How to Find the Best Insurance Pricing Strategy for Optimal Results

Finding the Pricing Model that Delivers on Your Business Goals

The quest for competitive and profitable pricing strategies in insurance is a never-ending one. Today’s insurance market moves faster than ever, requiring the application of dynamic pricing, backed by the latest in artificial intelligence (AI) and machine learning (ML) technologies.

In this blog post we’ll look at alternatives when crafting insurance pricing strategies, various implementation approaches, and the many desirable outcomes possible.

Understanding Insurance Pricing Strategy

At a basic level, insurance pricing strategy involves the interplay of risk assessment, actuarial analysis, market dynamics, regulatory compliance, and customer characteristics. Insurers aim to strike a balance between adequately covering risks, remaining competitive, and satisfying regulatory requirements, all while striving for profitability.

The number of interrelated variables to be considered makes the pricing process complex, and one that involves all the functions of the business:

1. Risk Assessment

Insurers must analyze the risks associated with insuring a particular property or liability. Consideration involves such traditional factors as location, age, condition, usage, and past claims history. More recently, through such technologies as Internet of Things (IoT) monitoring and the use of sensors, behavior and usage have contributed additional data points that can be factored into risk assessment, and made the assessment more reflective of actual risk patterns.

2. Actuarial Analysis

Actuaries have traditionally used standard statistical models and historical data to estimate the likelihood and cost of potential losses, including the frequency and severity of past claims, natural disaster risks, and demographic trends.

3. Underwriting

Over the years and based on past experience, underwriting guidelines are put into place that outline the criteria for accepting or rejecting risks and determining appropriate premiums. These guidelines may vary based on variables such as the insurer's risk appetite, financial goals, and regulatory constraints.

4. Competitive Analysis

Market dynamics and competitors' pricing strategies have become more important, as consumers can “shop around” among traditional insurance companies and online-only alternatives. This requires more attention than ever to the ability to offer competitive premiums to attract customers while not endangering stability.

5. Regulatory Compliance

Insurers operate in a highly-regulated world, and must comply with regulatory requirements, which include rate filings, approval processes, and limitations on pricing practices. Compliance is designed to ensure fairness, transparency, and consumer protection within the market, and is highly dependent on local/national regulations.

6. Loss Mitigation and Risk Management

Insurers may incentivize policyholders to adopt risk mitigation measures, such as installing security systems or implementing safety protocols. These efforts can reduce the likelihood and severity of losses, potentially leading to the “win-win” of lower premiums.

7. Policyholder Characteristics

Individual policyholders' characteristics, such as credit scores, claims history, and coverage options, can also influence premium rates. Insurers may offer discounts or surcharges based on these factors.

8. Distribution Channels

The method through which insurance products are sold can impact pricing strategy. For instance, direct sales via online platforms may result in lower distribution costs, allowing insurers to offer more competitive premiums.

All of these considerations and constraints and have been in place throughout the history of the industry. Traditional pricing in insurance typically involves setting premiums based on historical data, actuarial analysis, and underwriting guidelines. Insurers have used static rating factors such as age, location, property type, and claims history to determine rates. Once set, these premiums remain relatively fixed for a specified period, typically one year.

Key Components of an Effective Insurance Pricing Strategy 

All of the elements outlined above continue to play a role in modern insurance pricing strategy and implementation. Risk assessment and underwriting principles, for example, don’t go away - what varies is the manner in which pricing is determined. 

Dynamic pricing incorporates real-time data and predictive analytics to adjust premiums based on current risk factors and individual behaviors.

This new approach allows insurers to personalize products and services for each policyholder, reflecting their unique risk profile at any given time. It is the ultimate in market segmentation (e.g., defining market segments of one), and targeting those individuals with each transaction.

The end result is that insurers realize their most competitive positions - allowing them to react more quickly to market changes, varying economic conditions, changing consumer tastes, and climate change, among other factors.

Insurers are searching for the power of Intelligent Operations, or Intelligent InsurOps, which will allow them to tailor every customer interaction to the individual, driving faster revenue realization, enabling market share gains, raising customer satisfaction, lowering costs, and increasing profitability.

Types of Insurance Pricing Models

Insurance pricing models have evolved over the years, and in many cases carriers have employed, and continue to employ, combinations and permutations – very few have “pure” pricing strategies of one type or another.

Based on the type of insurance and the risk factors involved, here are some common insurance pricing models:

“Traditional” Fixed Pricing Models

There are many variations on the theme, but what these traditional methods all have in common is that customers and prospects are “lumped together” into approximated “groups,” despite the fact that more nuanced and informed groupings would benefit both the insurer and the insured. The data used to aggregate individuals into these groups, moreover, is imprecise and nowhere near granular enough to make risk assessment and pricing as accurate as it could be.

Examples include experience rating, in which premiums are based on the past claims experience of the insured individual or group. Or, class rating, in which individuals or groups (classes) with similar characteristics are grouped together and charged the same premium.

Both methods simplify the pricing process and were the norm for many decades, but fail to effectively predict future risk, and often lead to overcharging or undercharging certain groups, perceived and real unfairness, and other disparities.

Usage-Based Insurance (UBI) Pricing

Usage-Based Insurance (UBI) relies on the measurement of the insured’s behavior, such as can be gathered through vehicle telematics, smartphone apps, or other Internet of Things (IoT) devices.

This allows for customization of coverage and pricing based on individual behaviors (e.g., miles driven, sudden braking, speeding, etc. for auto coverage), and is an improvement over traditional fixed pricing models.

Other Behavior-Based Pricing Models

In addition to UBI, customers and insurers can often find mutually-beneficial models that reward (or disincentivize) certain behaviors.

From the insurer’s standpoint, these behavior-based pricing models can account more accurately for risk levels, leading to more equitable pricing and potentially lower losses from claims. Policyholders who exhibit safer behaviors or take steps to mitigate risks may enjoy lower premiums or receive other rewards for their efforts.

For example, insurers may offer discounts or incentives to homeowners who implement measures such as installing security systems, smoke detectors, or smart home devices that monitor environmental risks such as water leaks or fire hazards.

Some insurers offer home insurance policies in which premiums are based on factors such as the frequency of home occupancy, energy usage, or renting the home to others. For example, homeowners who spend more time at home during the day might be perceived as having a lower risk of burglary. Those who rent their homes as an Airbnb are more likely to incur property and liability losses.

Dynamic Pricing in Insurance

Dynamic pricing, changing the price for a product or service, has permeated the online world across industries, and is a familiar and accepted practice among consumers. They are well-versed in how airlines and hotels, for example, adjust pricing to seasonality, days of the week, desirability of locations, and to clear off their excess inventory.

In the insurance world, in precise alignment with regulatory requirements, factors such as consumer preference, location, claims history, purchase propensity and offer attractiveness can all be combined in pricing that varies with each customer or prospect.

Challenges of Dynamic Pricing

Dynamic pricing will encounter certain challenges and constraints, some of which are common with traditional approaches.

For example, both traditional and dynamic pricing must comply with regulatory requirements governing insurance pricing practices, including fairness, transparency, and non-discrimination.

In addition to regulation, dynamic pricing may raise concerns related to data privacy, transparency in pricing algorithms, and potential discrimination based on real-time factors such as determining location based on IP address or location services. Missteps in this area run the risk of poor customer perception and brand damage.

Another class of challenges for implementing dynamic pricing might be characterized as technology challenges. Dynamic pricing places added emphasis on data quality and availability, building and maintaining a robust technological infrastructure, and model complexity and interpretability.

Benefits of Dynamic Pricing

These challenges are not insurmountable, and the benefits far outweigh the effort required. There is a long list of desirable outcomes from dynamic pricing. Here are the top ones:

• Customer Engagement and Satisfaction - Dynamic pricing fosters greater engagement with policyholders by providing real-time feedback on behaviors and offering opportunities for cost savings through discounts or rewards. This enhanced engagement strengthens the insurer-customer relationship, increases satisfaction, and fosters loyalty over time.

• Product Personalization – Much of this customer engagement benefit comes from the way dynamic pricing enables insurers to personalize insurance products and options for each policyholder, considering factors such as driving habits, lifestyle choices, and usage patterns. This personalized approach enhances the overall customer experience by offering tailored coverage options that better meet individual needs and preferences.

• Adaptability to Market Changes - Dynamic pricing allows insurers to adapt quickly to changes in risk factors, regulatory requirements, competitive moves, and market dynamics. Insurers can adjust premiums in response to emerging risks, economic fluctuations, or regulatory mandates, ensuring that pricing remains competitive, compliant, and aligned with business objectives.

• Improved Underwriting Accuracy - By more accurately assessing risk and adjusting premiums accordingly, dynamic pricing helps insurers improve underwriting profitability. Insurers can better align premiums with expected claims costs, reduce adverse selection, and optimize risk selection and retention strategies to maximize profitability while maintaining competitiveness.

• Fairness and Accuracy - Dynamic pricing allows insurers to adjust premiums based on real-time data and individual risk factors, leading to more accurate pricing that reflects each policyholder's unique risk profile. This fairness ensures that policyholders pay premiums that align with their actual risk exposure, rather than being overcharged or undercharged based on static factors.

All these factors taken together result in improved customer acquisition rates, higher retention, high customer satisfaction scores, and increased profitability.

Evaluating Your Business Needs and Goals

If indeed “every journey begins with a single step,” establishing your objectives begins the process of pricing optimization. Every carrier will have unique goals and objectives, based on their position in the market, competitive pressures, and financial objectives.

The most effective analyses take an “outside-in” view of the market and it challenges.

Market position can be assessed through competitive analysis, segmentation strategies, demographics, consumer behaviors, and customer needs. Pricing analysis can aid in tailoring products and services to specific segments, to better address customer preferences, and improve customer satisfaction.

Another powerful tool is customer lifetime value (CLV) analysis, to assess and optimize the long-term value of a customer relationship by predicting future revenue and costs associated with servicing the customer. In addition to general pricing strategies, insurers can use CLV insights to model the effects of personalized discounts or incentives to high-value customers, and prioritize marketing efforts to retain the most valuable segments of the customer base.

Implementing Dynamic Pricing in Insurance

There are several steps in implementing dynamic pricing. The ultimate goal, the development of a well-oiled machine for optimizing pricing strategies and performance, starts with data.

The Importance of Data

Insurers are often sitting on vast amounts of data that they can put to use in analysis, but it’s not always easy to organize and analyze.

Unstructured data existed in insurance long before computerization. Paper forms were used for decades (or centuries, depending on the insurer), and were housed in filing cabinets. Analyzing this data, when done at all, was laborious and tedious work.

Today’s unstructured data can encompass digital sources, such as social media posts, emails, customer reviews, spreadsheets, images, competitive information, video and audio files, but it’s still disorganized and siloed. Specialized software may be needed to bring some semblance of order to improve its usefulness in analysis (speech to text software, for example).

Structured data includes discrete data points such as customer names, addresses, important dates, car makes and models, and insurance claims history. The data may be stored in customer relationship management systems (CRM) or agency management systems (AMS), among other locations.

To ensure privacy and confidentiality, this data must be anonymized through techniques such as the development of synthetic data, which yields the desired representation of data relationships without compromising sensitive information.

Many insurers have supplemented these “traditional” structured data sources with behavioral data, such as is available through telematics in vehicles, and from website browsing and navigation data that can indicate users’ search patterns and preferences.

Data, of course, is a prerequisite to reaching the right conclusions, but all that data must be fed into models that will be predictive.

Choosing the Right Analytics Software

Sophisticated AI and ML software are required to analyze the various pricing options and their effects on demand, competitive position, and financial outcomes (closed business, profit margins, etc.). The power of these advanced techniques is their ability to determine and present appropriate options based on policyholder characteristics.

Price optimization involves using mathematical algorithms to determine the optimal pricing strategy that will help insurers grow their book of business and revenue. Their flexibility allows insurers to adjust premiums based on observed consumer behavior, market trends, and pricing sensitivities.

The tradeoffs of desired business outcomes can also be evaluated to ensure they are in line with regulations, adhere to internal underwriting guidelines and other policies and procedures, balancing all the myriad possibilities that pricing presents for business optimization.

Steps to Developing an Effective Insurance Pricing Strategy

In many ways, implementing an effective insurance pricing strategy follows the outline of any effective business transformation. It is a process of continuous improvement, following the same basic steps that might be followed for profit improvement, revenue enhancement, or public relations awareness building.

1. Define Objectives - Clarify what you aim to achieve with AI pricing. Are you looking to prioritize revenue, optimize profit margins, increase market share, or achieve some combination?

2. Implementation - Integrate the AI pricing model into your pricing infrastructure, whether it's an e-commerce platform, pricing software, or enterprise resource planning (ERP) system. Establish processes for monitoring and updating the pricing model quickly over time as new data becomes available and market conditions change.

3. Evaluation and Optimization - Continuously monitor the performance of the pricing system against defined objectives and KPIs. Refine and optimize the pricing model based on insights gained from ongoing analysis and feedback loops. AI-based pricing models afford a level of agility and flexibility not found in earlier modeling paradigms.

Conclusion

In today’s world, AI-driven insurance pricing enables the rewarding experiences that customers and prospects crave. Pricing teams can deploy dynamic pricing that moves at the speed of the market, and pricing models can be scaled up to meet expanding markets and be maintained with ease.

The Earnix solution capitalizes on advanced AI technologies to bring the best in pricing and rating to insurers. These agile insurance solutions integrate flexibly with existing systems and infuse automation and industry-leading analytics into every aspect of the pricing process.

The outcomes and benefits are substantial and proven in the “real world” – the case study of BavariaDirekt is just one of many outstanding insurer outcomes from implementing Earnix technology. Earnix customers regularly report increased revenue, lower costs and overhead, and new levels of profitability.

We invite you to learn more today about how Earnix delivers on the promises of this new world of insurance pricing.