establishing a data quality foundation for a successful ... a data quality...3 clients 165 clients 1...

Establishing a Data Quality Foundation for a Successful MDM Initiative

Tony Fisher Peter HarveyPresident & CEO President & CEODataFlux Intellidyn

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Smith, BillB. SmithBill Smith

Governing Data for Corporate Success

Product Data

Customer Data

Are you ready for MDM?

Pure and simple: The most Pure and simple: The most critical factor to master critical factor to master data management is data data management is data quality.quality.

-- David LoshinDavid Loshin

MDM Programs . . .

Promote and ensure corporate alignment

Identify data providers and consumers

Encompass People, Policy and Technology

Must be built on a robust data quality platform, finding and fixing at

the source and auditing

Leverage the consistency, standardization and reuse of data assets

Look Familiar?

Stakeholder Perspectives

Business Drivers for MDM

Operational Efficiency

Risk Management

Competitive Advantage

IT Modernization

Data Governance Maturity Model

HIGH

LOW

Risk

LOW

HIGH

Rew

ard

UNDISCIPLINED REACTIVE PROACTIVE GOVERNEDPeople, Policies, Technology Adoption

BPM Integration

CDI

PDM

CRM

ERP

Data Warehouse

SFA

Database Marketing

MDM

Think Locally,Act Locally.

Think Globally,Act Locally.

Think Globally,Act Collectively.

Think Globally, Act Globally.

Copyright © 2007 DataFlux Corporation LLC, Cary, NC, USA. All Rights Reserved.

© 2006 Intellidyn Corporation 2007 Reproduction Prohibited

Data Quality Practices and Challenges

By

Intellidyn Corp.

November 14, 2007

© 2006 Intellidyn Corporation 2007 Reproduction Prohibited12/11/2007 10

Corporate SnapshotIncorporated June 23, 1998, Began Full time operations January 2000“S” Corporation100% Owned by CEO, P. E. HarveyEmployees: 42Profitable since Inception

Revenue Mix:56% Modeled Data10% Analytic Services12% Strategic Consulting22% Prospect Data Warehousing

Sampling of Clients across Banking, Lending, Insurance, Non-Profit, Travel and CollectionsFidelity Investments Ace MortgageMarch of Dimes Countrywide LendingBanco Popular GMAC Mortgage CorpUS Bank Acurian PharmaceuticalNationwide Insurance JP Morgan ChaseAEGON Accredited Home LendersAllstate NovaStar Mortgage Inc.Fireman’s Fund Insurance CannonYour Man Tours (Travel) Viking River CruisesCapital One Tritium Card Services


The “Inside Track” on Intellidyn“Boot Strapped” from:

A blank sheet of paper$40,000 of personal moneyNo external funding to dateProfitable since inception

Information Infrastructure / Analytic capability:Equivalent to: Merkle, Harte Hanks, EpsilonSurpassing: Trans Union, Donnelley, Knowledgebase, Allant, BeNow, Others

Built by Fortune 50 database/analytic employees to be serviced the way they needed to be serviced

Access to everything“Out Thinking” the client“Zero Defect”Migrate internally, when ready


What Sets Intellidyn Apart?

We get the client past their data issues and beyond multi-channel campaigning . . .

We are the strategists, leveraging advanced database marketing techniques with our

marketing experience.

Operating from an award winning technology platform


Since 2001, We Have Scaled Exponentially

From: To:3 clients 165 clients

1 Master File > 5 Master Files

.25 terabyte single master file > 4 TB (ea.) integrated master files

1 terabyte live storage > 30 TB live storage, >75TB Off line

4 models > 300 models

4 processor single thread 20 processor SMP Multi-threading

5 days to load master files < 24 hrs without impacting clients


Personal InfoName : John DoeSpouse : Jane DoeAddress : 555 Main St

Bethpage, NY 11714Tel # : (516) 555-1212Bus Tel # : (631) 555-1212

Purchased Behavior

Purchased $1,000 in electronic last 6months from catalogs, online & retailPurchased $300 in gifts in last 12 monthOpted in and clicked on email offers

Reported DemographicsBirth date : Jan 1, 1945Martial Status : Married# of Children : 2Children Age Range : < 18 YearsGender : Male

Credit InfoHome Value : $ 500,000Home Purchase Date : Sept, 1985Length of Residence : 15 Yr 7 MonthHomeowner Dwelling Size: SingleHousehold Income : $ 250,000Occupation : Senior ManagementCredit Limit : $ 21,000Highest Purchase: $ 10,500 Vehicle : 2000 Ford Mustang, Aug-00

Model Scoring.62 / .12 / .15 / .06

Vertical ListData Warehouse (6)Visions (15)

Lifetime Value $$$ITA & PA Scoring4 / 10 / 15 / 2 / 5 / 9

Subprime

Alt A

Insur.

PRime

OtherBalance

ResponseConversion

BalanceChannel

We deliver each client a complete marketing database of the US consumer base, their customers, contacts, campaign history under a

proprietary set of Business Rules

“True” CRM Database

LifestyleCredit DataCredit Bureaus Property Data

Purchase Behavior

Retail/Catalog Transactions


We integrate national prospecting on an enterprise level, enablingclients to speak in one voice to their customers and prospects

Marketing DatabaseDatabase Integration

Address Correction and Validation

Model Scoring

Suppressions

Prospect Screening

Prospect Allocation: zip

Prospect Response & Privacy Requests

Intelligent Consumer Information• Management • Analysis • Marketing Execution

Integrated:Data MiningTargetingResponse AnalysisStatistical ModelingContact MgmtSegmentation

Demographic

Purchase Behavior

Life Style

Prospects

Prospects

Prospects

National Data Sources

VerticalLists Prospects

Direct Mail

Local Marketing

Telemarketing

External Requests/Rules

ProfitOptimizatio

n

Product Managemen

tCampaign

Management

Recycling


An +80mm mailer alerted us to “Ghosts”A person other than the one on the database

lives at that address

Deed RecordsFound Equifax by KeyZip4

Equifax by KeyLN

No Total Yes Total Grand Total

Found Experian by KeyZip4

Found Experian by KeyLN

No - Last Name in EFAX

Yes - Last name in

EFAX*No - Last

Name in EFAX

Yes - Last name in EFAX8

No last name EXP 70,691 154 70,845 7,372 7,455 14,827 85,672

Yes Last name in EXP* 821 38 859 59 80 139 998

No Total 71,512 192 71,704 7,431 7,535 14,966 86,670No last name

EXP 37,257 60 37,317 1,169 359 1,528 38,845Yes Last name

in EXP* 74,850 139 74,989 791 509 1,300 76,289Yes Total 112,107 199 112,306 1,960 868 2,828 115,134Grand Total 183,619 391 184,010 9,391 8,403 17,794 201,804

Yes address in EXP

No address in EFAX Yes address in EFAX

No address in EXP

January to April 2007 New DEED Records matched to Experian and Equifax

Jan Feb Mar Apr % of Total Type

13,493 13,041 19,044 25,113 39% missing

8,874 7,597 10,634 18,693 35% ghosts


Overall AssessmentInvestigation Approach:

• Quantification biased– The selected counties were not representative of U.S.

• Biggest driver of non-matches is quality of Deed file N&A– High percentage of:

• Missing Names• Incorrect/incomplete addresses

• Next driver is lack of matching logic sophistication– Soundex, Reference files, Normalization, CASS/NCOA, other

• Client was not comparing “Apples-to-Apples”– Matched at the individual level– Should be at the household level


Approach: Replicate what we normally achieve, typically +/- 90% match levelsInvestigation Approach:

• We matched Experian Gold to other Master files:– Credit bureaus (Experian & TU)– Acxiom– US 411 Directory– NOT property, due to quality

• Matched at:1. N&A level,

– Distribution by Dwelling type and Recipient Reliability code2. Non-matches matched to aged credit files3. Individual, Household levels4. Address level


We achieved a near 80 percent match at the individual N&A level (Experian Gold to other master files)

Approximately 75% of Experian Gold records are at the same address on both credit files (Trans Union and Experian) and Acxiom

Additional 2.54% matches applying the Non-matches to historical credit files

Person one on Experian Gold matched as follows

Multi-Family Marginal Multi-Family PO BOX Single Family TOTALMatched to Credit or Acxiom file 9.91% 2.03% 2.36% 61.33% 75.62%

Non Matches 5.62% 1.27% 1.94% 15.55% 24.38%15.53% 3.30% 4.29% 76.88% 100.00%

Multi-Family

Marginal Multi-Family

PO BOX

Single Family TOTAL

ched 3 Month Aged dit 0.53% 0.12% 0.45% 1.43% 2.54%

Matchs 17.17% 3.57% 5.05% 71.67% 97.46% 17.70% 3.69% 5.50% 73.10% 100.00%

Investigation Results:


Match rates exceed 90 percent at the household and address levels, while “Ghosts” become apparent

• At the household level we are at 85 percentVolume Match Rate

CUM. Match Rate

Matched to Current Credit or Acxiom file 63,880,714 75.62% 75.62%Matched to aged Credit File 1,252,208 1.48% 77.10%

HH Matched to Credit File 6,841,996 8.10% 85.20%Non Match 12,498,879 14.80% 14.80%

84,473,797 100.00% 100.00%

• At the household level and address levels we are at 90 percent Volume Match Rate

CUM. Match Rate

Matched to Current Credit or Acxiom file 63,880,714 75.62% 75.62%Matched to aged Credit File 1,252,208 1.48% 77.10%

HH Matched to Credit File 6,841,996 8.10% 85.20%Address match to Credit Files 4,252,181 5.03% 90.24%

Non Match 8,246,698 9.76% 9.76%84,473,797 100.00% 185.20%



Since 2003, to address this “Ghost” issue Experian created a Recipient Reliability Code (RRC) process

• We deploy RRC 1 & 2 in the prospect selection process• It attempts to solve for the mobility of a person across addresses• It’s a combination of:

• Address Quality• Mobility Score (Predictive Model of likely to move)• Phone confidence score (Connectivity for members at that unit)

RRC Code RRC

Description Recipient Contact Points

Available Components

1 Very High (default for mailing)

Postal/Phone Name/address/phone where available

2 High (default for mailing)

Postal/Phone Name/address/phone where available

3 Moderate Postal/Phone Name/address/phone where available

4 Low Postal/Phone Name/address/phone where available

5 Telemarketing Phone only Name/phone connectivity (No address)

6 End-dated/Address Only Postal only Resident or Occupant mailing (No name)

The top 2 tiers of the RRC model represent over 90% of the US Living units



We continue to assess the impact of limiting RRC to level 1 for younger individuals and possibly 2 for ages 35 - 64

General Mobility of Hshlder by Tenure by Age Range

23%

15%

8%4% 2%

25%21%

11%

52%

35%

0%

10%

20%

30%

40%

50%

60%

15-24 25-34 35-44 45-64 65+

Source: US Census, CPS Mobility Series Table 17 -- March 2004

Owner Occupied (OO) Renter Occupied (RO)


When we match the remaining “Ghosts” to the daily 411 file we located all but 2.43 percent

Sum of N EG_DWELLCREDIT_PRESENCE AX_PRESENCE Match411 Marginal multi Multi-Family PO BOX Single Family Grand TotalIndividual Individual (blank) 1.06% 5.50% 1.04% 40.45% 48.05%

(blank) (blank) 0.32% 1.70% 0.47% 6.62% 9.11%Historic (Individual) (blank) (blank) 0.07% 0.31% 0.26% 0.84% 1.48%Household (blank) (blank) 0.32% 1.55% 0.42% 5.81% 8.10%Address + Apt (blank) (blank) 0.72% 2.53% 0.65% 5.78% 9.68%Address Only (blank) (blank) 0.07% 1.10% 0.00% 0.06% 1.24%(blank) Individual (blank) 0.65% 2.71% 0.85% 14.26% 18.47%

(blank) Individual 0.01% 0.02% 0.00% 0.55% 0.59%Household 0.01% 0.01% 0.00% 0.51% 0.53%Address 0.02% 0.03% 0.00% 0.29% 0.34%(blank) 0.04% 0.07% 0.61% 1.71% 2.43%

Grand Total 3.30% 15.53% 4.29% 76.88% 100.00%

Approximately 10 percent are moving, which are found during NCOA processing


Why is this such an imperative?

Campaign Performance drives ROI

Model Performance drives Campaign Performance

Match Rates drive Model Accuracy

Data Quality drive match rates


We are all in a unique position:

Unit costs decliningdramatically • Processing• Storage• Data

Capability increases Exponentially

• Processing speed• On-line Storage • Data:

– Volumes– Determinicity– Rates of flow– Scope

Our only limitation is talent !


So, our job is to make “Paradigms out of the Fundamentals”

Data

Daily Credit 411 Directory• Daily • Cell #’sClick Behavior Enterprise:• Touch Points

Modeling

Media PhysicsDynamic:• Variables• Scoring

Personalization

Data/Event Driven: • Copy • Creative • Package • Channel

StrategicPlanning

Integrating:• Property• Credit• Transaction• Media Spend • Market ShareOVER TIME

Performance Analytics

Weekly Across ChannelsROI-Based Over time

Database

TB Level, Refreshed • In Real time • Historical • Behavioral


The Database Marketing Paradigm?

Who ? Models tell us this(to Target)

Which? We’re getting better at this(Channel)

When? Via “Trigger” Events(to Solicit)

What? Experiences drive Transactions

The Paradigm lies in transforming “Consumer Experiences”into predictive scores in real time


The Database Marketing Paradigm

To Predict Transactions(as the dependent variables)

ResponseConversionSatisfactionRetentionRenewalCross sell / Up-sell

The Consumer’s Experience(as the independent variables)

To Marketing Stimulus:• Direct Marketing• Interactive• Broad Market Media

+Their Interactions:• VRU Response• Customer Service• Wireless Inquiries• Web site visits

• Frequencies• Pages• Searches

• Inbound Call Centers


We’ll model on Response to all types of Stimulus and Visits OVER TIME The independent variables

Stimulus:• Direct Marketing

• Telemarketing• Direct mail • Fax

• Interactive• E-mail• Banners & Pop-ups• Keywords• Blogs• Search Engines

• Broad Market Media• Radio• TV• Billboard• Outdoor/Cinema• Print

1 2 3 4 5 6

X XXX X X X X

X X

X X X XX X

X XX X X X

X X XX X X X X X

X XX X X

X XX

X X X

Frequency

Consumer Visits:• VRU Response• Customer Service• Web site visits

• Frequencies• Pages• Searches

• Inbound Call Centers

1 2 3 4 5 6

X XXX X X X X

X X

X X X XX X

Frequency

WE’LL SHIFT FROM POINT-IN-TIME TO TIME SERIES MODELS


“Customer Experience” Models will have a completely different set of variablesFrom point in time variables

Demographic Number of adults in HouseholdMail Order DonorBody Size of newest carLevel of educationMarket Value DecileProperty type by detailLoan To Value Range

Real EstatePast MortgagesMortgage AmountLTVHome ValueInterest RateMortgage TypeOpen Date

InvestmentsAnnuitiesBondsCertificate Of DepositIRA's/401K’sMoney Market FundsMutual FundsSavings AccountStocks

0 1 2 3 4 5 6Months

To transaction behavior over timeResponse to Broad Market Media X X XResponse to Direct mail X X# of VRU InquiriesFreq. of Web site visits (by type of visit)

Services X XProduct information X X X X XBalance Inquiry X


Model gains charts will look similar to today’s

Decile

Cumulative # of

Prospects

Cumulative # of

Responders

Cumulative Response

Rate

Cumulative % of

Responders

Cumulative % of

ProspectsCumulative

Lift1 394,592 2,576 0.653% 20.5% 8.8% 2342 813,758 4,453 0.547% 35.5% 18.1% 1963 1,250,421 6,026 0.482% 48.0% 27.8% 1734 1,696,783 7,376 0.435% 58.8% 37.7% 1565 2,158,901 8,566 0.397% 68.2% 48.0% 1426 2,631,539 9,703 0.369% 77.3% 58.5% 1327 3,076,999 10,585 0.344% 84.3% 68.4% 1238 3,546,327 11,370 0.321% 90.6% 78.8% 1159 4,022,636 12,051 0.300% 96.0% 89.4% 10710 4,500,465 12,552 0.279% 100.0% 100.0% 100

Consumer Experience Model

LIFTWith quite different variables

0 1 2 3 4 5 6 7 8 9Months

Change in balance of all mortgage accounts - Current and 4 mths prior# of currently active bankcard accounts - Current and 4 mths prior

# Personal finance inquiries - Current and 2 mths prior# of months since oldest upscale retail account opened

Ratio of Current and 2 mths prior months since most recent trade openedRatio Between Current and 2 mths prior # of accounts with delinquency of 30 daysRatio Between Current and 2 mths prior # Open rev bank trades with hc/cl > 5000


These models will assign prospects into their Appropriate Consumer Experience decile

Requiring Models for each Customer Experience

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2

3

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Decile

Cumulative # of

Prospects

Cumulative # of

Responders

Cumulative Response

Rate

Cumulative % of

Responders

Cumulative % of

ProspectsCumulative

Lift1 394,592 2,576 0.653% 20.5% 8.8% 2342 813,758 4,453 0.547% 35.5% 18.1% 1963 1,250,421 6,026 0.482% 48.0% 27.8% 1734 1,696,783 7,376 0.435% 58.8% 37.7% 1565 2,158,901 8,566 0.397% 68.2% 48.0% 1426 2,631,539 9,703 0.369% 77.3% 58.5% 1327 3,076,999 10,585 0.344% 84.3% 68.4% 1238 3,546,327 11,370 0.321% 90.6% 78.8% 1159 4,022,636 12,051 0.300% 96.0% 89.4% 10710 4,500,465 12,552 0.279% 100.0% 100.0% 100


Decile

Cumulative # of

Prospects

Cumulative # of

Responders

Cumulative Response

Rate

Cumulative % of

Responders

Cumulative % of

ProspectsCumulative

Lift1 394,592 2,576 0.653% 20.5% 8.8% 2342 813,758 4,453 0.547% 35.5% 18.1% 1963 1,250,421 6,026 0.482% 48.0% 27.8% 1734 1,696,783 7,376 0.435% 58.8% 37.7% 1565 2,158,901 8,566 0.397% 68.2% 48.0% 1426 2,631,539 9,703 0.369% 77.3% 58.5% 1327 3,076,999 10,585 0.344% 84.3% 68.4% 1238 3,546,327 11,370 0.321% 90.6% 78.8% 1159 4,022,636 12,051 0.300% 96.0% 89.4% 10710 4,500,465 12,552 0.279% 100.0% 100.0% 100


Decile

Cumulative # of

Prospects

Cumulative # of

Responders

Cumulative Response

Rate

Cumulative % of

Responders

Cumulative % of

ProspectsCumulative

Lift1 394,592 2,576 0.653% 20.5% 8.8% 2342 813,758 4,453 0.547% 35.5% 18.1% 1963 1,250,421 6,026 0.482% 48.0% 27.8% 1734 1,696,783 7,376 0.435% 58.8% 37.7% 1565 2,158,901 8,566 0.397% 68.2% 48.0% 1426 2,631,539 9,703 0.369% 77.3% 58.5% 1327 3,076,999 10,585 0.344% 84.3% 68.4% 1238 3,546,327 11,370 0.321% 90.6% 78.8% 1159 4,022,636 12,051 0.300% 96.0% 89.4% 10710 4,500,465 12,552 0.279% 100.0% 100.0% 100


Decile

Cumulative # of

Prospects

Cumulative # of

Responders

Cumulative Response

Rate

Cumulative % of

Responders

Cumulative % of

ProspectsCumulative

Lift1 394,592 2,576 0.653% 20.5% 8.8% 2342 813,758 4,453 0.547% 35.5% 18.1% 1963 1,250,421 6,026 0.482% 48.0% 27.8% 1734 1,696,783 7,376 0.435% 58.8% 37.7% 1565 2,158,901 8,566 0.397% 68.2% 48.0% 1426 2,631,539 9,703 0.369% 77.3% 58.5% 1327 3,076,999 10,585 0.344% 84.3% 68.4% 1238 3,546,327 11,370 0.321% 90.6% 78.8% 1159 4,022,636 12,051 0.300% 96.0% 89.4% 10710 4,500,465 12,552 0.279% 100.0% 100.0% 100



We’ll deploy both Consumer Experience and Response/Conversion models together

1

2

3

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Who ? (to Target)

What CE Segment Customer Experience (CE)

AMonthly e-mails of links to special discount programs

BAuto-Populate Blogs, call backs within 7 days of each purchase

CQuarterly phone calls within three days after direct mail in-home date

DPersonal Shopper in PM hrs ready with next suggestion

EDirect Mail Only with Annual Calendar of reminders

F Auto messages via email and phone of Gift Reminders and suggestions

What (Customer Experience Segment)

Which (Channel)

Mail


The Paradigms of Analytic-Driven Strategy Development/Execution

Our job is to continuously introduce new paradigmsAdding new data sources, coupled with unique derivatives and applicationsAccelerating to dynamic, near real time refreshesCreating marketing programs that reflect the way the consumer prefers to be marketed toIt’s no longer a technical or data sourcing challenge . . . It’s an innovation challenge!

MDM Lessons Learned

establishing a data quality foundation for a successful ... a data quality...3 clients 165 clients 1...

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