An Approach to Calculate Customer Lifetime Value Team

An Approach to Calculate Customer Lifetime Value Team Mathletes: Luo Wang, Xiaoyi Yang, Tae Park, Junyi Yang

Agenda • Introduce Customer Lifetime Value • Potential Factors： • Birth Order • Self-employment • Single parent • Mortality Salience(MS) • Conceptual Data Model • Limitation and Recommendations • Conclusion • Questions

Introduce Customer Lifetime Value (CLV) • The drawback of Embedded Value (EV) model • Definition • Importance • Beyond traditional factors

Birth Order • Definition • Firstborns tend to be more conscientious

Birth Order

Birth Order Assumptions: Social Security Actuarial Life Table, 6% interest rate, 10% birth order factor

Self-employment • Negative correlation with CLV • Self-employed people show risk -taking personality.

Self-employment Health Risk The self-employed exhibit more high risk health behaviors. The selfemployed are more likely to smoke, and compared to salaried workers on average they have higher body mass indices.

Single Parents • 50 percent of marriage end up divorce. • Unmarried women accounted for 41 percent of births. • Living alone makes up the second largest American household type. • 80 percent of single parents are female. • Single parent need life insurance the most. • Term life insurance is affordable. • Help close relatives financially due to closer relationship than married couple.

Single Parent ● 70% of single parents with children living at home don’t carry life insurance. ● Educate single parents. ● 50 percent divorce rate will lead to many single parents every year. ● Life insurance companies keep track of married couple with children who recently divorced. ● High retention rate.

Mortality Salience (MS) ● Definition of Mortality Salience (MS) ● Relationship: MS & Customer Lifetime Value ○ “Financial Mortality” ● Mortality Salience Measurement ○ Self-Esteem ■ Social Network Usage

Conceptual Data Model Type A Customer (existing customer)

Conceptual Data Model Type A Customer (existing customer)

Conceptual Data Model Type A Customer (existing customer)

Conceptual Data Model: Enterprise Data Warehouse

Conceptual Data Model Type A Customer (existing customer)

Conceptual Data Model Type A Customer (existing customer)

Conceptual Data Model Type B Customer (Potential Customer)

Conceptual Data Model Graduating! Life Insurance? • Type B Customer Example: • Prudential: life products: • Term Essential® • Term Elite® • VUL Protector® • Luo’s quote: Term Essential® • Type B CLV Model Results: Term Elite® , VUL Protector®

Conclusion Limitation and Recommendations Lacking of practical data. ❖ Data available: data industries or data sources such as IBM Insurance Warehouse, Federated Data Warehouse, U. S Census Bureau and Society of Actuary CLV potential factors and the impacts of these factors vary. The model needs modified frequently to maintain its efficiency. The conceptual data schema can be created by De. Zign for Databases or Access. Discussion

Questions Luo Wang: luo. wang@uconn. edu Xiaoyi Yang: xiaoyi. yang@uconn. edu Tae Park: tae. park@uconn. edu Junyi Yang: junyi. yang@uconn. edu