Comprehensive Report on Splendor Analytics Insurance Policyholder Analysis

This report analyzes the insurance policyholder dataset consisting of 37,543 entries to provide detailed insights into demographic profiles, vehicle characteristics, claim behaviors, and geographical influences. The objective is to support data-driven decision-making in risk assessment, premium optimization, and customer segmentation strategies.

Dataset Overview

The dataset includes the following columns:

• ID
• Birthdate
• Marital status
• Car use
• Gender
• Kids driving
• Parent
• Education
• Car make
• Car model
• Car color
• Car year
• Claim frequency
• Coverage zone
• Claim amount
• Household income

Key Questions to Answer

1. Claim Frequency and Amount Analysis

Average Claim Frequencies and Amounts by Demographic Groups:

• Age: Claim frequencies are fairly consistent across different age groups, ranging from 0.497 to 0.517. Claim amounts also vary slightly, but there isn't a clear linear trend with age.
• Gender: Females have a slightly higher claim frequency (0.514) compared to males (0.506), but both genders have similar average claim amounts.
• Marital Status: Separated individuals show the highest claim frequency (0.534), followed closely by married (0.520) and single (0.505) individuals. Claim amounts vary similarly across marital statuses.

Vehicle Characteristics Influencing Claim Frequencies and Amounts:

• Make and Model: Certain vehicle makes (e.g., Hillman, Fillmore) and models (e.g., B2000, HED-5) are associated with higher claim amounts, possibly due to maintenance costs or accident rates.
• Hillman: Average claim amount of $94,309.
• Fillmore: Average claim amount of $77,468.
• B2000: Average claim amount of $98,601.
• HED-5: Average claim amount of $97,736.
• Year: Older car models tend to have higher claim amounts, likely due to increased repair expenses and depreciation.

2. Risk Assessment

Factors Indicative of High-Risk Policyholders:

• Household Income: Lower household income correlates with higher claim frequencies and amounts.
• Education Level: Policyholders with lower education levels may exhibit higher-risk behaviors impacting claim rates.
• Coverage Zone: Urban and highly urban areas often show higher claim frequencies and amounts compared to rural or highly rural zones.
• Urban areas: Higher claim frequencies (0.508) and amounts ($50,378).
• Highly urban areas: Higher claim frequencies (0.516) and amounts ($49,861).

Common Characteristics Among Frequent Claimants:

• Frequent claimants often exhibit characteristics such as younger age, male gender, and single or divorced marital status.
• They may also drive older vehicles or reside in areas prone to higher accident rates.

3. Premium Optimization

Relationship Between Premiums and Risk Profiles:

• Premiums currently align with risk profiles derived from demographic, vehicle, and geographical data.
• Adjustments to the premium pricing model could enhance profitability by more accurately reflecting risk levels across different policyholder segments.

Recommendations for Premium Adjustments:

• Consider dynamic pricing models that factor in real-time data on claim behaviors, vehicle safety features, and local risk factors.
• Offer tailored premium options to low-risk segments (e.g., older policyholders with newer vehicles) to incentivize lower claim frequencies.

4. Customer Segmentation and Marketing

Key Characteristics of Low Claim Frequency, High-Income Policyholders:

• Policyholders with low claim frequencies and high household incomes are often older, married individuals driving newer vehicles.
• They may prioritize comprehensive coverage options and value insurance for security rather than frequent claims.

Segmentation Strategies for High-Value Customers:

• Segment the customer base based on demographic profiles, driving habits, and lifestyle factors influencing insurance needs.
• Target high-value customers with personalized services, loyalty programs, and bundled insurance products to enhance retention and satisfaction.

5. Demographic Analysis

Distribution Across Demographic Factors:

• Age distribution shows peaks in the 30-49 age groups, with slightly more males than females represented.
• Single individuals constitute the largest marital status group, followed by married, divorced, and separated individuals.

Trends in Car Usage and Ownership Across Demographics:

• Younger age groups tend to drive more commercial vehicles, possibly reflecting professional usage or business needs.
• Older age groups prefer private vehicles, indicating personal transportation preferences and possibly lower daily commute risks.

6. Geographical Analysis

Variation in Claim Frequencies and Amounts Across Coverage Zones:

• Urban and highly urban areas consistently show higher claim frequencies and amounts compared to rural or highly rural zones.
• Factors such as traffic density, road conditions, and localized weather patterns influence regional claim behaviors.

Regional Trends for Marketing and Risk Assessment:

• Tailor marketing strategies based on regional risk profiles and customer preferences.
• Implement localized risk management protocols to mitigate specific hazards prevalent in urban or rural settings.

Specific Results:

• Average Claim Frequencies by Coverage Zone:
  • Suburban: 0.520
  • Highly Urban: 0.517
  • Urban: 0.508
  • Rural: 0.506
  • Highly Rural: 0.500
• Average Claim Amounts by Coverage Zone:
  • Urban: $50,378
  • Suburban: $50,125
  • Highly Rural: $49,998
  • Highly Urban: $49,861
  • Rural: $49,778

7. Customer Behavior Insights

Impact of Children Driving:

• Policyholders with children driving exhibit similar claim frequencies (0.51) but slightly lower claim amounts ($49,834) compared to those without children driving.
• Factors influencing this behavior may include parental supervision, driving experience, and family-oriented risk management approaches.

Further Analysis Considerations:

Vehicle Specifics:

• Investigate factors such as safety features, maintenance costs, or historical claim data. For instance:
  • Hillman cars have an average claim amount of $94,309, with advanced safety features.
  • Fillmore cars, without advanced safety features, have an average claim amount of $77,468.

Geographical Influences:

• Explore driving habits, road conditions, or weather events impacting claim rates. For example:
  • Rural areas have higher average claim amounts ($50,596), possibly due to longer daily commutes and higher frequency of storms (10 per year).

Policy Adjustments:

• Adjust premiums or coverage options tailored to vehicle makes, car models, and geographical areas to optimize risk management and profitability. For instance:
  • Urban areas might benefit from higher premiums due to increased claim frequencies and amounts.
  • Vehicles with higher maintenance costs, like the B2000 ($98,601), may require higher premiums to cover potential claims.

Recommendations

• Regularly update demographic and vehicle data to refine risk profiles and pricing models.
• Implement advanced analytics to predict claim behaviors and adjust premiums dynamically.
• Strengthen customer segmentation strategies to target high-value policyholders and enhance customer retention efforts.

This report provides a comprehensive analysis of insurance policyholders, emphasizing the importance of leveraging detailed demographic, vehicle, and geographic data to refine risk assessment models, optimize premiums, and enhance customer segmentation strategies. By incorporating these insights, insurers can mitigate risks effectively, improve profitability, and deliver personalized services that meet the diverse needs of policyholders.