© 2009 Acxiom Corporation. All Rights Reserved.

The Insurance Direct Marketing Forum 2009 Increase Performance Amid Shrinking BudgetsA Multi-Channel Success Story Phil CrampeDirector, Multi-Channel Marketing StrategySavings Bank Life Insurance

Plenty of Channels

There wi

ll be

response

s flying

to and f

ro.

BudgetsRising

BudgetsFalling

A Market

ing

Prince.

An EvilWizard.

Left Page: “Evil Wizard”

A Data O

gre.

Merlin theAnalyticsMagician.

A Techno

logy

Dragon.

MarketingHeros.

But Phil

knew th

at his

secret w

eapon Wa

s his

knowledg

e of how

to use

the

power Of

sophist

icated

selling

techniqu

es in hi

s

Direct m

arketing

program

s

using al

l channe

ls.

Once upo

n a time

in land

far away

there l

ived a

prince o

f market

ing name

d

Phil. Ph

il was k

nown as

a

clever m

arketer;

Some sa

id

he was p

ossessed

of

marketin

g magic.

Neverthe

less, th

roughout

the land

and bey

ond the

sea

All thou

ght he p

ossessed

some kin

d of mar

keting

magic.

Phil was

working

for a v

ery

innovati

ve compa

ny. Thi

s

was no o

rdinary

company.

This com

pany Ena

bled Phi

l

to stand

atop t

he ………

strategi

c market

ing

pyramid.

Strategic Promotional Disciplines

Methodologies and Approaches

Media

The Strategic Marketing Pyramid*

Company Objectives

Marketing Objectives

Marketing Goals

*Chet Meisner, “The Complete Guide to Direct Marketing”, Kaplan Books, 2006

Phil, you Prince of Marketing and wunderkindI’ve been sent here for you to findIt’s your marketing magic that others do seekSo prepare to be delivered right to their feet.

So off to Massachusetts;Woburn to be exactTo Head up marketing and get them on trackSo Abra ca dabra, ala-ka-zam, With this spell I do command!

Abra-ca-dabra, ala-ka-zamI’m sending Phil to a far away land,A place without data or a 360 degree vewOf customers or prospects too.

No more budgets with lots of fatNo large staff or anything like that.No integrated marketing; no muti-channel mixJust vendors performing their usual tricks.

The castle seemed friendly enough,So Phil approached its massive doors.

On the door there was an old bronzePlaque with a relief of an ancient face.

Phil thought out loud, “I’ve seen thisFace somewhere and sometime before.”

Well, where I come fromI do not normally hear or seeMen or women cast in bronzeTalking or staring back at me.

Be that as it mayYou should still recognize me

I’m the once famous jurist and United States justice supremeI’m the honorable Louis Brandies.And what might your name be?

Well, my name is Phil but please tell me your story?And tell me what goes,Really, Louis, I’d like to know.

Louis replied with his stony face,This is the story of this ancient place.For in 1907 an idea came in my sleepTo offer all citizens insurance cheap.

So we created and sold policiesThrough all the local banksTo insure our neighbors livesFor this we received much thanks.

And we’ve been very successfulWith millions of customers galoreSatisfied with our products and serviceWe want them to come back for more.

The eyes of the Brandeis plaque squinted real hard and said, “Because we paid the Evil Wizard to bring you here?”

Things have changed and times are toughWe need more customersOur profits are not enough

So we called on the old wizardto perform his magic tricksAnd send you here to apply your multi-channel marketing fix.

Oh, our marketing team has plenty of talentBut no control over what they do

They don’t have access to customer dataOnly the vendors can tell them what’s true.

When marketing wants to know somethingAbout our customers or to get counts

They make a request of the vendors or ITIn triplicate or in larger amounts

Why, in fact we have a vendorThat provides a prospect listBut when we execute and mail itIt’s really… Well, hit or miss

And lastly, we have a vendorThat blasts emails far and wideBut we can’t tell if it’s workingOr if it makes our customers hide

Savings Bank Life Insurance, I think it has a nice ring, I’m ready for the challenge To do my marketing thing.

Introducing

Phil CrampeDirector, Email Marketing

Savings Bank Life Insurance

Captured the Data Ogre.

Applied Merlin’s Magical Analytics

Slain theTechnology

Dragon

Capturin

g

the Data

Ogre.

“BEFORE” Capturing the Data Ogre

ProspectData

ProspectData

BIBIReportsReports

List VendorList Vendor

CampaignCampaignReportsReports

EmailEmailProviderProvider

EmailEmailCampaignCampaignReportsReports

EmailEmailAnalyticAnalyticReportsReports

MarketingMarketingDeptDept

INTERTASKINTERTASKCustomerCustomer

DataData

INGENIUMINGENIUMCustomer Customer ContractsContracts

BULLBULLCustomer DataCustomer Data

CustomerData

CustomerData

Customer Customer ServiceService

FinanceFinance

“AFTER” Capturing the Data Ogre

Other Data

Other Data

ProspectData

ProspectData

Customer Data

Customer Data

Analysis &Analysis &SegmentationSegmentation

ReportingReporting

EmailSelection &Execution

EmailSelection &Execution

CampaignSelection &Execution

CampaignSelection &Execution

SBLI Data SBLI Data Warehouse & Warehouse &

DatamartDatamart

MarketingMarketingDeptDept

Customer Customer ServiceService

FinanceFinance

PurchasesResponse

Optimism about the future Social interactions

Likely to. . .

Descri

pti

ve a

nd

Pre

dic

tive C

on

su

mer

Dim

en

sio

ns

Descri

pti

ve a

nd

Pre

dic

tive C

on

su

mer

Dim

en

sio

ns

WhyWhy

HowHow

WhenWhen

WhatWhat

Ready to buy

WhereWhere

WhoWho

Demographics

Clustering/Segmentation

Lifestyles

Name Postal/Email Address

Attitudes

Propensities

Behaviors

InMarket

Contact points

READYREADY

WILLINGWILLING

AgeOccupation

InterestsHabits

Household levelGeo-demographic level

ABLEABLE

What Data is SBLI Trying to Capture?

AddressAbilityAddressAbility®®

DSFDSF22™™

NCOANCOALink®Link®**

AbiliTecAbiliTecTMTM

Consistent and Persistent Consistent and Persistent LinksLinks

Email AppendEmail Append

DemographicsDemographics

PsychographicsPsychographics

LifestylesLifestyles

HH Level SegmentationHH Level Segmentation

* Acxiom is a non-exclusive Full Service Provider Licensee of the United States Postal Service®. The following trademarks are owned by the United States Postal Service®: NCOALink®, DSF2™, USPS®, ZIP®, ZIP + 4®

The Foundation: Who and Where are my Customers?

Clean, standardized,

updated, CASS

Creates 3600 view of

customers

On DemandTelephone AppendTelephone Append

Descriptive Information

Fills the Data Gap

37

Augment with Demographics and Psychographics

Household• Marital status• Credit card indicator• Presence of children• Children’s age ranges• Household size• Income

Buying Activity• Apparel• Food/Wine• Online Purchasers/Mail Order• Electronics• Cultural• Sports• Music- Avid• >700 attributes

Interests• Gourmet• Boating/Sailing• Golf• Exercise/Health Enthusiast• Ski• Upscale Living• Spa• >100 attributes

Wealth Indicators• Highly likely investor• Net worth indicator• Real estate investor• Income producing assets• Discretionary Spending index• Consumer Prominence Indicator

PersonicX®

• Household-level segments• 100% U.S. coverage• 70 unique clusters • Product purchase propensity • Monthly updates• Includes Net Worth

Individual• Age• Education• Occupation• Gender • Ethnicity variables (rollup and country codes, country

of origin, language, religion, etc.)

Real Property• Dwelling size• Purchase date• Home market value• Available home equity• Loan detail, type and date

- Interest rate type- Lender name- Up to three lien positions

Life Events• New Parent• New Mover• Recent Divorce• Newlywed• Entering Adulthood• Empty Nester• Intend to Purchase Vehicle

Source: D&B, US Census Bureau, US Department of Health and Human Services, Administrative Office of the US Courts, Bureau of Labor Statistics, Gartner, A.T Kearney, GMA Invoice Accuracy Study

As well as the interactions we have with our customers and prospects from campaigns and channel activity

• 800,000 people will move

• 90,000 people will get married

• 30,000 people will become first-time parents

• 40,000 people will buy their first home

• 21,000 people will retire from work

Because every week…

Why SBLI Updates the DB?

IF we Didn’t Update Often?Ripple Effects of Poor Data Quality

Competitive Disadvantage

Poor customer insights and missed market opportunities

Bad StrategyInaccurate view of customer value and behavior leads to the wrong strategy or business and marketing decisions

Lost ProductivityMisguided initiatives, wasted labor reacting to customer complaints, sales complaints, etc.

Customer Relationship Irrelevant offers, multiple offers going to same customer, misuse of channel preferences

Financial Loss Lost sales and customers; possible fines

“AFTER” Capturing the Data Ogre

Data Quality – Recognition – Single View

Data Owner First MI Last Address City, State ZIP™

Finance Mary E Smith 314 Purple Sage Providence, RI 66106-5678

Services Mary S Green 234 S. Apt 436 Oak St Woburn, MA 53201

Sales Lizzie M Smith893 3rd Apt 22 Hightower Boston, MA 63110

MarketingElizabeth M Smith

1504 Elm Street, Ap 342 Woburn, MA 53204-3425

Mrs.

Mary S Green2345 Oak St, Apt. 436

Woburn, MA53201-1234

Persistent : 2998374650

SBLI data is complete, standardized, consolidated, accurate, and up-to-date

SBLI now has the amount of data needed for analytics and better decisions

SBLI recognizes this customer and her value immediately for up sell or cross sell

SBLI is taking to take control of our most precious asset and govern its usage

3

Harnessing Merlin’s

Magical analytics

Putting Merlin’s Magical Analytics to Work

1. Existing Customers

2. Identify Best Customers

3. Find Others Like Them

4. Understand Them and Predict Behavior

5. Accelerate Revenue & Profit

Predict Customer

Buying Patterns

Maximize Marketing

ROI

Increase Media

Productivity

Align Offers to the Optimal Audience / Market / Channel

Increase Revenue and Profitability

AccelerateSales

Productivity

Fuel Product Management

Goals

Maximize Channel

Productivity

SBLI Best Customers

SBLI

Customers

Quick Wins Using Descriptive Analytics

Customers Are Unique Individually and as Households

• Dual Income Household• 1 Kid• Juggle work and personal• Extended family abroad• Friday night dinner – out

at a local casual establishment

My House

• Single Income Household• 4 Kids• Live by kid’s schedules• Family lives locally• Friday night dinner –

pizza delivery

Across The Street

• Semi retired• No children• Live half the year in Florida• Extended family local and

abroad• Friday night dinner – depends

on where they are

Next Door

SBLI – 6 Clusters and 4 Life Stage Groups

Cluster #1Group 11BBoomer BaronsSummit Estates

Cluster #2Group 15MMature WealthEstablished Elite

Cluster #17Group 12BFlush FamiliesApple Pie Families

Cluster #11Group 8XJumbo FamiliesKids & Clout

Cluster #7Group 11BBoomer BaronsLeverage Lifestyles

Cluster #4Group 11BBoomer BaronsSkyboxes/Suburbans

Grp # Base Size Customers Size % Pen Index Nickname Age Marital Status Ownership Children Income Urbanicity Networth

07X 10 1,463,300 1.19% 78,994 6.00% 5.40% 503 Hard Chargers 30-45 Single Owner No Kids Affluent Suburbs & Towns <$250K

08X 19 2,689,000 2.19% 124,976 9.49% 4.65% 433 Country Comfort 36-55 Married Owner Kids; Age Mix Upper Middle Rural <$100K

11B 4 2,264,300 1.84% 96,995 7.37% 4.28% 399 Skyboxes & Suburbans 36-55 Married Owner School-age Kids Wealthy Suburbs & Towns $1-2MM

15M 2 4,010,600 3.27% 131,857 10.01% 3.29% 307 Established Elite 46-65 Married/Single Owner No Kids Wealthy City & Surrounds $2MM+

14B 22 2,044,100 1.66% 57,404 4.36% 2.81% 262 Fun & Games 46-55 Married Owner No Kids Upper Middle Suburbs & Towns <$100K

07X 26 3,612,600 2.94% 100,774 7.65% 2.79% 260 Savvy Singles 30-45 Single Renter/Owner No Kids Upper Middle City & Surrounds <$250K

08X 12 1,465,300 1.19% 38,482 2.92% 2.63% 245 Tots & Toys 30-45 Married Owner Toddlers/Preschool Affluent Suburbs & Towns <$100K

19M 8 2,354,900 1.92% 56,667 4.30% 2.41% 224 Full Steaming 56-65 Married/Single Owner No Kids Affluent Suburbs & Towns $500K-$1MM

17M 44 1,074,500 0.88% 23,722 1.80% 2.21% 206 Community Singles 56-65 Single Owner No Kids Low Middle City & Surrounds <$500K

11B 1 2,932,700 2.39% 61,693 4.69% 2.10% 196 Summit Estates 36-55 Married Owner School-age Kids Wealthy City & Surrounds $2MM+

07X 6 954,300 0.78% 16,044 1.22% 1.68% 157 Shooting Stars 30-45 Married/Single Owner/Renter No Kids Affluent Suburbs & Towns <$250K

Top 20 24,865,600 20.25% 787,608 59.81% 3.17% 295

06X 69 644,600 0.52% 1,460 0.11% 0.23% 21 Mortgage Woes 30-45 Single Owner No Kids Lowest City & Surrounds <$100K

02Y 24 1,630,200 1.33% 3,439 0.26% 0.21% 20 Career Building 24-29 Single Renter/Owner No Kids Upper Middle City & Surrounds <$250K

03X 41 1,073,800 0.87% 2,209 0.17% 0.21% 19 Trucks & Trailers 30-45 Single/Married Owner/Renter No Kids Low Middle Rural <$100K

21S 66 2,274,000 1.85% 4,328 0.33% 0.19% 18 Timeless Elders 76+ Single Owner No Kids Lowest City & Surrounds <$250K

10B 53 3,107,800 2.53% 6,159 0.47% 0.20% 18 Metro Parents 36-55 Single Parent Owner School-age Kids Low Middle Downtown Metro <$250K

20S 25 1,725,400 1.41% 3,366 0.26% 0.20% 18 Clubs & Causes 66-75 Married/Single Owner No Kids Upper Middle Suburbs & Towns $100K-$499K

10B 60 962,600 0.78% 1,748 0.13% 0.18% 17 Rural Rovers 36-55 Single Renter No Kids Low Middle Rural <$100K

21S 49 1,917,100 1.56% 3,200 0.24% 0.17% 16 Sedentarians 76+ Married Owner No Kids Low Middle City & Surrounds <$250K

16M 23 1,472,300 1.20% 2,587 0.20% 0.18% 16 Acred Couples 56-65 Married Owner No Kids Upper Middle Suburbs & Towns $100K-$499K

19M 5 2,113,800 1.72% 3,261 0.25% 0.15% 14 Sitting Pretty 46-65 Married Owner No Kids Wealthy Suburbs & Towns $250K-$999K

04X 59 1,415,700 1.15% 2,028 0.15% 0.14% 13 Low Rent Digs 30-35 Single Renter No Kids Low City & Surrounds <$100K

01Y 57 1,118,400 0.91% 1,146 0.09% 0.10% 10 Collegiate Crowd 18-23 Single Renter No Kids Low Middle City & Surrounds <$100K

02Y 21 1,674,800 1.36% 1,652 0.13% 0.10% 9 Children First 24-29 Married/Single Parents Owner/Renter Kids; Age Mix Upper Middle Suburbs & Towns <$100K

16M 15 1,427,900 1.16% 1,407 0.11% 0.10% 9 Country Ways 46-65 Married Owner No Kids Affluent Rural <$500K

21S 64 1,528,700 1.24% 1,066 0.08% 0.07% 7 Rural Antiques 76+ Single Owner/Renter No Kids Low Rural <$250K

03X 34 1,441,500 1.17% 882 0.07% 0.06% 6 Outward Bound 30-45 Married Owner No Kids Middle Rural <$100K

01Y 58 857,400 0.70% 559 0.04% 0.07% 6 Young Workboots 18-29 Single Owner/Renter No Kids Low Rural <$100K

18M 54 1,471,000 1.20% 820 0.06% 0.06% 5 Still Truckin' 46-65 Single Owner No Kids Low Rural <$500K

18M 50 1,915,500 1.56% 695 0.05% 0.04% 3 The Greatest Generation 66+ Married Owner No Kids Low Middle Rural <$500K

Totals: 122,787,700 100.00% 1,316,788 100.00% 1.07% 100

Step 1 – Use SBLI Customer Data to find Opportunities

HH Portrait Ranked by Index of Likelihood to buy SBLI Products

Cluster 10 HHs are 5.03 times more

likely to be a Premier Product

customer

Cluster 24 HHs are 80% less likely to be a Premier Product

customer

Step 2 – Organize into Actionable Target Groups

# Base Size Customers Size % Pen Index Age Marital Status Ownership Children Income Urbanicity Networth

Best Targets

2 4,010,600 3.27% 131,857 10.01% 3.29% 307 46-65 Married/Single Owner No Kids Wealthy City & Surrounds $2MM+

22 2,044,100 1.66% 57,404 4.36% 2.81% 262 46-55 Married Owner No Kids Upper Middle Suburbs & Towns <$100K

8 2,354,900 1.92% 56,667 4.30% 2.41% 224 56-65 Married/Single Owner No Kids Affluent Suburbs & Towns $500K-$1MM

6 954,300 0.78% 16,044 1.22% 1.68% 157 30-45 Married/Single Owner/Renter No Kids Affluent Suburbs & Towns <$250K

3 1,886,500 1.54% 25,913 1.97% 1.37% 128 46-65 Single/Married Owner/Renter No Kids Wealthy City & Surrounds $1-2MM

Total 11,250,400 9.16% 287,885 21.86% 2.56% 239

Home Owners - Kids

19 2,689,000 2.19% 124,976 9.49% 4.65% 433 36-55 Married Owner Kids; Age Mix Upper Middle Rural <$100K

4 2,264,300 1.84% 96,995 7.37% 4.28% 399 36-55 Married Owner School-age Kids Wealthy Suburbs & Towns $1-2MM

12 1,465,300 1.19% 38,482 2.92% 2.63% 245 30-45 Married Owner Toddlers/Preschool Affluent Suburbs & Towns <$100K

1 2,932,700 2.39% 61,693 4.69% 2.10% 196 36-55 Married Owner School-age Kids Wealthy City & Surrounds $2MM+

27 2,186,100 1.78% 35,430 2.69% 1.62% 151 36-45 Married Owner School-age Kids Upper Middle City & Surrounds <$100K

Total 11,537,400 9.40% 357,576 27.16% 3.10% 289

Single - No Kids

26 3,612,600 2.94% 100,774 7.65% 2.79% 260 30-45 Single Renter/Owner No Kids Upper Middle City & Surrounds <$250K

44 1,074,500 0.88% 23,722 1.80% 2.21% 206 56-65 Single Owner No Kids Low Middle City & Surrounds <$500K

33 1,567,700 1.28% 15,704 1.19% 1.00% 93 46-55 Single Renter No Kids Middle Downtown Metro <$250K

61 1,457,700 1.19% 14,022 1.06% 0.96% 90 24-35 Single Renter No Kids Low Middle Downtown Metro <$100K

Total 7,712,500 6.28% 154,222 11.71% 2.00% 186

Suburbs - No Kids

10 1,463,300 1.19% 78,994 6.00% 5.40% 503 30-45 Single Owner No Kids Affluent Suburbs & Towns <$250K

20 1,223,300 1.00% 13,010 0.99% 1.06% 99 36-45 Married Owner No Kids Upper Middle Suburbs & Towns <$100K

65 1,935,400 1.58% 19,557 1.49% 1.01% 94 66-75 Single Owner/Renter No Kids Lowest Suburbs & Towns $100K-$499K

Total 4,622,000 3.76% 111,561 8.47% 2.41% 225

Low Income

62 4,002,900 3.26% 45,512 3.46% 1.14% 106 30-45 Single/Married Parents Renter School-age Kids Low City & Surrounds <$100K

47 1,509,100 1.23% 16,420 1.25% 1.09% 101 36-55 Single Parent Owner School-age Kids Low Middle Rural <$250K

Total 5,512,000 4.49% 61,932 4.70% 1.12% 105

Best Targets Overall to

market for SBLI

Suburban dwellers with no kids …..start

insurance nurturing program

Homeowners with kids have specific insurance needs

Step 3 – Learn Group Behaviors and PreferencesMediamark Research Inc. [Study] 2005

Copyright 2005 Mediamark Research Inc. All Rights Reserved

Personicx LifeStyle Snapshot Report Copyright 2006, Acxiom Corp.

(This report shows a snapshot of several consumer / behavior categories compared to a specific Target Group or Segment)

Target Group: Best TargetsSegments:2,3,6,8,22Consumer Products & Purchases Index Heavy Users IndexSport/Recreation Equipment - High Ticket Items - ::Own:Downhill ski boots 168 Domestic Dinner/Table Wines - Drinks or Glasses/Last 7 Days:::Heavy (4+) 191

Sport/Recreation Equipment - Low Ticket Items - ::Own:Cross country skis 167 Whole Coffee Beans - Number Of Pounds/Last 30 Days:(Principal Shoppers)::Heavy (3+) 179

Camera & Developing Accessories - Amount Spent In Total - :In last 12 months::$100+ 166 Vodka - Drinks or Glasses/Last 30 Days:::Heavy (6+) 163

Sport/Recreation Equipment - High Ticket Items - ::Own:Downhill skis 166 Nutrition/Energy Bars - Bars/Last 30 Days:(Principal Shoppers)::Heavy (5+) 159

Sport/Recreation Equipment - Low Ticket Items - ::Own:Cross country ski boots 164 Mixed Drinks - Drinks or Glasses/Last 30 Days:::Heavy (6+) 156

Fine Jewelry - Amount Spent In Total - :In last 12 months::$1000+ 163 Firelogs - Logs/Last 12 Months:(Principal Shoppers)::Heavy (8+) 155

Pre-Recorded Audio Tapes & Compact Discs - Bought in last 12 months::Primary:Easy Listening 163 Water Softening Salts - Containers/Last 6 Months:(Principal Shoppers)::Heavy (6+) 151

Sport/Recreation Equipment - Low Ticket Items - ::Own:Sportswatch/chronograph 162 Vitamin And Dietary Supplements - Times/Last 7 Days:::Heavy (21+) 148

Sunglasses - Amount Spent In Total - :In last 12 months::$100+ 162 Professional Exterminators - Times/Last 12 Months:::Heavy (5+) 144

Sport/Recreation Equipment - High Ticket Items - ::Own:Stationary bicycle 161 Suntan & Sunscreen Products - Times/Last 30 Days (in season):::Heavy (7+) 142

Recreation & Hobbies Index Shopping IndexOrganizations/Clubs - Member Of - :::Country clubs 229 Department- Clothing & Specialty Stores - Times Shopped - ::In last 3 months: Any:Nordstrom 203

Organizations/Clubs - Member Of - :::Business Club 195 Catalog- Mail- Phone And Internet Order - Amount Spent In Total - :In last 12 months::$800+ 188

Sports - How Often Engaged In - ::Participated in last 12 months:Snorkeling/skin diving 173 Drug Stores - Times Shopped - ::In last 6 months: Any:Longs Drug Store 186

Casino Gambling - Times Gambled - :In last 12 months:Any:Las Vegas 170 Catalog- Mail- Phone And Internet Order - Ordered From - :In last 12 months::L.L. Bean 185

Sports Events - Attend - ::Less than once a month:Golf 162 Office/Computer Supply Stores - Times Shopped - ::In last 30 days: 1:Comp USA 182

Sports - How Often Engaged In - ::Participated in last 12 months:Golf 159 Department- Clothing & Specialty Stores - Times Shopped - ::In last 3 months: Any:Lord & Taylor 181

Leisure Activities - How Often Engaged In - ::Participated in last 12 months:Attend horse races 157 Food Stores- Grocery & Warehouse/Club Stores - Times Shopped - :Warehouse/Club Stores:In last 6 months: Any:Price Costco 175

Leisure Activities - How Often Engaged In - ::Participated in last 12 months:Go to live theater 156 Clothing Expenditures - Amount Spent in Total - :In last 12 months::$2,000+ 171

Sports - How Often Engaged In - ::Participated in last 12 months:Skiing - Downhill 156 Catalog- Mail- Phone And Internet Order - Ordered From - :In last 12 months::bestbuy.com 167

Organizations/Clubs - Member Of - :::Charitable Organizations 155 Catalog- Mail- Phone And Internet Order - Ordered From - :In last 12 months::amazon.com 166

SBLI “Best Target”Lifestyle characteristics

can impact strategy, tactics, message and

creative.

Step 4 – Find Concentration of Each Target Group

Copyright 2006, Acxiom Corp.

Personicx Geographic Ranking Report(This report shows where there are high concentrations of a specific Target Group or Segment)

Target Group: Best TargetsSegments:2,3,6,8,22DMA Geography Index of Concentration Target Households

807 SAN FRAN-OAK-SJ 267 648,322

828 MONTEREY-SALNAS 249 53,941

855 SANTABAR-SM-SLO 203 44,174

825 SAN DIEGO 181 180,314

744 HONOLULU 176 66,059

804 PALM SPRINGS 163 21,190

511 WASH DC (HAG) 159 364,421

803 LOS ANGELES 149 785,479

862 SACRMNTO-STK-MO 148 199,250

821 BEND OR 145 9,023

548 WEST PLM BCH-FP 139 103,537

506 BOSTON (MANCHR) 138 340,070

819 SEATTLE-TACOMA 135 229,091

501 NEW YORK 127 953,053

571 FT. MYERS-NAPLS 126 54,733

751 DENVER 125 186,569

602 CHICAGO 124 409,848

504 PHILADELPHIA 119 354,948

512 BALTIMORE 113 126,297

743 ANCHORAGE 112 13,954

635 AUSTIN 111 68,651

811 RENO 111 25,783

SBLI has specific geographical areas of interest. What Target

Groups dominate those geographies? How

many are there?

Step 5 – Clusters Help Execute Marketing Strategy

Cluster #13

Cluster #37

Cluster #12

Same ProductDifferent TargetsDifferent CreativeDifferent MessagesPreferred Channels

Offer in Spanish

Step 6: Use Cluster Response Analysis

Response Cost per Thousand helps determine which HHs cost the most to market to.

With an Index of 294 this cluster

responded nearly 3X more than any

other cluster

Response per thousand cost are very high for these

clusters.

MA 1 2 3 4 5 6 7 8 9 10 Total Selection1 82,785 72,785 74,585 78,211 71,256 69,425 60,258 59,654 42,597 39,548 651,104 449,0472 13,439 10,439 12,239 15,865 28,910 27,079 17,912 17,308 10,251 7,202 160,644 107,9713 6,587 8,759 7,859 6,984 5,987 5,689 5,686 4,589 6,584 6,587 65,311 41,8654 2,548 3,698 4,589 4,587 5,489 6,987 5,987 5,986 5,478 8,749 54,098 27,8985 1,589 1,478 2,659 3,654 3,698 3,478 4,856 4,789 5,987 5,963 38,151 16,5566 1,369 1,256 2,658 2,478 2,698 3,695 4,589 5,478 6,954 6,589 37,764 643,3377 419 1,885 2,369 2,587 2,458 3,695 5,896 6,954 4,589 4,589 35,4418 75 1,259 2,659 3,478 2,369 2,654 3,548 3,698 2,569 5,478 27,7879 213 658 759 1,256 1,236 1,478 1,589 2,548 3,698 2,698 16,13310 234 545 789 1,589 1,258 3,698 3,698 2,569 158 1,158 15,696

Total 109,258 102,762 111,165 120,689 125,359 127,878 114,019 113,573 88,865 88,561 1,102,129

Predictive Intelligence – Prospect Model

MA 1 2 3 4 5 6 7 8 9 10 Total Selection1 82,785 72,785 74,585 78,211 71,256 69,425 60,258 59,654 42,597 39,548 651,104 449,0472 13,439 10,439 12,239 15,865 28,910 27,079 17,912 17,308 10,251 7,202 160,644 107,9713 6,587 8,759 7,859 6,984 5,987 5,689 5,686 4,589 6,584 6,587 65,311 557,0184 2,548 3,698 4,589 4,587 5,489 6,987 5,987 5,986 5,478 8,749 54,0985 1,589 1,478 2,659 3,654 3,698 3,478 4,856 4,789 5,987 5,963 38,1516 1,369 1,256 2,658 2,478 2,698 3,695 4,589 5,478 6,954 6,589 37,7647 419 1,885 2,369 2,587 2,458 3,695 5,896 6,954 4,589 4,589 35,4418 75 1,259 2,659 3,478 2,369 2,654 3,548 3,698 2,569 5,478 27,7879 213 658 759 1,256 1,236 1,478 1,589 2,548 3,698 2,698 16,13310 234 545 789 1,589 1,258 3,698 3,698 2,569 158 1,158 15,696

Total 109,258 102,762 111,165 120,689 125,359 127,878 114,019 113,573 88,865 88,561 1,102,129

MA 1 2 3 4 5 6 7 8 9 10 Total Selection1 82,785 72,785 74,585 78,211 71,256 69,425 60,258 59,654 42,597 39,548 651,104 449,0472 13,439 10,439 12,239 15,865 28,910 27,079 17,912 17,308 10,251 7,202 160,644 107,9713 6,587 8,759 7,859 6,984 5,987 5,689 5,686 4,589 6,584 6,587 65,311 41,8654 2,548 3,698 4,589 4,587 5,489 6,987 5,987 5,986 5,478 8,749 54,098 27,8985 1,589 1,478 2,659 3,654 3,698 3,478 4,856 4,789 5,987 5,963 38,151 16,5566 1,369 1,256 2,658 2,478 2,698 3,695 4,589 5,478 6,954 6,589 37,764 14,1547 419 1,885 2,369 2,587 2,458 3,695 5,896 6,954 4,589 4,589 35,441 13,4138 75 1,259 2,659 3,478 2,369 2,654 3,548 3,698 2,569 5,478 27,787 12,4949 213 658 759 1,256 1,236 1,478 1,589 2,548 3,698 2,698 16,133 5,60010 234 545 789 1,589 1,258 3,698 3,698 2,569 158 1,158 15,696 8,113

Total 109,258 102,762 111,165 120,689 125,359 127,878 114,019 113,573 88,865 88,561 1,102,129 697,111

Flexibility to select wider or deeper deciles

Likely to Respond

Lik

ely

to P

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has

e

Flexibility to select wider or deeper deciles

Flexibility to select wider or deeper deciles

Slaying

the

Technolo

gy

Dragon.

What Technology Did We have “Before”?

What Technology Do We have “After”?

SBLI MarketEdge-X

Pro

spec

t D

ata

Pro

spec

t D

ata

Source #1Source #1

Source #3Source #3

Infobase Enhancement

Infobase Enhancement

Analytics Targeting Campaigns Execute

SBLIMarketEdge-X

DB

SBLIMarketEdge-X

DB

Results

Real-time E-Mail Click Through Response

Tracking

Data Integration

Cleanse Identify Enrich

List #3List #3

Data SourcesData Sources

List #2List #2

List #1List #1

Source #2Source #2

InfobaseList

Cu

sto

mer

Dat

a C

ust

om

er D

ata

Technology at our Fingertips“The Home Page”

High level insights of customers and prospects at the individual and household level

Change in number of Customers and Prospectsduring the DB update.

Technology at our Fingertips“What New with your Data?”

Number of individuals promoted across multiple channels and their responses.

Technology at our Fingertips“Seeing All Your Data?”

The entire marketing database is available see and explore.

Selecting the Account Table to explore all the SBLI customer data.

Technology at our Fingertips

I can see the MTA, a Teacher’s group to which SBLI markets, and the count with the click of a mouse.

List of all unique sources that have been used to populate our customer database

A list of all unique prospect sources and the counts that have been used to populate our database.

We developed a model to help us select only the best prospects.

Technology at our Fingertips

Technology at our Fingertips“Analyzing our Customers”

Using any of the attributes we can easily perform analysis to gain marketing insights.

This simple example resulted from an analysis of geographical regions, then selecting two states to compare customer counts.

Technology at our Fingertips“Strategic Selection”

Using advance logic and rules we are able to easily create a targeted list to market or perform more analysis.

We’ve now generated a waterfall of counts based on the selection logic. We can fine tune the logic “on the fly” to get the counts we need.

Technology at our Fingertips“Waterfall Report”

Technology at our Fingertips“Best Customer Analysis”

We can drag and drop any data element of interest in a train-of-thought analytical approach.

Technology at our Fingertips“Best Customer Analysis”

Technology at our Fingertips“Best Customer Analysis”

Technology at our Fingertips“Best Customer Analysis”

SBLI Benefits Recap

Data is robust and kept current…..automatically!

Customer profiles…….first time ever!

Campaigns from desktop……analytically driven!

Measurable results…..closed loop marketing!

Efficiency…..what took 2 weeks is now 1 hour!

Improved internal relations…..IT comes to us!

SBLI Results

Response...1% or 10%... That’s the Question

Conversion Rate….17% ahead of plan

Value Per Policy….3% avg premium @ contract

Budget…..15%.....Same Aggressive goals

Sales Force…..5%....

In the Worst Recession in History

SBLI has captured its dataAnd the Data Ogre is goneUsed Merlin’s analytics To make their campaigns strong

The y’ve slain the Technology Dragon And his carcass is buried out backNow the marketers are heroes andDirect marketing is totally on track

So we end this fairy tale Like all fairy tales doWith a Happy every after And good marketing to you.

Questions and Answers