Speed, accuracy and personalization affect insurance policy prices

In today’s world, where patience is an increasingly scarce resource and customers expect instant answers with minimal effort, quick pricing has become a crucial element of the insurance buying process.

Before customers begin discussing the details of a policy with an insurance agent, they first want to know the answer to the most important question: How much will it cost?

On the other hand, the agent wants to provide this information as quickly as possible so that they can focus on discussing the key aspects of the offer, such as coverage, additional options or explaining the terms of the policy. Without quick pricing, the entire process becomes complicated, diverting the agent’s attention from the customer’s needs and causing frustration from using unintuitive tools.

Key elements of quick pricing
To ensure a quick and efficient pricing process, it is necessary to organize the sales route into appropriate stages. In discussions with representatives of insurance companies, I often use a three-step division:

1. Minimum data collection: In this step, it is important to focus only on the information that is essential for estimating the policy costs, such as the type of insurance, personal details of the policy holder, scope of coverage, duration of insurance, payment preferences.
2. Presentation of the quote: After collecting the necessary data, the system must quickly generate and present the quote so that the customer immediately knows the approximate cost of the insurance.
3. Complete data collection: The final stage is to collect all the remaining information necessary to bind the policy, which, however, does not affect the premium.

Such a three-step process not only speeds up the sales cycle, but also allows the agent to focus on the client’s key needs instead of struggling with cumbersome tools. Importantly, this does not exhaust the subject of price quickly.
Integration of external data sources
Another key step in optimizing the rapid pricing process is integrating the system with multiple data sources. Typically, the pricing process begins with identifying the customer and the insured item, for example, using a social security number, vehicle registration number, property address, or company registration number. This allows the insurance company’s internal databases to be quickly checked, and if a match is found, the form can be automatically filled in with the correct data, eliminating the need for manual data entry.

However, data integration is not always as simple as it might seem. In practice, there are many challenges, such as differences in data formats, inconsistencies in reference data, or the need to use advanced algorithms to convert old data to a new format. To effectively support the agent in this process, the system should also use external databases to enrich the available information.

It is important to design a solution that performs these operations in parallel without blocking the user or delaying the process. The worst thing you can do is force the agent to click multiple buttons to manually check different data sources. The system should autonomously and automatically decide when and how to use external data sources and how to inject this data into the form in a way that is transparent to the user.

Customer segmentation and personalization of the offer
Fast charging doesn’t end with automatic data populating. A key element is also the creation of an offer that is appropriate to the customer’s needs. This is where customer segmentation comes in. By analyzing historical data, predefined offer packages can be created for different customer segments. Studies show that the best approach is to offer three options: a good deal for the customer, the best option, and a third that slightly exceeds the customer’s needs. The client most often chooses the middle option, which proves the effectiveness of this approach.

Of course, there is a possibility that the system may not perfectly match the customer’s offer. In such a situation, the system must allow changing the composition of the package to better meet the customer’s expectations. Rules for segmentation and package composition can be stored in a separate business rules engine, which allows for rapid adjustment of values ​​and rules to changing market conditions.

In the context of fast pricing, AI can play a key role in formulating customer segments and creating appropriate packages. AI, based on data analysis, can significantly improve the accuracy and personalization of offers, which in turn leads to greater customer satisfaction and better sales results.

Example of rapid implementation of prices
As an example of the effective implementation of quick prices, we can refer to the agent portal, where it takes less than 10 seconds from opening the first page to obtaining a quote in most cases of car insurance. This result is possible thanks to the correct organization of the process, integration with databases and personalized presentation of offers. This allows agents to focus on key aspects of customer service while customers receive fast and accurate quotes, significantly improving the insurance buying experience.

The introduction of quick pricing is a step into the future where speed, accuracy and personalization are the keys to success in the insurance industry.