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Customer Lifetime Value
- 1. Customer Lifetime Value National Credit Union Jenna Toler Ryan Murphy
- 2. The Customer Lifetime Value (CLV) metric is important for businesses across many industries. It is a metric that allows companies to save money, market more strategically, and drive greater amounts of sales — all because it can inform an organization of the customers that have the highest potential. Context
- 3. While the calculation for CLV is relatively straightforward in many contexts, it can be difficult to determine for financial institutions such as banks or credit unions. Take for example a savings account: banks must pay interest to the depositor, making it a cost; banks also rely on these savings to use when distributing loans, which generate revenues for the institution. Key Problem
- 4. The data being used in this project measure the personal financial activity of over 600,000 customers with data spanning six months from March through August. Data Set
- 5. Data Set
- 6. Data Set
- 7. Questions Question 1. How valuable is the customer right now? Question 2. How valuable will the customer be for the remainder of the relationship with the institution? Question 3. How can we describe the highest-valued customers and subsequently pursue them?
- 8. How valuable is the customer right now? Current Customer Value to the Bank ● Difference in generated revenues and incurred costs ○ Spread from interest-bearing accounts ○ Interest earned from loans, credit cards, and debit card transactions ○ Costs of services such as branch visits or using ATMs ● Values are annualized
- 9. How valuable will the customer be? Customer Lifetime Value ● Forecasted estimate of Current Customer Value to the Bank ○ Estimated 92% retention rate ○ 6% discount rate ○ Assumed death at age 80 ○ Customer acquisition cost
- 10. How do we describe these customers? Lifetime Value Groups ● Two-step clustering into five groups ○ Cluster #1 comprises the least profitable customers including all negative-CLV ○ Cluster #5 comprises the most profitable customers ■ 75% correlation between totals in mortgage interest and CLV
- 11. CLV by Age Group
- 12. CLV by Origin Group
- 13. We recommend that Navy Federal Credit Union do the following: ● Utilize RFM approach ○ Adjusted RAND Index ● Evaluate additional variables Recommendations
- 14. Suggested Variables ● Greater timeline of data ● Specific age of customer ● Geographic area ● Terms of mortgages ● Customer acquisition costs ● Retention rate ● Default rates and refinancing on loans ● Number of dependents Additional Variables
- 15. Customer Lifetime Value Navy Federal Credit Union Jenna Toler Ryan Murphy
Editor's Notes
- Presentation notes.
- Presentation notes.
- Presentation notes. When will they pay off loans to determine decrease of revenue generation Churn is how many people you lose. Make a variable that says that? Or we can estimate an age when usefulness runs out. Determine profitability by category—when have mortgages and auto loans and educational loans been paid off?
- Presentation notes.
- Take into consideration whether this is reflective of the entire customer base
- Presentation notes.
- Presentation notes.
- Not including interest still to be paid.
- The 92% retention rate is based on industry averages across both commercial banking and related industries such as insurance. 6% discount rate is based on the 10-year treasury yield with a built-in risk-premium. According to the World Health Organization, American males die at about age 79 while females die at about age 81.
- The 92% retention rate is based on industry averages across both commercial banking and related industries such as insurance. 6% discount rate is based on the 10-year treasury yield with a built-in risk-premium. According to the World Health Organization, American males die at about age 79 while females die at about age 81.
- Presentation notes.
- Presentation notes.
- Specific age of the customer to provide a more granular analysis of … Number of kids Greater timeline of data, especially because consumer spending trends differ at various points of the year. Geographic area, taking into consideration greater property values by location. Customer acquisition costs, for a more complete post–CLV analysis to make cost-benefit determination. Retention rate for churn analysis (predictions of future churn)
- Specific age of the customer to provide a more granular analysis of … Greater timeline of data, especially because consumer spending trends differ at various points of the year. Geographic area, taking into consideration greater property values by location. Customer acquisition costs, for a more complete post–CLV analysis to make cost-benefit determination. Retention rate for churn analysis (predictions of future churn) Number of kids
- Presentation notes. What kinds of marketing implications are there for this sort of analytics? Supplement: things to think about with these implications? RFM over CLV “You end up with more questions than answers.”