Optimizing Customer Relationship Management:
Adding Optimization to Segmentation and Automation
at MarketSwitch
1
What is Marketing Optimization?
The “Marketer’s Dilemma”
Products
• each with specific
goals and requirements
Prospects
-Opportunity set is huge
-Resources are limited
-How do I satisfy everyone?
Classic Optimization Problem...
Channels
• each with specific
costs and capabilities
2
Optimizing The Demand Chain
Customer & Product
Data Managment &
Analysis
Data
Accumulation
Response Modeling,
Profit Modeling,
Personalization,
Segmentation
Likelihood's
and
Propensities
per Product
MultiDimensional ,
ConstraintBased
Optimization
Optimal
Offer/
Optimal
Channel
Intelligent
Marketing
Resource
Allocation
Multi-Channel
Management &
Delivery
Marketing
Activity
3
The Role Of Optimization
Oracle
Hyperion
Cognos
MicroStrategy
WebTrends
Claritas
E Data
Experian
First Data
Trans Union
Others
SAS
RightPoint
Net Perceptions
Black Pearl
SPSS
Quadstone
Info Advantage
Others
Siebel
E.piphany
DoubleClick
BroadVision
Xchange
Prime Response
Clarify
Others
4
The Real World Problem
Customers
(millions)
Offers
(hundreds)
Constraints...
 Minimum sales
 Maximum cost per customer
 Maximum total budget
?
 Minimum NPV per customer
 Minimum solicited offers
 Maximum solicited offers
 Maximum number of promotions
per customer
“What if” Scenarios...
 Limit budget
Which offers should I send
to which customers?
 Maximum promotions per
customer
 Maximize profit
 Acquire the most customers
5
Offer Optimization
Offers
-Ads~Promotion
-Products
Offer Eligibility Conditions
Optimization
Engine
Offer Economics
-CPM rate (ads)
-Delivery cost
Response & Profit Models
-Propensity Score
-Profitability per prospect
Real Time
Campaign
Deployment
INPUT
Optimization Goal
-max. NPV
-max. click-through/imp.
-min. Budget
Optimization
Schema
Optimization Strategy
Development
(Maximize Business Goals
and simultaneously satisfy
all Constraints)
Application of unique
mathematical
algorithm to generate
optimization schema
Business Constraints
-min. NPV
-max. Budget
-min/max contacts
6
Real Time Offer Optimization
How it Works
Online Promotions
Optimization Workflow
7
Real Time Offer Optimization
Offer~Ad Request
Optimization
Web Sites
Scoring
S
e
r
v
e
r
Optimal Offer Selection
Real Time Adjustment
Activity
Log
Ads or
Offers
8
Real Time Offer Optimization
Case Study
9
Snowball.com Business Model
• Leading network for
Generation-I
– aggregates sites
– brings content to audience
– brings audience to
multiple products and
services
• 25th largest web property
– 14+ million unique monthly
visitors
– registering 20,000 new
members per day
10
Opportunity
• Business Challenge
– Maximize the value of the 20,000 daily network registrants
• businesses can offer special promotions to registrants
• revenue generated by serving offers and generating
conversions
Cool - I’ll
Register
Visitor
Arrives
General Network
Registration Flow
Personalize
Value
Exchange
E-mails &
Offers
11
12
13
14
15
Snowball.com Marketer’s Dilemma
• Most relevant offer
• Achieve revenue and profit goals
• Satisfy our specific business
commitments
– such as number of impressions per offer
and conversions
• Execute in a dynamic environment
Customers
(millions)
Offers
(hundreds)
?
– new offers rolling in and old offers
rolling out
Which offers should be promoted
to which customers?
16
Today’s Process
How is it accomplished today?
– Combinations of business rules
• if question 2 is answered A or C
• and
• question 1 is answered D
• and
• gender is Male
• then show offers X and Z
17
Today’s Limitations
Limitations of this approach
– Significant profit being left on the table
• optimal promotions at any point in time not
being served
– Extremely resource intensive
• team of marketers trying to manage “If~Than”
business rules
– Not flexible/adaptable
• not scalable…as offer pool grows so does the
resource requirements
18
Business Objective
Clear Objective:
– Implement solution that maximizes the
revenue derived from registration promotional
offers
• do it fast
• do it with minimal resources
• do it in a scalable fashion
19
Requirements
• Target limited offers (max 6) from a large
pool
• Manage inventory of multiple offers
• Access entered data and serve offers based on
–
–
–
–
–
User profile
Performance completion models
Inventory levels
Revenue per user
Dictated constraints
20
Typical Constraints
• Constraints applied
–
–
–
–
–
By particular network
By age (i.e. users age 20 or older)
By gender (i.e. females only)
By geographic location (city or state)
Offer can only be selected once per user. If selected, it should not be
served again to particular user
• Data used
–
–
–
–
–
–
–
Birthday
Gender
Zip Code
Network
Historic offers selected
Offer details, profitability and goals
Other...
Offer
Pool
Response
Modeling
Customer
Data
Multi-Dimensional
Optimization
21
Key Functionality
• Marketing inputs
– required conversions
– ability to model
–
–
–
–
–
deal term
included networks
targeted demographics
financials
required click-throughs and impressions
• Calculate impressions to achieve benchmarks
• Ability to update in real time
• Ability to use transactional data sources
22
Expected Benefits
“Third” page real estate put to optimal
revenue generation use
– Snowball presents promotional offers
mathematically proven to maximize revenue
• cross-network registrants get promotion offers that
are relevant to them
– real world business constraints are met
– marketing team can focus on other efforts
• versus “If X and D, then A, B and C”
23
Conclusion
• Constraint-based optimization is required to
maximize a function
– personalization is a tool…optimization puts it to work
to achieve financial goals
– ability to deal with constraints is critical
• Optimization unlocks value from business
agreements
$ $
$
– many promotional partners
$
$
– many sites
– many registrants
$
$ $ $$
24
Outbound Marketing Optimization
Cross Selling Case Study
Cross Selling High Speed Data and
Complementary Telephone Services
Cross Selling credit cards, affiliate
programs, balance transfer programs,
insurance...
25
Impact
pact
• Revenue increases ranging from
8-20%
ImpB2B
Challenges
• Significant reduction
of management
time
in developing offer assignments
• Scalable and adaptable as results develop.
26