Up-sell prediction
For insurance companies, the driver of growth is the sale of products, however the preferred way is through the existing clientele, as finding new customers is a much more expensive process. Therefore, companies rely on the data they have in order to understand customer behaviour and make more targeted product promotions.
Examples: An insurance company wants to identify which insurance customers are likely to spend more by purchasing an upgraded or premium version of the product they originally intended to buy.
The problem
For insurance companies, the driver of growth is the sale of products, however the preferred way is through the existing clientele, as finding new customers is a much more expensive process. Therefore, insurance companies rely on existing data in order to understand customer behavior, make more targeted product promotions and offers, and extract the maximum possible capital that a customer can allocate.
The purpose
Predict if an existing customer will be interested in a higher-end version of a policy.
The solution
Data analytics, machine learning and artificial intelligence are excellent tools in the targeting process, that is, in the process of identifying existing customers who, with a high probability, would respond positively to a proposal of purchasing a higher-end version of the policy they are interested in.
The benefit
Applying the above methods in the process of up-selling will result in an increased ROI, acceleration of the speed of the process in question as well as in the reduction of costs related mainly to the required human resources and communication costs.
Indicative presentation of the data needed:
- The first column of the data file should contain the state of interest in purchasing a new insurance product (e.g. 0: Not interested, 1: Interested) from historical data. The first column of the data file must contain the insurance product Upgrade status (e.g. 0: Upgrade, 1: No upgrade) from historical data.
- The following columns of the file must contain the values of historically recorded characteristics such as: Age, Gender, Education, Marital Status, Employment Status, Income, Area of Residence, Sales Channel, Number of Insurance Policies, Policy Type, Premium, Customer Lifetime Value, Number of insurance claims, Amount of insurance claims, Number of complaints, Insurance premium, Application data, Policy data, etc.
Table 1. Sample table of user input data
|
Response |
Gender |
Age |
Education |
Last contact |
Car owner |
N. of policies |
|
Yes |
F |
19 |
MSc |
14 |
Yes |
3 |
|
No |
M |
32 |
BSc |
78 |
Yes |
1 |
|
No |
F |
26 |
BSc |
98 |
No |
1 |
|
Yes |
F |
29 |
High School |
20 |
Yes |
2 |
|
|
M |
32 |
BSc |
9 |
Yes |
1 |
System Prerequisites:
- Toolbox accepts xlsx or csv files.
- The first column should contain the data from the target variable (e.g. “Response for product upgrade”), the creation of which results from historical customer data.
- The target variable should not contain missing values.
- In case the user wants to make a forecast, he must enter the data of the customers for whom he needs the forecast, in the same file (excel or csv) as the historical data, provided that the first cell which is the target variable will not contain values (see Table 1).
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.
