Insurance Forum Fairy Tale SBLI
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© 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
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Plenty of Channels
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There wi
ll be
response
s flying
to and f
ro.
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BudgetsRising
BudgetsFalling
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A Market
ing
Prince.
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An EvilWizard.
Left Page: “Evil Wizard”
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A Data O
gre.
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Merlin theAnalyticsMagician.
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A Techno
logy
Dragon.
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MarketingHeros.
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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.
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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
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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.
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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.
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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.”
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Well, where I come fromI do not normally hear or seeMen or women cast in bronzeTalking or staring back at me.
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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?
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Well, my name is Phil but please tell me your story?And tell me what goes,Really, Louis, I’d like to know.
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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.
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So we created and sold policiesThrough all the local banksTo insure our neighbors livesFor this we received much thanks.
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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?”
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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.
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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.
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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
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Why, in fact we have a vendorThat provides a prospect listBut when we execute and mail itIt’s really… Well, hit or miss
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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
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Savings Bank Life Insurance, I think it has a nice ring, I’m ready for the challenge To do my marketing thing.
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Introducing
Phil CrampeDirector, Email Marketing
Savings Bank Life Insurance
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Captured the Data Ogre.
Applied Merlin’s Magical Analytics
Slain theTechnology
Dragon
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Capturin
g
the Data
Ogre.
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“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
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“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
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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?
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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
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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
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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?
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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
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“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
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Harnessing Merlin’s
Magical analytics
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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
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Quick Wins Using Descriptive Analytics
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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
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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
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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
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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
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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.
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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?
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Step 5 – Clusters Help Execute Marketing Strategy
Cluster #13
Cluster #37
Cluster #12
Same ProductDifferent TargetsDifferent CreativeDifferent MessagesPreferred Channels
Offer in Spanish
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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.
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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
urc
has
e
Flexibility to select wider or deeper deciles
Flexibility to select wider or deeper deciles
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Slaying
the
Technolo
gy
Dragon.
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What Technology Did We have “Before”?
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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
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Technology at our Fingertips“The Home Page”
High level insights of customers and prospects at the individual and household level
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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.
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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.
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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
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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
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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.
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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.
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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”
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Technology at our Fingertips“Best Customer Analysis”
We can drag and drop any data element of interest in a train-of-thought analytical approach.
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Technology at our Fingertips“Best Customer Analysis”
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Technology at our Fingertips“Best Customer Analysis”
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Technology at our Fingertips“Best Customer Analysis”
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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!
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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
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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.
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Questions and Answers