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The Insurance Direct Marketing Forum 2009

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Title: The Insurance Direct Marketing Forum 2009


1
The Insurance Direct Marketing Forum 2009
Increase Response Amid Shrinking Budgets A
Multi-Channel Success Story Phil
Crampe Director, Multi-Channel Marketing
Strategy Savings Bank Life Insurance
2
Plenty of Channels
3
There will be responses flying to and fro.
4
Budgets Rising
Budgets Falling
5
A Marketing Prince.
6
Left Page Evil Wizard
An Evil Wizard.
7
A Data Ogre.
8
Merlin the Analytics Magician.
9
A Technology Dragon.
10
Marketing Heros.
11
Nevertheless, throughout the land and beyond the
sea All thought he possessed some kind of
marketing magic. Phil was working for a very
innovative company. This was no ordinary
company. This company Enabled Phil to stand atop
the strategic marketing pyramid.
But Phil knew that his secret weapon Was his
knowledge of how to use the power Of
sophisticated selling techniques in his Direct
marketing programs using all channels.
Once upon a time in land far away there lived a
prince of marketing named Phil. Phil was known as
a clever marketer Some said he was possessed of
marketing magic.
12
The Strategic Marketing Pyramid
Company Objectives
Marketing Objectives
Marketing Goals
Strategic Promotional Disciplines
Methodologies and Approaches
Media
13
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14
So off to Massachusetts Woburn to be exact To
Head up marketing and get them on track So Abra
ca dabra, alla-ka-zam, With this spell I do
command!
Abra-ca-dabra, ala-ka-zam Im sending Phil to a
far away land, A place without data or a 360
degree vew Of customers or prospects too.
No more budgets with lots of fat No large staff
or anything like that. No integrated marketing
no muti-channel mix Just vendors performing their
usual tricks.
15
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16
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17
Well, where I come from I do not normally hear or
see Men or women cast in bronze Talking or
staring back at me.
18
Im the once famous jurist and United States
justice supreme The honorable Louis
Brandies. And what might your name be?
19
Well, my name is Phil but please tell me your
story? And tell me what goes, Really, Louis, Id
like to know.
20
Louis replied with his stony face, This is the
story of this ancient place. For in 1907 an idea
came in my sleep To offer all citizens insurance
cheap.
21
So we created and sold policies Through all the
local banks To insure our neighbors lives For
this we received much thanks.
22
The eyes of the Brandeis plaque squinted real
hard and said, Because we paid the Evil Wizard
to bring you here?
And weve been very successful With millions of
customers galore Satisfied with our products and
service We want them to come back for more.
23
Things have changed and times are tough We need
more customers Our profits are not enough So we
called on the old wizard to perform his magic
tricks And send you here to apply your
multi-channel marketing fix.
24
Oh, our marketing team has plenty of talent But
no control over what they do They dont have
access to customer data Only the vendors can tell
them whats true.
25
When marketing wants to know something About our
customers or to get counts They make a request
of the vendors or IT In triplicate or in larger
amounts
26
Why, in fact we have a vendor That provides a
prospect list But when we execute and mail
it Its really Well, hit or miss
27
And lastly, we have a vendor That blasts emails
far and wide But we cant tell if its working Or
if it makes our customers hide
28
Savings Bank Life Insurance, I think it has a
nice ring, Im ready for the challenge To do my
marketing thing.
29
IntroducingPhil CrampeDirector, Email
MarketingSavings Bank Life Insurance
30
Capturing the Data Ogre.
31
BEFORE Capturing the Data Ogre
INTERTASK Customer Data
BULL Customer Data
List Vendor
Marketing Dept
Campaign Reports
Finance
INGENIUM Customer Contracts
BI Reports
Email Provider
Email Analytic Reports
Customer Service
Email Campaign Reports
32
AFTER Capturing the Data Ogre
Marketing Dept
Customer Service
Finance
33
SBLI Customer Data Integration
1st PassHygiene
BestAddress 170 MillionAddress Records
Use AbiliTec to compare to BestAddress for
potential address corrections
SBLI Weekly updates 10 of all old records
2nd PassHygiene
SBLI Stops HereToday
Apply Business Rules to choose most deliverable
3rd PassHygiene
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
34
AFTER Capturing the Data OgreData 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 Smith 893 3rd Apt 22 Hightower Boston, MA 63110
? Marketing Elizabeth M Smith 1504 Elm Street, Ap 342 Woburn, MA 53204-3425
? Mrs. Mary S Green 2345 Oak St, Apt. 436 Woburn, MA 53201-1234 Persistent 2998374650
35
SBLI Customer Data is Constantly Changing
Because in one year
  • 4.7 mm people will get married
  • 1.53 mm people will become first-time parents
  • 2.04 mm people will buy their first home
  • 1.9 mm people will get divorced
  • 43 mm people will move
  • 1.4 mm people will retire from work

As well as the interactions we have with our
customers and prospects from campaigns and
channel activity
Source DB, 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
36
Ripple Effects of Poor Data QualityBefore
Capturing the Data Ogre
Competitive Disadvantage Poor customer insights and missed market opportunities
Bad Strategy Inaccurate view of customer value and behavior leads to the wrong strategy or business and marketing decisions
Lost Productivity Misguided 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
37
Augment with Demographics and Psychographics
  • 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.)
  • Household
  • Marital status
  • Credit card indicator
  • Presence of children
  • Childrens age ranges
  • Household size
  • Income
  • 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
  • Buying Activity
  • Apparel
  • Food/Wine
  • Online Purchasers/Mail Order
  • Electronics
  • Cultural
  • Sports
  • Music- Avid
  • gt700 attributes
  • Interests
  • Gourmet
  • Boating/Sailing
  • Golf
  • Exercise/Health Enthusiast
  • Ski
  • Upscale Living
  • Spa
  • gt100 attributes
  • Life Events
  • New Parent
  • New Mover
  • Recent Divorce
  • Newlywed
  • Entering Adulthood
  • Empty Nester
  • Intend to Purchase Vehicle

37
38
SBLI Vision for Data Infrastructure
When
Ready to buy
READY
Purchases Response
What
How
Likely to. . .
Why
Optimism about the future Social interactions
WILLING
Where
Contact points
ABLE
39
Harnessing Merlins Magical analytics
40
70 Clusters 21 Groups
41
Augment with Household-Level Segmentation
Three Next-Door Neighbors Three Different Life
Stages
The Green Household
The Smith Household
The Jones Household
Same ZIP 4, Block Group, Tract and
ZIP Different households with different Life
Stage Clusters
42
Birds of a Feather May Not flock together
Married, 2 College Students, Small Business Owner
Married, 2 Toddlers, Likes to Swim
Married, 2 Teens, Likes to Cook
22
20
36
Single, Wealthy, Retired
27
Married, Wealthy, 2 Teens, Elderly Parent
Married, Mid-Income Retired, Raising 2 Grandkids
23
44
10
Married, No Kids, Lower Income 1st Time Home
43
SBLI 6 Clusters and 4 Life Stage Groups
Cluster 2 Group 15M Mature Wealth Established
Elite
Cluster 7 Group 11B Boomer Barons Leverage
Lifestyles
Cluster 4 Group 11B Boomer Barons Skyboxes/Suburb
ans
Cluster 1 Group 11B Boomer Barons Summit Estates
Cluster 17 Group 12B Flush Families Apple Pie
Families
Cluster 11 Group 8X Jumbo Families Kids
Clout
44
Step 1 Use 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
45
Step 2 Organize into Actionable Target Groups
Best Targets Overall to market
Homeowners with kids have specific insurance
needs
Specific Cluster of Suburban dwellers with no
kids ..start insurance nurturing program
46
Step 3 Learn Group Behaviors and Preferences
Specific Clusters have lifestyle characteristics,
like recreational and hobby interests, that can
be used effectively in messaging and content to
specific segments of HH clusters
47
Step 4 Find Concentration of Each Target Group
SBLI has specific geographical areas of interest.
What Target Groups dominate those geographies?
How many are there?
48
Step 5 Clusters Help Execute Marketing Strategy
  • Same Product
  • Different Targets
  • Different Creative
  • Different Messages
  • Preferred Channels

Cluster 13
Cluster 37
Cluster 12
49
Step 6 Use Cluster Response Analysis
With an Index of 294 this cluster responded
nearly 3X more than any other cluster
Response per thousand cost are very for these
clusters.
Response Cost per Thousand helps determine which
HHs cost the most to market to.
50
Portrait Comparisons for Cross-Sell
Cluster households most likely to use both SBLI
Premier Product 1 2
Cross-sell priorities More likely to use both
products
More likely to use Premier Product 1 and less
likely to use Product 2
More likely to use Premier Product 2 and less
likely to use Product 1
Less likely to use either product
51
Analytical and Planning Applications
Competitive SWOT Analysis
T
W
Cluster households under most competitive pressure
S
O
Cross-sell priorities More likely to use both
products
More likely to use Premier Product 1 and less
likely to use Product 2
More likely to use Premier Product 2 and less
likely to use Product 1
Less likely to use either product
52
Modeling
53
Putting Predictive Intelligence 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
AccelerateSales Productivity Fuel
Product Management Goals Maximize Channel
Productivity
Align Offers to the Optimal Audience / Market /
Channel
Increase Revenue and Profitability
54
Slaying the Technology Dragon.
55
Technology at our FingertipsThe Home Page
High level insights of customers and prospects at
the individual and household level
56
Technology at our FingertipsWhat New with your
Data?
Change in number of Customers and
Prospects during the DB update.
Number of individuals promoted across multiple
channels and their responses.
57
Technology at our FingertipsSeeing All Your
Data?
The entire marketing database is available see
and explore.
Selecting the Account Table to explore all the
SBLI customer data.
58
Technology at our Fingertips
List of all unique sources that have been used to
populate our customer database
I can see the MTA, a Teachers group to which
SBLI markets, and the count with the click of a
mouse.
59
Technology at our Fingertips
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.
60
Technology at our FingertipsAnalyzing 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.
61
Technology at our FingertipsStrategic Selection
Using advance logic and rules we are able to
easily create a targeted list which to market or
perform more analysis.
62
Technology at our FingertipsWaterfall Report
Weve 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.
63
Technology at our FingertipsBest Customer
Analysis
Marketers can drag and drop any data element of
interest in a train-of-thought analytical
approach.
64
Technology at our FingertipsBest Customer
Analysis
65
Technology at our FingertipsBest Customer
Analysis
66
Technology at our FingertipsBest Customer
Analysis
67
SBLI Benefits Recap
  • Data is robust and kept current..automatically!
  • Customer profiles.first time ever!
  • Campaigns from desktopanalytically driven!
  • Measurable results..closed loop marketing!
  • Efficiency..what took 2 weeks is now 1 hour!
  • Improved internal relations..IT comes to us!

68
SBLI has captured its data And the Data Ogre is
gone Used Merlins analytics To make their
campaigns strong The yve slain the Technology
Dragon And his carcass is buried out back Now
the marketers are heroes and Direct marketing is
totally on track So we end this fairy tale Like
all fairy tales do With a Happy every after and
And good marketing to you.
69
Questions and Answers
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