Challenge
A large insurance company with a wide range of products (life, property, motor, and travel insurance) noticed that agents often did not fully understand customer needs. The goal was to increase insurance sales and improve customer satisfaction.
Action
- Thorough understanding of customer risk
The company checked the customer's age, gender, whether they had a family and children, and what kind of job they did (e.g., desk work or physical labor). It looked at whether the customer played sports (especially extreme sports), what hobbies they had, whether they owned a house, and whether they drove a car. If the customer already had policies and agreed to share their data, the system checked their previous insurance and claims. - Personalized recommendations
Based on customer knowledge, the system suggested specific insurance policies or sets of policies. For example:
- A young couple with children and a mortgage – life insurance with additional protection in case of job loss and home insurance.
- A person who is very active in sports – accident insurance with a higher compensation amount.
- Drivers with a long accident-free history – attractive car insurance packages (third-party liability/comprehensive) with cool extras.
The iPresso system selected appropriate, pre-prepared content which, based on customer behavior, presented benefits tailored to their situation.
- Sending personalized offers through various channels
The company sent automated emails with tailored offers and simple advice (e.g., “Why is life insurance important for your family?”). Customers also received mobile push notifications about new offers and reminders about the upcoming end of their current policy with a proposal to renew it on better terms. Thanks to a preliminary risk analysis, insurance offers were presented very clearly, with the possibility of easily comparing different options.
Results
- Sales of key insurance products increased by 20%.
- Customer satisfaction improved by 16%. In NPS surveys, customers indicated that personalized recommendations were tailored to their needs.
- 13% fewer customers declined offers because the proposals were more relevant to them
