In today's world, where patience is becoming an increasingly rare resource, and customers expect instant responses with minimal effort, quick pricing has become a crucial element of the insurance purchasing process.
Before customers start 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 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 due to the use of unintuitive tools.
Key elements of quick pricing
To ensure a fast and efficient pricing process, it is necessary to organize the sales path into appropriate stages. In discussions with insurance company representatives, I often use a three-step division:
- Collecting minimal data: In this step, it is important to focus solely on the information that is crucial for estimating the policy costs, such as type of insurance, personal details of the policyholder, scope of coverage, duration of the insurance, payment preferences.
- Presentation of the quote: After collecting the necessary data, the system must quickly generate and present the quote so that the customer can immediately know the approximate cost of the insurance.
- Collecting complete data: The final stage is gathering all the remaining information needed to bind the policy, which, however, does not affect the premium.
Such a three-step process not only accelerates the sales cycle but also allows the agent to focus on the customer's key needs instead of struggling with inconvenient tools. Importantly, this does not exhaust the topic of quick pricing.
Integration of external data sources
Another key step in optimizing the quick pricing process is integrating the system with multiple data sources. Typically, the pricing process begins with the identification of the customer and the insured object, for example, using a social security number, vehicle registration number, property address, or company registration number. This allows for quick verification of the insurance company's internal databases, and if a match is found, the form can be automatically populated 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 into a new format. To effectively support the agent in this process, the system should also utilize 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 offer personalization
Quick pricing doesn't end with automatically populating data. A key element is also creating a quote that will be appropriate for the customer's needs. This is where customer segmentation comes into play. 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 offer for the customer, the best option, and a third one that slightly exceeds the customer's needs. The customer most often chooses the middle option, which proves the effectiveness of this approach.
Of course, there is a possibility that the system will not perfectly match the offer to the customer. In such a situation, the system must allow for the modification of the package composition to better meet the customer's expectations. The rules for segmentation and package composition can be stored in a separate business rules engine, which allows for quick adjustment of values and rules to changing market conditions.
In the context of quick pricing, artificial intelligence 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 higher customer satisfaction and better sales results.
Example of quick pricing implementation
As an example of effective quick pricing implementation, we can refer to the agent portal, where it takes less than 10 seconds from opening the first page to getting a quote in most auto insurance cases. This result is possible thanks to the proper 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 quick and accurate quotes, significantly improving the insurance purchasing experience.
Introducing quick pricing is a step toward the future, where speed, accuracy, and personalization are the key elements of success in the insurance industry.
