Customer Segmentation
Insurance companies spend a significant part of their budget on marketing, creating campaigns, retaining existing customers, as well as attracting new ones. However, in order to carry out a targeted campaign at the lowest possible cost, a key requirement is to identify the various customer profiles, understanding their behavioural characteristics, demographics, creditworthiness, consistency, etc.
The problem
Insurance companies spend a large part of their budget on marketing, creating campaigns, retaining existing customers as well as attracting new ones. However, to achieve the above at the lowest possible cost and with more precise targeting, a key requirement is to identify the various customer profiles, through understanding their behavioral characteristics, demographics, creditworthiness, consistency etc.
The purpose
Customer segmentation i.e the process of. dividing customers into groups based on common characteristics such as demographics or behaviors.
The solution
With the use of machine learning and artificial intelligence models that rely on historical customer data, a more meaningful and precise categorization of customers can be carried out, based on correlations and patterns that traditional techniques ignore.
The benefit
Segmenting the company’s customer base and analyzing the performance of these groupings can improve marketing strategies, sales and customer service efforts. Customer segmentation enables the insurance company to identify patterns in the acquisition of customers as well as patterns of engagement in different segments.
Indicative presentation of the data needed:
- The columns of the data 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
|
Gender |
Age |
Education |
Job type |
Vehicle age |
Traffic index |
N. of claims |
|
F |
19 |
MSc |
Hybrid |
10 |
45 |
2 |
|
M |
32 |
BSc |
Classic |
8 |
80 |
0 |
|
M |
26 |
BSc |
Classic |
5 |
110 |
2 |
|
F |
29 |
High School |
Remote |
7 |
98 |
0 |
System Prerequisites:
- Toolbox accepts xlsx or csv files.
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 results of the segmentation.
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.
