Customer Lifetime Value prediction
Customer Lifetime Value (CLV) prediction is an important problem in Insurance where an accurate estimate of future value allows insurers to effectively allocate marketing spend, identify and nurture high value customers and mitigate exposure to losses. Higher CLV means each customer generates more revenue for your business without you having to invest anything extra, which also means your company has more money to spend on acquiring new customers.
Examples: An insurance agent wants to identify the expected CLTV of the new customers based on historical customer profiles. CLTV prediction process will be used to predict his future values based on his characteristics and other provided info.
The problem
For insurance companies, the main driver of growth is product sales, however the preferred way is through existing clientele, as finding new customers is a much more expensive process. Therefore, companies rely on the historical data in their possession to understand customer behavior, make more targeted product promotions, and thereby extract the maximum possible capital that a customer can allocate. A critical factor in identifying a customer's profile is their net value to the company (Customer Lifetime Value - CLV), which takes into account the difference between the total amount of revenue received by the company from the customer and the costs incurred by him during their relationship.
The purpose
Predict customer lifetime value.
The solution
By using machine learning and artificial intelligence models based on historical customer data, a safe prediction of a customer's future net worth to the insurance company can be made.
The benefit
Forecasting the amount of money a customer will potentially cost the insurance company as well as the profit it can bring to the insurance company is considered necessary to give a clear picture of the amount of money worth spending to acquire, retain and service of.
Indicative presentation of the data needed:
- The first column of the data file should contain the net worth of customers based on historical data.
- The following columns of the file should contain the values of historically recorded characteristics such as: Age, Gender, Marital Status, Employment Status, Income, Area of Residence, Sales Channel, Premium, Number of Policies, Number of Complaints, Policy Type, Months since last claim, Education, Customer net worth, Months since the start of the policy, Number of insurance claims, Amount of insurance claims, Application data, Policy data, etc.
Table 1. Indicative table of user input data
|
CLV |
Gender |
Age |
Education |
N. of complaints |
Claim amount |
N. of Claims |
|
2500 |
F |
19 |
MSc |
0 |
1000 |
1 |
|
1300 |
M |
32 |
BSc |
0 |
0 |
0 |
|
1000 |
M |
26 |
BSc |
2 |
2000 |
1 |
|
800 |
F |
29 |
High School |
1 |
0 |
0 |
|
|
F |
28 |
High School |
1 |
0 |
2 |
System Prerequisites:
- Toolbox accepts xlsx or csv files.
- The first column should contain the data from the target variable (e.g. “Customer Lifetime Value-CLV”), the creation of which results from historical customer data.
- The target variable should not contain missing values.
Output:
After the data has been entered by the user and after a short period of time for its automatic analysis:
- A report of the results and the statistical methods used is extracted from the system.
- An excel is exported with the forecast results in case the user wants to make a forecast (see Prerequisites 4)
Note: For any clarification you need regarding the content of the use case or any information related to the collection or validity of your data please contact us.
