Discussion Topics Current State of Insurance Marketing Customer - - PDF document

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Analysis of Internet Purchasing Behavior

October 3, 2011 CAS I F S i CAS In Focus Seminar

Experience the Pinnacle Difference! Kevin Levitt Senior Vice President comScore, Inc. Roosevelt Mosley, FCAS, MAAA Principal Pinnacle Actuarial Resources Nick Kucera Consultant Pinnacle Actuarial Resources

Discussion Topics

Current State of Insurance Marketing Customer response analyses comScore background and data comScore background and data Description of research Characteristics of shoppers Model development

Analysis of quotes submitted

Analysis of policies purchased

Analysis of policies purchased

Impact of price on conversion Unstructured data Additional Research

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Current State of Insurance Marketing

Explosion in the investment that insurers are making in marketing Many different advertising mediums are being used

Traditional, internet, social media, etc.

Has led to the understanding that customer behavior is about more than price

Customer service, reputation, convenience

Different elements are important to different market segments

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Customer Response Analyses

Marketing Effort

Quoting A l i

Quote

Sale

Analysis Conversion Analysis Lapse / Cancelation Analysis

Renewal Quote Renewal

Analysis Retention Analysis

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Customer Response Analyses

Quoting analysis: analysis of the likelihood of a prospective insured obtaining an insurance quote from you you Conversion analysis: analysis of the likelihood of a prospective insured that has received a quote purchasing insurance from you Lapse/Cancelation analysis: likelihood of an insured not lapsing or canceling the policy mid-term R i l i l i f h lik lih d f Retention analysis: analysis of the likelihood of a current insured renewing with you

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Customer Response Modeling – Challenges for Insurance Companies Model structure and parameterization New territory – learning curve New territory learning curve Priority Internal data availability Internal data applicability Availability of price change data Availability of price change data Measuring market competitiveness Applications

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comScore Background & Data

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comScore is a Global Leader in Measuring the Digital World

NASDAQ SCOR Clients 1700+ worldwide Employees 900+ Headquarters Reston, VA Global Coverage 170+ countries under measurement; 43 markets reported Local Presence 32 locations in 23 countries

• V0910

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What We Do….

We provide digital marketing intelligence that helps

• ur customers make better-informed business decisions

and implement more effective digital business strategies p g g We measure the continuous online activity of 1 million people in the U.S. who have granted us explicit permission to confidentially measure their Internet usage patterns. Our consumer panel is a representative cross-section of the U.S. population, worldwide regions and individual countries We also have permission to:

Survey panelists Survey panelists Match to third-party databases Append offline data 9

The Trusted Source for Digital Intelligence Across Vertical Markets

9 out of the top 10

INVESTMENT BANKS

9 out of the top 10

AUTO INSURERS

47 out of the top 50

ONLINE PROPERTIES

45 out of the top 50 4 out of the top 4

WIRELESS CARRIERS

9 out of the top 10

PHARMACEUTICAL COMPANIES

9 out of the top 10 9 out of the top 10

INTERNET SERVICE PROVIDERS

45 out of the top 50

ADVERTISING AGENCIES

9 out of the top 10

MAJOR MEDIA COMPANIES

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CONSUMER FINANCE COMPANIES

9 out of the top 10

CPG COMPANIES

V0910

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Auto Insurance Quote Detail

Data is captured from what panelists see using scraping technology scraping technology

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Auto Insurance Quote Detail

Data for 5 Top Auto Insurance Company Sites

– Quote

• ZIP code
• Bodily injury liability limits

– Drivers

• Age
• Gender

Bodily injury liability limits

• Coverage package
• Premium quoted
• Final Purchased Premium
• Company name
• Homeownership
• Whether SSN entered
• Primary driver education
• Gender
• Marital Status
• Industry/Occupation

– Vehicles

• Vehicle year/make/model/type
• Vehicle use
• Annual mileage
• Comprehensive deductibles
• Current Insurance Information
• Whether currently insured
• Length of gap in coverage
• Length of current carrier
• Length continuously insured
• Prior BI Limit
• Collision deductibles

– Incidents

• Incident Type
• Incident Description

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Auto Insurance Quote Detail Data – Source of Traffic Data

Source of traffic is broken out into the following categories:

P id h

Paid search Natural search Webmail sites Other referred Non-referred

For search, we know the search engine, click type (paid/natural), and the search phrase For webmail sites and other referred, we know the referring site

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Pinnacle & comScore Research

Population Target Description Marketing Effort Auto Insurance Website Visitor Quote Initiated

Of those that visit the website, people with what characteristics are more likely to start the quote process?

Quote Initiated Quote Submitted

Of those that initiate the quote process, what is the likelihood that they complete it?

Q t P li Bi d

Of those that actually submit the quote

Quote Quote Submitted Policy Bind

Of those that actually submit the quote, how many complete the purchase?

Sale

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Characteristics of Shoppers

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Data Summary – Means of Entry

570 559 546 600 80.0%

Model - Quote Submitted Means of Entry

46.8% 32.6% 463 546 411 300 400 500 30.0% 40.0% 50.0% 60.0% 70.0% F r e q u e n c y

• f

S u b m i t t e d p e r N u m b e r

• f

V i s

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9.7% 5.4% 5.5% 100 200 0.0% 10.0% 20.0% Natural Search Non-Referred Other Referred Sponsored Search Webmail Q u

• t

e s 1 , s i t

• r

s Means of Entry Exposure Percentage Frequency per 1000

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Data Summary – Session Day/Time

120.00 30.0%

Model: Purchase Made During Session

40.00 60.00 80.00 100.00 10.0% 15.0% 20.0% 25.0% Purchase Frequency (per 1,000) ure (Sessions with completed quote)

• 20.00

0.0% 5.0% Weekday Morning Weekday Day (9-5) Weekday Evening Weekday Late (10PM+) Weekend Night Saturday Sunday Expos Session Day/Time

Data Summary – Driver1 Age

100.00 18.0%

Model: Purchase Made During Session

20 00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% Purchase Frequency (per 1,000) re (Sessions with completed quote)

• 10.00

20.00 0.0% 2.0% 4.0% <=18 19 20 21 22 23 24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+ P Exposur Age Driver 1

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Data Summary – Number Vehicles/Drivers

90.00 70.0%

Model: Purchase Made During Session

20 00 30.00 40.00 50.00 60.00 70.00 80.00 20.0% 30.0% 40.0% 50.0% 60.0% urchase Frequency (per 1,000) re (Sessions with completed quote)

• 10.00

20.00 0.0% 10.0% 1 / 1 1 / 2+ 2 / 1 2 / 2 2 / 3+ 3+ / < 3 3+ / 3+ Pu Exposur Policy Number Vehicles / Drivers

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Data Summary – Bodily Injury Limit

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Model Development

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Modeling Techniques

Neural N k Decision Tree Network Regression Analysis

Ensemble

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Quotes Submitted Analysis

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Submitted Likelihood Model – Decision Tree

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Decision Tree – English Rules

• Low Estimate

IF total_pages < 11.5

AND total ssl page < 9 5

AND total_ssl_page < 9.5 Quote Initiated: 0.4%

High Estimate

• IF aig EQUALS 1
• AND 11.5 <= total_ssl_page
• AND 4.5 <= visit_time
• AND means_of_entry IS ONE OF: WEBMAIL NON-REFERRED SPONSORED SEAR

NATURAL SEARCH NATURAL SEARCH

• AND total_pages < 15.5
• Quote Initiated: 71.4%

Quote Submitted – Number of Prior Visits

1 000 1.200 R e

Number of Prior Site Visits

1.000 0.889 0.779 0.668 0.557 0 400 0.600 0.800 1.000 l a t i v e L i k e l i S u b m i t t i n g Q

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0.000 0.200 0.400 1 2 3 4 h

• d
• f

u

• t

e Number of Prior Visits Relative Likelihood of Submitting Quote

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Policy Purchased Analysis

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Age of Driver

131% 16% 18% 1.40

Model: Purchase made during session

41% 47% 67% 69% 76% 72% 100% 117% 111% 118% 104% 104% 103% 62% 4% 6% 8% 10% 12% 14% 0.40 0.60 0.80 1.00 1.20

Exposure Percentage

Relativity 20% 0% 2% 4% 0.00 0.20

Age Driver 1

Sessions with Completed Quote Modeled Relativity One Way Relativity

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Education

45% 50% 2.50

Model: Purchase made during session

70% 100% 126% 131% 146% 155% 196% 149% 15% 20% 25% 30% 35% 40% 1.00 1.50 2.00

Exposure Percentage

Relativity 0% 5% 10% 0.00 0.50

Primary Driver Education

Sessions with Completed Quote Modeled Relativity

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Price Sensitivity – One Session

106% 60% 1.20

Model: Purchase made during session

100% 76% 79% 71% 52% 76% 10% 20% 30% 40% 50% 0 20 0.40 0.60 0.80 1.00

Exposure Percentage

Relativity 0% 10% 0.00 0.20

Percent Final Quoted Premium greater than Minimum Quoted Premium

Sessions with Completed Quote Modeled Relativity

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Price Sensitivity – One Month

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Regression Preliminary Results

5 60% 1 15 1.20

Model: Purchase made during session

115% 100% 97% 10% 20% 30% 40% 50% 0.95 1.00 1.05 1.10 1.15

Exposure Percentage

Relativity 0% 10% 0.85 0.90

Did user come to insurer’s site from another insurer’s site?

Sessions with Completed Quote Modeled Relativity

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Unstructured Data

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Search Phrase Analysis

Categories

Specific companies

Process

Remove punctuation Specific companies “Quotes” – auto,

homeowners, life, insurance

Value – cheap,

affordable, etc.

Specific agents Remove punctuation

and non-characters [|./!#?*$%()-@]

Parse phrase into words Summarize the

frequency of each word

Correct for misspellings

p g

Circumstance - teen Other – TurboTax,

hydrogen fuel cell p g

Build cluster analysis to

determine key word associations

34 Insuranced insurance: insurance0 insurance3 insurances insurancer insurancej insurancel insuranceg insurrance inssurance insurannce insurancce iinsurance innsurance insuraance isnurance insuarnce inusrance insuranceco

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Cluster Text Analysis

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Summary & Additional Research

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Cumulative Lift

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Lift Chart

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Additional Research

Population Target Description Marketing Effort Auto Insurance Website Visitor Quote Initiated

Of those that visit the website, people with what characteristics are more likely to start the quote process?

Quote Initiated Quote Submitted

Of those that initiate the quote process, what is the likelihood that they complete it?

Q t P li Bi d

Of those that actually submit the quote

Quote Quote Submitted Policy Bind

Of those that actually submit the quote, how many complete the purchase?

Sale

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Data Considerations

Voluntary panel Only captures internet shopping trends Only captures internet shopping trends Does not marry online shopping with offline shopping May not track same user on separate computers p

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Potential Applications

1. For potential insureds with a higher likelihood of purchasing a policy, steps can be taken within the quote and purchase t th t th t d t d t process to ensure that the customer does not drop out 2. For potential insureds with a higher than average likelihood

• f purchase, insurance companies would be able to market

more aggressively to these potential insureds f l d h h l h 3. For segments of potential insureds that have a lower than average likelihood of purchasing a policy, an insurance company can identify and investigate where these lower than average likelihoods are and try to increase the likelihood of purchase

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Questions? Questions?

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