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Analysis of Internet Purchasing Behavior

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

Experience the Pinnacle Difference!Kevin LevittSenior Vice PresidentcomScore, Inc. Roosevelt Mosley, FCAS, MAAAPrincipalPinnacle Actuarial Resources Nick KuceraConsultantPinnacle Actuarial Resources

Discussion TopicsCurrent State of Insurance MarketingCustomer response analysescomScore background and datacomScore background and dataDescription of researchCharacteristics of shoppersModel development

Analysis of quotes submittedAnalysis of policies purchasedAnalysis of policies purchased

Impact of price on conversionUnstructured dataAdditional Research

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Current State of Insurance Marketing

Explosion in the investment that insurers are making in marketingMany 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, convenienceDifferent elements are important to different market segments

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Customer Response Analyses

Marketing Effort Quoting A l iQuoteSale

AnalysisConversion AnalysisLapse / Cancelation AnalysisRenewal QuoteRenewal

AnalysisRetention Analysis

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Customer Response Analyses

Quoting analysis: analysis of the likelihood of a prospective insured obtaining an insurance quote from youyouConversion analysis: analysis of the likelihood of a prospective insured that has received a quote purchasing insurance from youLapse/Cancelation analysis: likelihood of an insured not lapsing or canceling the policy mid-termR i l i l i f h lik lih d fRetention 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 parameterizationNew territory – learning curveNew territory learning curvePriorityInternal data availabilityInternal data applicabilityAvailability of price change dataAvailability of price change dataMeasuring market competitivenessApplications

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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 8

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What We Do….We provide digital marketing intelligence that helps our customers make better-informed business decisions and implement more effective digital business strategiesp g gWe 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 countriesWe also have permission to:

Survey panelistsSurvey panelistsMatch to third-party databasesAppend offline data

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The Trusted Source for Digital Intelligence Across Vertical Markets9 out of the top 10INVESTMENT BANKS 9 out of the top 10AUTO INSURERS

47 out of the top 50 ONLINE PROPERTIES45 out of the top 50

4 out of the top 4WIRELESS CARRIERS 9 out of the top 10PHARMACEUTICALCOMPANIES9 out of the top 10

9 out of the top 10INTERNET SERVICEPROVIDERS

45 out of the top 50ADVERTISING AGENCIES9 out of the top 10MAJOR MEDIA COMPANIES

9 p 0CONSUMER FINANCECOMPANIES9 out of the top 10CPG COMPANIESV0910

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Auto Insurance Quote Detail

Data is captured from what panelists see using scraping technologyscraping technology

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Auto Insurance Quote DetailData for 5 Top Auto Insurance Company Sites– Quote

• ZIP code• Bodily injury liability limits

– Drivers• Age• GenderBodily 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 hPaid searchNatural searchWebmail sitesOther referredNon-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 DescriptionMarketing Effort

Auto Insurance Website VisitorQuote 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 quoteQuote

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 54660080.0%

Model - Quote SubmittedMeans of Entry

46.8%32.6%

463546

411300400500

30.0%40.0%50.0%60.0%70.0% F

requency

of

Submitted

per

Number

of

Vis

16

9.7% 5.4% 5.5%0100200

0.0%10.0%20.0%

Natural Search Non-Referred Other Referred Sponsored Search Webmail

Quotes

1,000

sitors

Means of EntryExposure 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%

Purc

hase

Fre

quen

cy (p

er 1

,000

)

ure

(Ses

sion

s w

ith c

ompl

eted

quo

te)

-

20.00

0.0%

5.0%

Weekday M

orning

Weekday D

ay (9-5)

Weekday Evening

Weekday Late (10PM

+)

Weekend N

ight

Saturday

Sunday

Expo

s

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%

Purc

hase

Fre

quen

cy (p

er 1

,000

)

re (S

essi

ons

with

com

plet

ed q

uote

)

-

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

Expo

sur

Age Driver 1

18

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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%

urch

ase

Freq

uenc

y (p

er 1

,000

)

re (S

essi

ons

with

com

plet

ed q

uote

)

-

10.00

20.00

0.0%

10.0%

1 / 1

1 / 2+

2 / 1

2 / 2

2 / 3+

3+ / < 3

3+ / 3+

Pu

Expo

sur

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 kDecision TreeNetwork Regression AnalysisEnsemble

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Quotes Submitted Analysis

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Submitted Likelihood Model – Decision Tree

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Decision Tree – English RulesLow Estimate

IF total_pages < 11.5AND total ssl page < 9 5AND total_ssl_page < 9.5Quote Initiated: 0.4%

High EstimateIF 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 SEARCHNATURAL SEARCH AND total_pages < 15.5Quote Initiated: 71.4%

Quote Submitted – Number of Prior Visits

1 0001.200

Re

Number of Prior Site Visits

1.0000.889

0.7790.668

0.5570 4000.6000.8001.000l

ative

Likeli

Submitting

Q

260.0000.2000.400

0 1 2 3 4

hood

of

uote

Number of Prior VisitsRelative 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%

0.400.600.801.001.20

Expo

sure

Per

cent

age

Rela

tivity

20%0%2%4%

0.000.20

Age Driver 1Sessions with Completed QuoteModeled RelativityOne 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%

1.001.502.00

Expo

sure

Per

cent

age

Rela

tivity

0%5%10%0.000.50

Primary Driver EducationSessions with Completed QuoteModeled 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%

0 200.400.600.801.00

Expo

sure

Per

cent

age

Rela

tivity

0%10%

0.000.20

Percent Final Quoted Premium greater than Minimum Quoted Premium

Sessions with Completed QuoteModeled Relativity30

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Price Sensitivity – One Month

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Regression Preliminary Results

560%

1 151.20 Model: Purchase made during session

115%

100% 97%10%20%30%40%50%

0.951.001.051.101.15

Expo

sure

Per

cent

age

Rela

tivity

0%10%

0.850.90

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

Sessions with Completed QuoteModeled Relativity

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Unstructured Data

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Search Phrase Analysis

CategoriesSpecific companies

ProcessRemove punctuationSpecific companies

“Quotes” – auto, homeowners, life, insuranceValue – cheap, affordable, etc.Specific agents

Remove punctuation and non-characters [|./!#?*$%()-@]Parse phrase into wordsSummarize the frequency of each wordCorrect for misspellingsp g

Circumstance - teenOther – TurboTax, hydrogen fuel cell

p gBuild cluster analysis to determine key word associations

34Insuranced 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 DescriptionMarketing Effort

Auto Insurance Website VisitorQuote 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 quoteQuote

Quote Submitted Policy Bind Of those that actually submit the quote, how many complete the purchase?Sale

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Data Considerations

Voluntary panelOnly captures internet shopping trendsOnly captures internet shopping trendsDoes not marry online shopping with offline shoppingMay not track same user on separate computersp

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Potential Applications1. 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 tprocess to ensure that the customer does not drop out

2. For potential insureds with a higher than average likelihood of purchase, insurance companies would be able to market more aggressively to these potential insureds

f l d h h l h3. 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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