This document discusses how insurance marketers can leverage analytics to better understand customers and improve marketing strategies. It provides examples of how descriptive and diagnostic analytics can help answer questions about what is happening and why. Predictive analytics can then indicate what may happen in the future. The document advocates moving from a "sense and respond" approach to a "predict and act" strategy using analytics to gain competitive advantages. Examples of distribution management and account-based marketing use cases demonstrate potential benefits like reduced cancellations and improved campaign response rates.

1. Smart Analytics
for insurance
marketers

2. Disruption in insurance industry
Source: World Economic Forum report, June 2015
Source: Gartner report, Feb 2016

3. Timing to engage client
No.1 priority for insurance marketers

4. Distribution
Biggest dilemma for insurance marketer
towards leveraging analytics – only a
Perception error
CustomersCarrier

5. Comparison
CPG Industry

6. Example
FixedAnnuity

7. Buyer Persona
 Name:Dr. JohnM.D.
 Gender:Male
 Age:50-55
 Occupation:Oncologist
 Education:Bachelorof
Science,BachelorofMedicine
 Salary:$350,000-$400,000
USD
 Family:Wifeandtwocollege
ageddaughters
Is this information good enough to pitch our products
to Dr. John?

8. Buyer Persona (contd.)
 Priority:Supportinghisdaughtersthroughcollege.
Maintainingupscalelifestyleafterretirement,including
vacations,cars,andhouseimprovements.
 PainPoints(issues/problems):Time,oftenchallenged
findingawork/playrelationship
 Motivation:Retire(comfortably)
 Needs:Trustworthyadvisor,whoisefficientandeffective
 Habits
MediaConsumption:FinancialPublications,Financial
AdvisorBlogs
Likes:Beingtreatedasanimportantclient,golf,family
time
Dislikes:Timeconsumingadvice
ResearchMethods:Google,newsletters
What more information could help ?

9. Recommendation

10. Analytics journey curve for insurance
marketer
Sense & Respond Predict & Act
Raw
Data
Cleaned
Data
Standard
Reports
Ad Hoc
Reports &
OLAP
Generic
Predictive
Analytics
Predictive
Modeling
Optimization
What happened?
Why did it happen?
What will happen?
What is the best that could
happen?
CompetitiveAdvantage
Analytics Maturity
Unlocking data to move decision making from ‘sense & respond’ to ‘predict & act’
Current State

11. Poll
1. Where do you feel your organization is
currently placed in the analytics journey
curve?
a) Able to get Cleaned data but unable to generate
Standard reports
b) Able to generate Standard reports but unable to
perform OLAP
c) Able to perform OLAPbut unable to perform
‘Generic predictive analytics’
d) Able to perform Predictive modelling
e) Able to leverage Prescriptive modeling/Optimization

12. Analytics journey curve for insurance
marketer
Sense & Respond Predict & Act
Raw
Data
Cleaned
Data
Standard
Reports
Ad Hoc
Reports &
OLAP
Generic
Predictive
Analytics
Predictive
Modeling
Optimization
What happened?
Why did it happen?
What will happen?
What is the best that could
happen?
CompetitiveAdvantage
Analytics Maturity
Unlocking data to move decision making from ‘sense & respond’ to ‘predict & act’
Target
Current State

13. Step 1 : What happened?
Strategy & Assessment
Data Integration & Connectivity
Dashboard Implementation
Maintain & Enhance
 Track KPI tomeasure
business objectives
 Measure engagement at
granular level
 Trend analysis for various
segments provide deeper
insights into how segments
are performing over time for
various campaigns

14. Step 2: Why it happened?
 Hypothesis Testing
 Preparing business case
 ROIAnalysis
 Root CauseAnalysis
 Key DriverAnalysis
 E.g. Did design changes led
toadditional traffic on my
website?
Descriptive
Analytics
Diagnostic
Analytics

15. Data
Managem
ent
Big Data
Advanced
Analytics
Web Mobile Social Offline
Step 3: What next ?

16. Use Cases
 DistributionManagement
AglobalP&Cinsurerleveragedanalyticstounderstand
impactofitsagenciesoncancellationandrenewalpatterns
 evaluatesoverallperformanceofeachagencycategory
 Understandsvariabilityofagentswithinacategory
 Adjustsoriginationprocessandagentincentivestoincreasereturnon
investment
Outcome:30% fewercancellationsamongagencies
includedintheinitiative
 ABMmarketing
LeadingB2Binsurerleveragesanalyticsforitsaccount-
based-marketingcampaign
 Behavioralsegmentationofprospects
 Propensitymodelling
Outcome:40% higherresponseratefromthecampaign

17. Digital MarketingAnalytics
Analyze
•Traffic acquisition
insight
•Attribution analysis
•Multi-Channel Analytics
(Web, Social, Mobile)
Improve
•Search (organic,
inorganic)
•Personalization
•Re-Targeting
Optimize & Control
•Media Mix Optimization
•Ongoing A/B and MVT
testing
Capture &
Measure
•Strategy
•Measurement Planning
(KPI and Goal Definition)
•Tag Management

18. Connect with us
1-877-339-6264
www.edynamic.net
contact@edynamic.net
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youtube.com/1999dynamic
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