU)
3. Client Case Study: Staples
http://www.youtube.com/watch?v=84wXOr9ddVI&feature=plcp
5. Orchestration & Interaction
Marketing
Decisions
Multi-Channel Campaign Management
Real-Time Decisions
Marketing Optimization
Case Studies
Information Management & Analytics
ERP CRM EDW Online Social Other
Data Sources
6. Orchestration & Interaction
“Interacting with your customers in an appropriate
manner drives retention, migration, loyalty and growth.
Being able to deliver an appropriate offer – at the right
time, via the right channel and with the right collateral –
makes all the difference in satisfying your customer.
7. Precision Marketing
“The Know ME or NO ME savvy consumer votes
with their dollars and defect from brands that are
not relevant.”
- Lee Gallagher
“Precision marketing is about using data to drive
customer insights so that you send the right message
to the right person at the right time in the right
channel.”
- Sandra Zoratti
8. The Street & Precision Marketing
http://www.thestreet.com/video/index.html?bcpid=62
7941001001&bckey=AQ~~,AAAAAEBQhPI~,35stD8-
Ka9Fwet1OxGtEM5iOnD2FtfSl&bctid=1721392328001
9. Multi-Channel Campaign Management
“Marketers are aggressively shifting budget to digital media and seeing
interactive as more effective than traditional efforts. They look now to
campaign management applications that enable them to act on and
react to empowered customers rather than just integrate more
channels.”
47. Marketing Optimization
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?
47
48. Marketing Optimization Is An Assignment Problem
Customers &
Prospects
Offers,
Services,
and Pricing
Web Email Mail Mobile Phone Branch ATM Advisor
Channels
Checking Credit Cards Lines Investments
Products
Savings Loans Mortgages Insurance
48
49. 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?
49
50. Optimization Decision Components
Decisions
Which Offer(s) to present to each customer and channel
Objective
Maximize: Sales, Profit, Response, etc.
Minimize: Cost, Returns
Constraints
Aggregate business constraints (e.g. Budget)
Customer/HH level constraints
Customer/HH level contact policies
Contact policies
50
51. Examples Of Aggregate Constraints
Budgets
Spend at most $200,000 on Campaign A
Offer counts
Make at least 10,000 offers from Campaign B
Channel capacities
Call center is available for only 4,000 hours during June
ROI
Require an overall ROI of at least 15%
Risk
Average credit score for customers who receive the credit card
offer must be at least 700
51
52. Examples Of Contact Policy Constraints
Max/Min contacts
At most 3 offers per household
At most 1 email per customer per week
At least 1 offer to each high-value customer
Blocking
Call center blocks direct mail for at least 4 weeks
Mortgage campaign blocks credit card campaign for 2 months
General customer-level constraints
Maximum budget of $12 per customer
Maximum call center usage of at most 60 minutes per household
52
53. Optimization Framework Enables Evaluation
Of Trade-offs
What is the impact if I optimize against expected
profits instead of expected revenues?
Could I increase profits if my campaign budget is
increased? How much?
Is my contact policy optimal? Should I be contacting
my customers less frequently?
Is the number of offers required for the campaign
ideal? What would happen if this were changed?
53
53
54. 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
54
55. Illustration of Optimization Benefits Over
Business Rule Based Approaches
Expected Return = Propensity
to respond x Expected Value
Customers Checking Mortgage CC
Checking 1 100 125 90
2 50 70 75
3 60 80 65
Mortgage
4 55 80 75
5 70 60 50
6 75 65 60
Credit Card 7 80 70 75
8 65 60 60
9 90 135 60
55
56. Campaign Prioritization Based Approach
Constraints: Customers Checking Mortgage CC
1 100 125 90
1. Each customer must
2 50 70 75
get an offer from at
most one campaign 3 60 80 65
4 55 80 75
2. Each campaign must
target at most three 5 70 60 50
customers 6 75 65 60
7 80 70 75
8 65 60 60
9 90 135 60
Expected Return = $670
56
57. Customer Rules Based Approach
Constraints: Customers Checking Mortgage CC
1 100 125 90
1. Each customer must
2 50 70 75
get an offer from at
most one campaign 3 60 80 65
4 55 80 75
2. Each campaign must
target at most three 5 70 60 50
customers 6 75 65 60
7 80 70 75
8 65 60 60
9 90 135 60
Expected Return = $705
57
58. Optimization Yields The Best Results
Customers Checking Mortgage CC
Constraints:
1 100 125 90
1. Each customer must
2 50 70 75
get an offer from at
most one campaign 3 60 80 65
4 55 80 75
2. Each campaign must
target at most three 5 70 60 50
customers 6 75 65 60
7 80 70 75
8 65 60 60
9 90 135 60
Expected Return = $775
58
59. Not All Decision Approaches Yield The Same Results
10–100+ %
Optimization
- Solves by taking a
5-10 % holistic approach
- Factors in all constraints
- Determines the best
Customer Rules possible result
- First In, First Out
- Prioritized by
Customer/Campaign
- Fails in the face of
Prioritization constraints
- First In, First Out
- Prioritized by
Campaign
- Does not provide best
possible combination
59
60. Marketing Optimization Process Flow
Campaigns
Offer definitions
Offer costs
Offer/customer eligible
transactions
Customer Data
Model scores Optimized Output
Demographic/behavior O1 O2 O3 O4 O5 .. Oj
al information C1 x
Marketing Optimization Engine C2 x
C3 x
Contact History Define Examine
C4 x
Optimize
Data Optimization Optimization C5 x
Offer/customer contact . x
Scenarios Reports x
Time of contact
x
x
Business Goal x
Profit, Revenue Campaign ID - Customer ID – Offer ID
What-If Analysis – Channel ID - Time
Score/Rank based
Constraints &
Business Rules
Offer & Channel levels
Offer conflict &
sequencing
Contact Policies
Global Opt-outs
Budget
60
61. Case Studies
Client Name Benefits
Commerzbank • 55% increase in profitability of DM program
• Payback in 4 months
Vodafone (Australia) • 3-10x Response Rate increase
• Improve campaign ROI by 4x
• 30% reduction in campaign costs
Scotiabank • 50% Campaign ROI improvement
Major US 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
61
63. Sunday Morning Preview
• Recognize the key elements of the customer
experience and how to manage them.
• Know how to integrate online and offline customer
data for better, faster decision making.
• Understand the critical role of full customer views in
assessing customer value and opportunities.
64. Adaptive Customer Experience
Marketing
Decisions
Customer Experience Analytics
Customer Experience Targeting & Personalization
Social Media Analytics
Case Studies
Information Management & Analytics
ERP CRM EDW Online Social Other
Data Sources
Create dialogue with client to understand:Where they think they are on the maturity graphWhat they think their organization is ready forWhere are the biggest opportunities
More channels Greater need for consistency across channels Even greater need with mergers and acqScale of the problem requires specialized optimization approaches in order to truly optimize
SAS has many customer references for MO whereas Unica has very few (e.g. Discover Financial Services)SAS has a number of examples where we have implemented MO where Unica Campaign is in place (and they even had Unica Optimize licensed.)
