Module 2 Orchestration and Interaction Final

Real-Time Analytics &
Attribution

• 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)

Client Case Study: Staples

http://www.youtube.com/watch?v=84wXOr9ddVI&feature=plcp

Module 2
Orchestration &
Interaction

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

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.

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

The Street & Precision Marketing

http://www.thestreet.com/video/index.html?bcpid=62
7941001001&bckey=AQ~~,AAAAAEBQhPI~,35stD8-
Ka9Fwet1OxGtEM5iOnD2FtfSl&bctid=1721392328001

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.”

January 2012 “The Forrester Wave™: Cross-Channel Campaign Management, Q1 2012”
Forrester Wave™: Cross-Channel Campaign Management, Q1 2012

© 2012, Forrester Research, Inc. Reproduction Prohibited

The TV used to be THE tribal camp fire…

11

Copyright © 2011, SAS Institute Inc. All rights reserved.

…but now it’s just another light source.

12


Menu: 1960
TV Channels
Radio Stations
Magazine Titles

13

Media choices used to be limited…

14

…but media today is an exotic buffet

Example Path to Conversion
Many ways to engage along the way!

Create Monitor & Increase
Engage Conversion
Interest Value
Interaction Value

Time

Analyze data to better understand and anticipate customer 15

behavior...and design targeted, relevant interactions.

Interactive Marketing
Maturity Model

Incorporate analytics, integrate online/offline data, multi- Optimized
channel campaigns, use analytics to drive new programs and Interactions
profitable customer relationships

Targeted communications based on behavioral Behavioral
segments and clusters, incorporate other Segmentation
customer profile data
Competitive Advantage

Timing emails, SMS, etc., with an
Event-Triggered “event”, such as a reservation,
ticket purchase or birthday

Personalized
Basic Tailoring content based on preferences and database
Segmentation attributes, incorporating previous email campaign
“responses” (click-throughs)
Batch and
Blast Scheduled weekly communications and offers

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Degree of Engagement

Where In The Marketing Process Are We?

Information Advanced Analytics
Management Business Intelligence

MKT DB

Web Data

Holistic View

Warehouse
Campaign Management
Real-time Decisioning

Responses Tracked
Integrated Workflow

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Campaign Workflow
 Manage tasks and details of marketing campaigns

18


Campaign Design
 Define simple or multi-stage and multi-channel campaigns
 Manage entire campaign process – from start to finish

19


Define Selections
 Define a single or multiple range
combination
 Graphically select or enter values directly
 Update counts instantly
 Manage waterfall selections

20


Reusable Segments
 Define global inclusions and suppressions
 Reuse them across campaigns

21


Integrated Analytics
 Up-to-date propensities based on the most
current data
 Dynamic use of analytic models

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Manage Tests & Audience Splits
 Can be based on counts, percentages and
customer attributes (e.g. channel,
preference, value)
 Statistical cell sizing calculator to
determine minimum cell sizes

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Multi-Channel
 Define multi-channel campaigns
 Link to any fulfilment channel

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Manage Communication Details
 Manage offers and channels
 Fulfilment lists
 Seeds
 Tracking for reporting
25


Manage Communication Details
 Manage offers and channels
 Fulfilment lists
 Seeds
 Tracking for reporting
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Treatments
 Create and manage the universe of treatments
 Assign treatments to a campaign
 Define custom treatment details

27


Schedule
 Schedule campaigns and communications
 Define them as recurring if necessary (e.g. birthdays)

28


Schedule
 Schedule campaigns and communications
 Define them as recurring if necessary (e.g. birthdays)

29


Campaign Workflow
 Manage approvals
 Support different roles on marketing team

30


Let’s Imagine A Business
Use Case….

31


Cross-sell / Upsell Marketing Application

Print Subscriber Visits Explores Media & Explores Subscription
Website & Logs In Marketing Content Bundle Offer

32


Cross-sell / Upsell: Outbound Marketing
Explores Subscription CRM Data
Bundle Offer

On Line Behavior

Enrichment Data

Trigger Outbound Marketing Advanced Analytics &
Campaign (Batch) Scoring

33


#DreamBigger

34


Real-Time Decisioning

http://www.youtube.com/watch?v=N4TiPtP08LE&hd=1&t=5m59s
(Stop Time Stamp: 10:48)
35


Real-Time The ability to present relevant and timely offers based on prior
Offer assignment of scores helps prioritize offers based on
Management customer eligibility.

Real-Time Decision management helps identify interactions to influence or to
Decision which to apply various business rules, such as "if [x], then [y]"
Management rules, for optimizing campaigns.

Real-Time Online Interaction optimization technology is infrastructure that applies
Interaction predictive and statistical techniques to tailor online experiences to
Optimization meet the needs of individual visitors or visitor segments.
36


Real-Time Decision Making Definition

Inbound Communications

Smarter Response
Organization Customer

 Real-time decision making enables the real-time delivery
of decisions and recommendations that optimize every
customer interaction to improve revenue, growth
and retention.
 Analytical insights (predictive and descriptive)
 Business rules
 Contact strategy
37


How Does It Work?

38


Isn’t It Nice When Things Are Easy?

Inbound Real-Time
Campaign Management

Outbound Batch
Campaign
Management

39


Let’s Imagine Another Business
Use Case….

40


Real-Time Marketing Application

Print Subscriber Visits Explores Media & Explores Subscription
Website & Logs In Marketing Content Bundle Offer

41


Real-Time Decisioning: Inbound Marketing
Explores Subscription CRM Data
Bundle Offer

On Line Behavior

Enrichment Data

Trigger Inbound Marketing Trigger Outbound Marketing
Website Personalization Campaign (Batch)
Campaign (Real-Time)

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Real-Time Decision Diagram: Inbound Targeting
Subscriber Logs
In

Subscriber
“Content” Click
Content
Type
Dynamic Analytic
Model & Scoring

Media &
Marketing
Potential Upsell
Offers
Media &
Marketing Other

Print Digital Print + Digital

Offer
Decision Real Time
Decision
RT
Decision

43


#NotEvenSheldonCanDoThis
44



http://youtu.be/ktUWdngfg98?hd=1
45


Marketing Optimization Defined

Marketing Optimization determines the best
offer(s) to deliver to each customer through
the right channel at the right time

46

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

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

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

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

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

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

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

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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

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

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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

Customer Rules Based Approach

Constraints: Customers Checking Mortgage CC
1 100 125 90
2 50 70 75
4 55 80 75
7 80 70 75
8 65 60 60
9 90 135 60

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Optimization Yields The Best Results

Customers Checking Mortgage CC
Constraints:
1 100 125 90
2 50 70 75
4 55 80 75
7 80 70 75
8 65 60 60
9 90 135 60

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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

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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

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


http://youtu.be/wQYyUDpgDaU?hd=1
62

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.

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

Module 2 Orchestration and Interaction Final

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