Transcript dmx group - Center

DMX GROUP
Business Intelligence
A Solution by DMX Group and Microsoft
November 9, 2005
Jaime Charaf
Vice President, Operations for Europe, Africa and the Middle East
([email protected])
Myrna Sabbagh
Manager – Projects, DMX Group EMEA
([email protected])
Rawad Yazigi
Senior System Engineer , DMX Group EMEA
([email protected])
DMX GROUP
Today’s Agenda
• Who is DMX Group?
• Why is a data strategy necessary?
• What is Business Intelligence? - DMX Group perspective
– Data Mining deep-dive
• The most powerful tools combined with Excellence in
Consultancy ( The DMX Group Mechanics )
• DMX Group Solution - Specific BI and data mining modules
• Questions
DMX GROUP
DMX Group Background
Started as Venture Capital-funded
company in March 2000.
•
Headquartered in Bellevue, Washington
•
$45 million in funding – Mayfield, Mohr
Davidow, American Express, Deutsche
Bank
•
Grew to over 120 employees
•
35 patents in technology and processes
•
Both technology and services
DMX GROUP
DMX Group Background
•
Beirut, Lebanon Office – May 15, 2005
– Fransabank Implementation Started
– Significant expansion in Lebanon
– Staff
•
Amman, Jordan office
– Opened November 2003
– Staff and offices
– Regional product support center
– Fully scaled data center
DMX GROUP
Example Customers (1 of 2)
Telecommunications
Financial Services/Publications
Manufacturers (especially Auto)
DMX GROUP
Example Customers (2 of 2)
Retailers
Technology
Media & Portals
DMX GROUP
DMX Group Principals
• Dr. Usama Fayyad – World-renowned authority in data mining, one
of founders of field, editor-in-Chief of technical journal, academic and
business leader, authored 2 books and over 150 technical articles. NASA,
Microsoft, and technology companies leadership roles
• Mr. Bassel Ojjeh – Expert in data warehousing, BI, and data
operations, built some of largest DW in the world at Microsoft and DMX
Group, built and shipped major products at Microsoft and Fox Software over
20 year career in DB, BI and IT.
• Mr. Jaime Charaf – 15 years experience as a large systems
integrator with EDS. 5 plus years experience building IT outsourcing
organizations. Expert in advanced engineering CAD/CAM and data
management systems with General Motors at the General Motors Tech
Center and at the Milford Proving Grounds.
DMX GROUP
DMX Group & Microsoft Solution
• Internationally and locally based
– .Net web services expertise
• Advanced and Advancing BI capabilities at little extra software costs
– IT World’s leading company, dedicated to advancing BI tools
• Industries most efficient use of hardware
• Scales to largest BI projects in the world ( terabytes )
• New generation data mining algorithms integrated into DMX Group
Solutions
• Close partnership over the past 16 years
DMX GROUP
DMX Group Mission
• Make enterprise data a working asset across the Institution:
– Data strategy for the business
– Implementation of BI and data mining capabilities
– Enable companies to truly access data on an enterprise level
– Illuminate business issues around data
• Look for the possibilities
• Expose data it to business users
• Train people and change processes
– Integrate with existing operational systems
DMX GROUP
The myths…
• Companies have built up large impressive data
warehouses
• True data mining is pervasive across verticals
• Large companies know how to do it
• Off-the-shelf tools effectively discover valuable
information at an enterprise-wide level
DMX GROUP
The truths…
• Data is not organized or integrated, most data mining efforts
end up not benefiting from existing data infra-structure
• Firms care a lot about data
• Firms are very concerned with understanding customer
behavior
• An extremely small number of firms are successfully mining
data
• Tailored BI and advanced mining solutions are always
optimal
DMX GROUP
Progress
• Who is DMX Group?
• Why is a data strategy necessary?
• What is Business Intelligence? - DMX Group perspective
– Data Mining deep-dive
• The most powerful tools combined with Excellence in
Consultancy ( The DMX Group Mechanics )
• DMX Group Solution - Specific BI and data mining modules
• Questions
DMX GROUP
Business data strategy
• How can your data influence your revenues?
• How do you optimize operations based on data?
• How do you increase customer retention based on
data?
• How do you utilize enterprise data assets to spot
new opportunities:
– Cross-sell to existing customers
– Grow new markets
– Avoid problems such as fraud, abuse, churn, etc
DMX GROUP
Why care about data strategy?
Data is the primary enabler of fact-based decisions in
all departments:
– Marketing
• Identify long-term targets
• Determine optimal customer mix
• Attract, retain and maintain profitable relationships
• Target intelligent offers to specific customers
– Product Management
• Support product design, pricing, packaging, and relationships
• Assist in future product and offer development and testing
DMX GROUP
DMX Group Strategy
Strategies, Solutions, and
Technologies That Bring the Power of
Business Intelligence to the Business
Decision Maker.
 PhDs,
 Statisticians
Business Users
Marketers
DMX GROUP
Progress
• Who is DMX Group?
• Why is a data strategy necessary?
• What is Business Intelligence? - DMX Group perspective
– Data Mining deep-dive
• The most powerful tools combined with Excellence in
Consultancy ( The DMX Group Mechanics )
• DMX Group Solution - Specific BI and data mining modules
• Questions
DMX GROUP
What is Data Warehousing?
• Much confusion exists about
the following terms
– Data Warehousing
– Data Model
– Business Intelligence
– Data Mining
– Artificial Intelligence
– Executive Information Systems
DMX GROUP
Data Warehouse - Summary
Billing
Customer
Segments
SMS
Prepaid Behavior
Revenue Analysis
Profitability
Roaming
Mediation
CDRs
Business Intelligence
Data
Warehouse
Targeted
Recommendations
Customer
Care
KYC
DMX GROUP
Data Warehouse / Business Intelligence
Business
Applications
Common
Data
Billing
- Incentive Compensation
- Operations Reporting
- Credit Risk
- Product Pricing
- CRM
Raw Data
Staging
Data Staging
ODS
Cleansed Data
Data
Mart
ODS
Conformed Data
Customer Care
- Incentive Compensation
- Operational Reporting
- Credit Risk
- Product Pricing
- CRM
OLAP
Cube
ODS
Data
Mart
Data
Mart
Dimensioned
Data
Normalized Data
Cross-sell / Up-sell
User Access
Tools
Reporting
Marketing Campaign Analysis
Fraud Analysis (Due Diligence)
OLAP
Cube
ODS
Customer Life Time Value
Conformed Data
Operations Data Store
Business Operations
Enterprise Data Warehousing
Decision
Support
Churn Prediction
OLAP
Cube
Roaming
- Incentive Compensation
- Operational Reporting
- Credit Risk
- Product Pricing
- CRM
Data Delivery
Advanced
Mining
Modules
Customer Segmentation
Human Resources
- Incentive Compensation
- Operational Reporting
- Credit Risk
- Product Pricing
- CRM
Data
Warehouse
Business Decision Support
Analysis
Operations
DMX GROUP
The Data Model –An Example
FB_CardBalance_Fact
PK
PK
CardOwnerID
FB_CardBranch_Dim
PK
PK MerchantGroupID
PK BankID
MerchantID
MerchantCode
OutletCode
MerchantName
FK3 MasterCardBankID
FK2 IssuedBranchID
FK4 MerchantGroupID
MerchantSignningDate
MerchantCancellationDate
FK1 ActionTypeID
ActionDate
eCommerceBusinessType
GeoID
BankCode
BankName
CardStatementID
FK3,FK4 CardOwnerID
FK3
HolderNumber
StatementDate
FK1
CurrencyID
StatementAmount
FB_Bank_Dim
FB_Merchant_Fact
FB_CardTransaction_Fact
PK
TransactionID
DayDate
FK5 TransactionTypeID
FK2 CardNumber
FK4 CurrencyID
TransactionAmount
FK3 MerchantCode
FK3 OutletCode
FK1 BatchID
FK6 MerchantCategoryCode
CardID
FK6 CardOwnerID
CardNumber
HolderNumber
BINNumber
ProductCode
SerialNumber
SupplementaryCode
FK5 ProductID
BeneficiaryName
FK1 CardTypeID
OpenDate
FK4 CardStatusID
IsCancelled
ExpiryDate
ActionTypeID
ActionDate
CancellationDate?
FK2 BranchID
FeeCode
IsPrimaryAccount
IsCredit
IsLocal
FB_CardStatement_Dim
BranchCode
BranchName
GeoID
FB_MerchantGroup_Dim
FB_Card_Fact
PK
OwnerNumber
FirstName
LastName
MiddleName
FK5 BankID
PK BranchID
PK
CardBalanceID
FK3 CardOwnerID
HolderNumber
AccountNumber
CreditCardLimit
ActionTypeID
ActionDate
FB_CardOwner_Dim
FB_CardCurrency_Dim
PK CurrencyID
CardCancellationID
FK1 CardNumber
FK2 CancellationReasonID
CancellationDate?
FB_CardType_Dim
ProductID
ProductCode
ProductDescription
BINNumber
FK1 ProductProgramTypeID
ICANumber
ClassCode
CardCurrency
BatchID
BF_CardTransactionType_Dim
PK
TransactionTypeCode
TransactionTypeDescription
TransactionID
FK6 CardNumber
DayDate
FK2 CurrencyID
FK3 TermType
FK4 ProcessorName
TransactionAmount
FK5 Authorizer
PrimaryTransactionCode
FK1 DeviceID
TransactionStatus
TerminalID
AuthorizationID
SecondaryTransactioncode
AvailaleBalance
BookBalance
TraceNumber
FB_InterrestPaymentStatus_Dim
PK InterrestPaymentStatusID
FB_Loan_Fact
PK
ID
FB_ActionType_Dim
PK ActionTypeID
ActionType
FB_LoanDivider_Dim
PK DividerID
DividerCode
DividerDescription
FB_User_Dim
FK1
PK UserID
UserName
FB_LoanType_Dim
PK
LoanTypeID
LoanTypeCode
LoanTypeDescription
FK1 LoanCategoryID
FK2
FK6
FK3
FK4
FB_LoanCategory_Dim
PK LoanCategoryID
LoanCategoryCode
LoanCategoryDescription
FB_LoanStatus_Dim
PK LoanStatusID
FK5
LoanID
AccountNumber
LoanSerial
LoanDate
LoanEffectiveDate
LastBillMaturityDate
GrossAmount
NetAmount
DividerID
InterrestAmount
InterrestPercent
StampAmount
CommissionAmount
PaymentCount
UserID
ActionTypeID
ActionDate
LoanStatusID
FirstPaymentDueDate
CreditAccountNumber
GLAccountNumber
LoanTypeID
GuarantorFirstName
GuarantorSecondName
GuarantorThirdName
LoanSubTypeID
LifeInsuranceAmount
PaymentCount1
LoanStatusCode
LoanStatusDescription
FB_BillStatus_Dim
FB_LoanBill_Fact
FB_User_Dim
UserName
PK
PK BillStatusID
ID
BillID
LoanID
BillSerial
MaturityDate
BillNumber
BillAmount
BillInterrestAmount
LoanBalance
OrderOf
RealBankInterrest
FK2 BillStatusID
PaymentDate
FK3 UserID
FK10 ActionTypeID
ActionDate
FK4 CollectionTypeID
FK5 CollectingBranchID
FK11 BatchID
SendingDate
IsReceived
ReceivingDate
FK6 TransactionID
FK7 PaymentBranchID
FK8 PhysicalBranchID
IsBillInTransit
InsuranceAmountPerBill
FK9 InterrestPaymentStatusID
BaloonBill
LastPaymentDate
InterrestPaymentDate
EPHNetAmount
PK LoanSubTypeID
LoanSubTypeCode
LoanSubTypeDescription
InterrestPaymentStatusCode
InterrestPaymentStatusDescription
BillStatusCode
BillStatusDescription
FK1
FB_BillDetail_Fact
PK
FB_BillCollectionType_Dim
PK CollectionTypeID
CollectionTypeCode
CollectionTypeDescription
FB_Branch_Dim
DetailID
BillSignerID
FK1 BillID
SignerSerial
SignerFirstName
SignerLastName
SignerFatherName
SignerAddress1
SignerAddress2
SignerAddress3
SignerPhonNumber
SignerFaxNumber
PK BranchID
BranchCode
BranchName
GeoID
FB_Transaction_Fact
PK TransactionID
FB_ActionType_Dim
PK ActionTypeID
ActionType
FB_LoanSubType_Dim
FB_ProductProgramType_Dim
PK ProductProgramTypeID
ProductProgramTypeCode
ProductProgramTypeDescription
FB_SwitchCardTransaction_Fact
PK TransactionTypeID
PK UserID
CancellationReasonCode
CancellationReasonDescription
PK CardTypeID
PK
BatchReference
BatchDate
FK1 BatchTypeID
BinType
IsManual
IsAbroad
MerchantDiscountAmount
BankShareAmount
BF_CancellationReason_Dim
PK CancellationReasonID
CardTypeCode
CardTypeDescription
FB_Batch_Fact
PK
Retail
FB_CardCancellation_Fact
PK
FB_Product_Dim
BF_BatchType_Dim
BatchTypeCode
BatchTypeDescription
CardStatusCode
CardStatusDescription
CurrencyCode
CurrencyName
MerchantGroupCode
MerchantGroupDescription
PK BatchTypeID
FB_CardStatus_Dim
PK CardStatusID
BranchID
TransactionFlag
AccountID
TransactionDate
SerialNumber
TransactionLine
TransactionTypeID
UserID
ChequeNumber
ValueDate
TransactionStatusID
CancellationFlag
TransactionDescription
TransactionAmount
AccountingSignID
CurrencyID
FB_SwitchCardLocalBin_Dim
PK BINID
BINNumber
FB_SwitchCardDevice_Dim
PK DeviceID
DeviceIDNumber
DeviceName
DMX GROUP
What is Business Intelligence?
• Gartner, coined the term “business intelligence”
during the 1990s.
• BI is the transformation of raw data companies
collect from their various operations into usable
information.
• BI software comprises specialized computer
systems that allow an enterprise to easily
aggregate, manipulate, and display data as
actionable information
DMX GROUP
What is Data Mining?
Finding interesting structure in data
• Structure: refers to statistical patterns, predictive models,
correlations or hidden relationships
• Interesting: is what is important to accomplishing the
Enterprise’s goal – The Data Strategy!!
• Examples of data mining tasks
– Predictive Modeling (classification, regression)
– Segmentation (Data Clustering )
– Affinity (Summarization)
• This is relationships between fields, associations, and visualization
DMX GROUP
Data Mining and Databases
Many interesting analysis queries are difficult to state
precisely
• Examples:
– which records represent fraudulent transactions?
– which households are likely to prefer one service or
provider over another?
– Who’s are the best credit risks in my customer DB?
• Yet database may contain the needed information
– Good or bad customers, profitability
– Did or did not respond to mailed survey...
DMX GROUP
What is Data Mining?
•
Specific database queries
•
“Drill-through” reports
•
OLAP
None of these tell
•
Cubes
•
Custom Reports
•
Export to Excel
you anything beyond
the answer to a wellformulated question
•
Lists of Users
•
Tracking Segments of Users
•
Data Templates
NOT DATA MINING
DMX GROUP
Data Mining Tasks
• Descriptive Analysis: to provide a concise and
succinct summarization of a collection of data
and distinguishes it from others.
• Association: to discover relationships or
correlation among items in transactions.
• Prediction: to predict the value of a variable
(the class) based on a set of training data based
on values of measured variables.
DMX GROUP
Data Mining Tasks
• Segmentation (Clustering): To identify clusters
of data objects that are similar to one another.
• Time series analysis: To analyze large set of
time series data to find certain regularities and
interesting characteristics, including search for
similar sequences or subsequences and mining
sequential patterns, periodicities, trends and
deviations.
DMX GROUP
The Predictive Modeling Process
• Define the Problem
•
Process the Data
–
–
–
–
•
Collect the Data
Clean the Data
Encode the Data
Transform the Data
Run and Evaluate Experiment s
– Try different learning algorithms
– Try different models
– Try different data processing
• Select Final Model
• Test Final Model
• Apply the Model
DMX GROUP
Classification
• To analyze a set of training data whose class label is known
and to construct a model for each class based on the
features in the data. Then to classify a given input into these
categories (classes).
X2
Class1
Class 2
X1
DMX GROUP
Segmentation & Clustering
•
•
To identify clusters embedded in the data, where a cluster is a collection of data
objects that are similar to one another.
Unlike classification, segmentation doesn’t associate categories to inputs. Instead,
the division into groups is based solely on the geometrical structure of the input
data.
Segment 3
X2
Segment 1
Segment 2
X1
DMX GROUP
Association
• To discover relationships or correlation among a set
of data.
• It’s a predictive modeling task whose goal is to
reveal combinations of items or events that often
occur together.
• Once identified these associations can be used to
improve decision-making in a wide variety of
applications.
DMX GROUP
Progress
• Who is DMX Group?
• Why is a data strategy necessary?
• What is Business Intelligence? - DMX Group perspective
– Data Mining deep-dive
• The most powerful tools combined with Excellence in
Consultancy ( The DMX Group Mechanics )
• DMX Group Solution - Specific BI and data mining modules
• Questions
DMX GROUP
Powerful Tools
•
Database queries - SQL
•
“Drill-Down” reports
•
Data Base / OLAP
– SQL Server 2005
•
Cubes
•
Export to Excel * (Clear example)
•
Lists of Users (Black Lists)
•
Tracking Segments of Users
DMX GROUP
BI Solutions do not come in boxes
BI Tools used to build your roof are not BI
DMX GROUP
Implementation of BI – Service Model
•
In-depth interviewing of Department Heads and End Users
– Define the specific reporting capabilities needed
– Allows Tailored Solution – Size as needed
•
Locating data required by specific reports – The hunt
•
Top down analysis of needed data
– Granularity is not beneficial if not necessary
– From “your questions” to the data warehouse not vice versa
– Avoid the “Data Tomb”
•
Cleaning data
– Data that has never seen the light of day is always interesting!
– Erroneous data extract reports are imperative
•
E/T/L
•
Custom reports
•
Custom data mining algorithms
DMX GROUP
A BI Implementation
User Interface
Two
Days
Portals &
Reports
One
Month
Two
Months
? Months
Data Integration
Data Discovery and
Data Transformation
DMX GROUP
A Brief History of BI - Yesterday
In the beginning there was Artificial
Intelligence ( Data Mining ) and
all tools were built by data miners
Scientists built and exclusively used
the first BI/AI applications
Think about this…the same scientist that
wrote 10,000 lines of AI code was creating
HTML and basic queries in order to get
this information to end Users
Data mining methods have their origins in a variety of fields: Statistics, Databases,
Pattern Recognition, AI, Visualization, High-Performance Computing, and
Information Retrieval. Successful deployment of these technologies to e-business
enterprise data requires: data warehouse construction, mechanisms to efficiently
update the warehouse, integration of data mining technologies, and delivery of
results in a form consumable by business end-users.
– Dr. Usama Fayyad, April 2000
DMX GROUP
A Brief History of BI - Today
• Each year BI tools grow more powerful
• Today we are rolling out one of the most powerful BI tools
yet created
• Today’s successful data warehousing implementations
MUST combine
– The most powerful BI and Data Mining tools
– The most experienced implementation methodologies
– And yet must reduce costs to BI customers
DMX GROUP
A Brief History of BI – Tomorrow
• DMX Group’s R&D organizations are studying how to get
out of the UI, E/T/L, and data base building business – SQL
Server 2005 helps us…
• Mass produced tools significantly lower BI costs
• We are excited about Microsoft’s Investment in BI
• This allows DMX Group to focus on
– Refined Implementation Methodology
• Tailoring to meet each customer’s specific needs
– Patented Data Mining (AI) Algorithms
– More Exact Behavioral Prediction and Segmentation models
– Industry/Environmental Specific Data Models
DMX GROUP
Interesting…
•
•
•
•
•
•
•
Abraham Lincoln was elected to Congress in 1846. John F. Kennedy was elected to
Congress in 1946.
Abraham Lincoln was elected President in 1860. John F. Kennedy was elected
President in 1960.
Lincoln's secretary was named Kennedy. Kennedy's Secretary was named Lincoln.
Andrew Johnson, who succeeded Lincoln, was born in 1808. Lyndon Johnson, who
succeeded Kennedy, was born in 1908.
John Wilkes Booth, who assassinated Lincoln, was born in 1839. Lee Harvey
Oswald, who assassinated Kennedy, was born in 1939.
Lincoln was shot at the theater named "Ford.“ Kennedy was shot in a car called
"Lincoln" made by "Ford."
Lincoln was shot in a theater and the assassin ran to a warehouse. Kennedy was
shot from a warehouse and the assassin ran to a theater.
•
•
•
•
Both Presidents were shot in the head.
Both assassins were known by their three names.
Both names are composed of fifteen letters.
Booth and Oswald were assassinated before their trials.
•
A week before Lincoln was shot, he was in Monroe, Maryland. A week before
Kennedy was shot, he was with Marilyn Monroe.
DMX GROUP
Progress
• Who is DMX Group?
• Data Strategy – A critical success factor
• Evolution of data management
• What is Business Intelligence? - DMX Group perspective
– What data mining is… What data mining is NOT
• BTW: Why am I talking about services more than products?
• DMX Group Solution - Specific BI and data mining modules
• Questions
DMX GROUP
How it Works
ATM
Data
Customer
Segments
Trans actions
Product
Data
Warehouse
Analytics
Internet Banking
SMS Banking
Credit Cards
Loans
Targeted
Recommendations
Customer
Operational CRM
DMX GROUP
Advanced Mining Modules Available
 Advanced Mining module
available on top of Business
Intelligence system
•Customer Segmentation
•Cross-sell / Up-sell
•Churn Prediction
•Marketing Campaign Analysis
•Fraud Analysis (Due Diligence)
•Customer Life Time Value
DMX GROUP
BI and Advanced Modeling
Modules
Customer Loyalty Modeling and Prediction
DMX GROUP
2
Sample
Database
Client Analysis
3
Build
Churn
Model
4
Score
Database
6
High Risk
Med Risk
Low Risk
5
High Val
Med Val
Low Val
Value
1
Customer
Interaction
Base
Assign
Customer
Value
6
Customer/Account Data
Risk
High Val High Val
High Val
High Risk Med Risk Low Risk
Med Val Med Val
Med Val
High Risk Med Risk Low Risk
Low Val
Low Val
Low Val
High Risk Med Risk Low Risk
DMX GROUP
LTV and Its Application
• A customer’s life-time value (LTV) is the net
value that a customer brings in to a business by
the end of their service. I.e. their profit
contribution.
• LTV modeling allows for decisions to be made
about individual customers that optimize returnon-investment (ROI). Examples:
– Aggressive retention programs, such as account
upgrade and Card renewal for high LTV.
– Differentiated customer care treatment for
reactivations/loyalty by customers with low LTV
DMX GROUP
Cost Rules Applied…
Cost Rules are introduced to define value
For Example:
– Deposit Value
– Product mix
– Average. daily balance
– Monthly service fees
– Technical operations/Support costs
– Branch/teller usage
– Late payment/Overdraft history
– Interest rate
– Contract term
– Credit Score
– Employment history/Income
DMX GROUP
Sample Analytics
Region 1
Region 2
Region 3
Region 4
Region 5
Region 6
DMX GROUP
Map Segments to Actions
High
Let them
go
Cost Reducing
Programs
Churn
Probability
Save Program
NSF, high-costs
Change
Bad Migration
Behavior
Cautiously
Defend
Account
Upsell
Feature Add
Grow
Margin
Feature Use
Aggressively
Defend
Card
Renewal
Elite Program
Nurture /
Maintain
Loyalty Programs
Low
Negative
Low
Forecasted
LTV
High
DMX GROUP
Sample Analytics
Segment 1
Segment 2
Segment 3
Segment 4
Segment 5
DMX GROUP
BI and Advanced Modeling
Modules
Customer Segmentation
DMX GROUP
Customer Segmentation
Customers
Are Segmented
Data Mining Engine
(Clustering)
DMX GROUP
The Challenges
• Marketers can’t manage online customer
segments in the language of their own business
• With current solutions, marketers are forced to
think in terms of DB Queries and complex data
operations
• Out of the box solutions are not flexible enough
to meet a variety of customers
• Marketing doesn’t have time to sift through the
raw data and interpret complex customer
interaction data
DMX GROUP
The Answer - Customer Segment Manager
Customer Segment Manager empowers
Marketers to…
• Easily segment their customers in the
language of their business
• Manage customer segments through
targeted marketing campaigns
• Track most valuable customers over time
DMX GROUP
Key Benefits
• Boosts revenues
– Market to precisely segmented groups
– Reach customers with campaigns tailored to their
interests
– Develop customers from low-value to high-value groups

Reduces marketing costs
 Rifle vs. shotgun approach eliminates wasteful marketing
 Automated customer intelligence gathering

Improves customer loyalty, retention, & satisfaction
 Targeted, personalized offers to customers
DMX GROUP
Sample Segment
• Imagine the following Customer Segment
– Employed long term
– Consistent monthly deposits
– Age: 25 – 55
– No past delinquent payment issues
– No outstanding mortgage loan
– Similar Spouse Situation
DMX GROUP
Resulting Marketing Campaign
• Marketing Campaign
– Mortgage Loan Offer
 Increased Customer Profitability
 Increased Customer Satisfaction
DMX GROUP
Segmentation Examples
Wealth Market
1
Wealth Market
Upscale Retired
2
Affluent Retired
3
Comfortably Retired
Upper Affluent
4
High Asset Pre-Retired Investors
5
High Asset Suburban Boomers
6
High Asset Exurban Boomers
7
Elite Pre-Retired Spenders
8
Metro Elite Boomer
9
Exurban Elite Boomers
10 Young Savvy Elites
Lower Affluent
11 High Asset Affluent Climbers
12 Established Empty Nesters
13 Metro Achievers
14 Greenbelt Achievers
15 Affluent Beginnings
16 Affluent Renters
Mass Market
17 High Asset Mass Market Savers
18 Pre-Retired Metro America
19 Urban Boomer Builders
20 Sunbelt Traditionals
21 Country Builders
22 Up & Coming Young Climbers
23 Urban Mass Market Owners
24 Rural Mass Market Owners
25 Urban Boomer Spenders
26 Aspiring Young Spenders
27 Midscale Metro Renters
28 Midscale Rural Renters
29 Striving Young Metros
30 Lower-Middle Metro Owners
31 Lower-Middle Exurban Owners
32 Lower-Middle Town Blues
33 Young Urban Renters
34 Lower Boomer Renters
Midscale Retired
35 Suburban Senior Owners
36 Conservative Retireds
Lower Market
37 Lower Market Owners
38 Metro Downscale
39 Rural Downscale
40 Inner City Strugglers
Downscale Retired
41 Downscale Sunbelt Security
42 Downscale Struggling Seniors
DMX GROUP
Developing Valuable Customers
Buy
Occasionally
High
Value
Customer
Bought
Recently
Use targeted offers
to move customers…
Bought
Three
Months
Ago
Buy
Frequently
Low
Value
Customer
DMX GROUP
Questions?
emails: [email protected]
[email protected]
[email protected]