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Insurers Are on a Never-Ending Quest
As an insurer, you serve many interests and many masters, often with goals that tug in different directions.
Consumers expect you to be nimble and responsive as you inform their decision-making; to provide competitive rates; to deliver comprehensive and affordable coverage; and, to be ready with outstanding customer service, especially in times of crisis and loss.
Stakeholders, including investors, expect you to meet those customer needs of course, but also to grow the business, be profitable, and act as a good corporate citizen, including honoring regulatory guardrails and environmental stewardship principles.
These goals are constants, and the quest for meeting them has been part of insurance since its inception. The speed with which those goals evolve, though, has become breathtaking.
In order to meet these competing needs, insurers need to enter an age of Intelligent InsurOps.
They need to unlock new strategies and processes, and take full advantage of an evolving ecosystem of technologies to transform how they operate. This agile new approach will allow insurers to seize new market opportunities and to serve their modern, demanding customers.
Central to meeting all these objectives, and achieving Intelligent InsurOps, is pricing.
Once an exercise that could accommodate months for development and promulgation, customer and market expectations are now driving the need for real-time pricing, which traditional methodologies and tools simply cannot deliver.
The answer is pricing intelligence. Pricing intelligence solutions, powered by artificial intelligence (AI) and machine learning (ML), are transforming insurance, enabling carriers to make smarter, data-driven pricing decisions in real-time to stay competitive and maximize profitability.
In this blog post we’ll look at just what pricing intelligence involves, how it can drive business results, and how to get from where you are today to a world of nimble, responsive pricing that satisfies all the needs we’ve outlined.
What Exactly is Pricing Intelligence?
Let’s use this as our definition:
Pricing intelligence is the practice of gathering, analyzing, and using data to make informed pricing decisions for products and services.
It encompasses a wide-ranging look across multiple data sources, using machine learning (ML) to build models that reveal novel observations, deploying the resulting prices to market seamlessly and in real time, and then leveraging market reaction and feedback to adjust and iterate pricing in a continuous fashion.
Conceptually, pricing intelligence has straightforward goals, and has been practiced in the industry, through various means and with varying degrees of success, throughout the history of insurance. Those goals boil down to the ability to set competitive prices, increase profitability, and enhance customer satisfaction.
As simple as this is conceptually, making it work in the “real world” requires several key ingredients, each applied on a continuous basis:
Competitive Price Monitoring: Through a variety of tools and techniques, continuously tracking the prices of competitors' products and complementary services, to identify changes and trends.
Ongoing Market Analysis: Understanding market dynamics, including supply and demand, climate and environmental shifts, and macroeconomic factors such as inflation and interest rates. Keeping one’s “finger on the pulse” of these macroeconomic factors is critically important to the analyses involved in pricing.
Consumer Behavior Analysis: Studying how prospects and customers respond to different price points, products, promotions, and discounts, to gauge price sensitivity and willingness to pay.
Historical Data Analysis: Reviewing past pricing data to identify patterns and predict future trends.
Dynamic Pricing Methodologies and Technologies: Where regulatory regimes allow, adjusting prices in real time based on market conditions, competitor actions, and other relevant factors.
Price Optimization Algorithms and Systems: Using AI and ML algorithms and models to determine the optimal price for maximizing revenue, profit, and market share. And, where local regulations permit, tying these systems to other customer-facing tools such as websites, social media, and customer service applications.
These are tasks that lie somewhere between daunting and impossible without the most up-to-date methodologies and technologies.
Insurers, in the past, and for some in the present, require armies of analysts, actuaries and pricing specialists to cover this waterfront. Today, to break from the slow and expensive status quo, the requisite modern tools are proven and readily available.
The Evolution of Pricing Intelligence in Insurance – How We Got Here
The history of pricing intelligence has been driven by advancements in technology and the explosion of available data, changes in market dynamics, and shifts in consumer behavior. Its evolution parallels broader trends in technology and business practices, moving from manual and extremely ponderous methods to today’s dynamic, data-driven approaches.
In the “pre-digital” era, pricing intelligence was limited, and resulted in a paradigm where prices were not frequently updated.
With the advent of computing, pricing shifted to various customized digital tools, first on mainframes and minicomputers, and then to microcomputers equipped with tools such as spreadsheets. With these tools carriers begin to uncover new insights about their customers and markets, but the tools were often slow to develop and expensive to maintain and update, required specialized IT resources, were “siloed” in insurers’ functional departments, and soon grew unwieldy and unable to keep up with the speed of the market.
The Internet, and the introduction of online commerce, has driven customer expectations for “instant gratification,” especially among the most desirable and profitable, younger segments of the market.
The need to “make it like Amazon” has driven and accelerated the need to wed competitive and market intelligence, exploding amounts of data of all kinds, and market reaction, all in real time, and has propelled the growth of AI- and ML-based tools, cloud/SaaS deployment models, and API development methodologies to propel pricing intelligence into the modern era.
The Benefits of Pricing Intelligence in Insurance
Insurance pricing intelligence offers a wide range of business benefits that enable newfound and enhanced agility, responsiveness, competitive advantage, and profitability in today’s rapidly-evolving market environment.
Here are just some of the key advantages to be had from pricing intelligence:
More Informed Decision-Making and Pricing Accuracy
Pricing intelligence incorporates comprehensive data on market trends, competitor pricing, and customer behavior, enabling carriers to make better-informed pricing decisions, including long-term strategic directions, enhancing their speed and agility.
Improved Customer Satisfaction
Understanding how customers respond to different price points allows insurers to tailor pricing strategies to meet customer expectations, making carriers who utilize pricing intelligence more competitive and improving their customer satisfaction.
An Ability to Implement Dynamic Pricing
Where permitted by regulation, pricing intelligence tools enable the implementation of dynamic pricing models, so insurers can adjust prices in real time, based on individual consumer preferences, market demand, economic conditions, and competitors’ actions and reactions.
Improved Profitability
Top-line revenue increases, lower costs, and market-focused pricing all contribute to improved profitability.
More Attractive Pricing
Evaluating the impact of base pricing, promotions, and discounts through pricing intelligence helps design more effective pricing structures and translate them into advertising, Web, social media, and other promotional mechanisms.
The Modern Approach to Risk Modeling – Driven by AI and ML
Pricing intelligence in insurance today is driven by tools powered by AI and ML, approaches and technologies that significantly enhance more sophisticated, accurate, and dynamic pricing strategies.
AI and ML contribute to improving pricing intelligence and deliver results for insurers in numerous ways:
Automating Data Gathering and Analysis
AI and ML algorithms can process vast amounts of data quickly, including historical sales data, claims data, competitor price movements, market trends, and customer behavior, often identifying complex patterns, interrelations and trends that might be missed by traditional analysis methods and human analysts.
New Insights from Predictive Analytics
AI models can predict future demand based on historical data and market conditions, and how changes in price affect demand and uptake for various policy/price combinations. This aids pricing for direct business (e.g., on the carrier’s website) and for channel-related business (through agents and brokers).
Many of these insights are enhanced by an ability to more precisely segment markets and customers.
Powering Dynamic Pricing
AI-powered pricing engines can adjust prices in real-time based on factors such as new policy options, competitor pricing, and customer behavior.
This allows insurers to deliver customized, individual options to consumers based on their purchasing history, usage data such as telematics and IOT, claims history, and other unique data.
Sharpening Competitive Analysis and Decision-Making
Modern tools can continuously monitor competitor prices and strategies, providing real-time insights into their market positioning and reactions, and enabling more strategic decision-making.
Enhanced Customer Experience (CX)
By offering competitive and personalized prices, insurers gain a “leg up” on enhanced customer satisfaction and loyalty.
AI-driven tools can also be unleashed on the base of current customers, identifying customer behaviors that are indicative of dissatisfaction, identifying customers at risk of churning, and adjusting prices, policy options, or offers to help retain them.
Scalability and Room for Unlimited Growth
Since they are most often deployed in a SaaS/cloud-based delivery model, AI-powered pricing intelligence systems readily scale to accommodate growing data volumes, turbocharged sales and marketing programs, and growing market complexity.
Steps to Developing and Implementing a Pricing Intelligence Strategy
Implementing pricing intelligence in the insurance industry involves several key steps, many of which are common to almost any business transformation project. The key is to get going, and get going fast – your competitors are not waiting around for you to figure it out.
As you begin your journey, or join this structured approach partway through, here are what we see as the key steps in that journey:
Assess Your Current Risk Modeling Practices
Every insurer has some form of risk modeling in place, whether based on traditional methods such as statistical analysis or early experiments with AI and ML. Whatever the starting point, there is always more to do, since nothing in the market stands still, and improvements can always be made, whether they be technological, organizational, or directional.
Delve into Customer Preferences and Behavior
Begin with an “outside-in” perspective and examine current pricing strategies and their competitive standing, as well as the profitability shown in various customer segments. Every insurer has access to massive amounts of current customer data to fuel this analysis.
Analyze the Competitive Landscape
Today’s competition includes traditional insurers and new digital-first entrants. This makes this crucial step more difficult and multi-variate, but also more important than ever.
Market research and analysis must also address inputs from social media, online reviews, and the data contained in your own database of customer service interactions, such as phone calls, text messages, and customer portal entries.
Set Clear Objectives and Goals
Obvious and yet all too often overlooked, determine what you aim to achieve with pricing intelligence, such as increased profitability, improved competitive positioning, or enhanced customer satisfaction.
Define the scope of the implementation, whether it's across all your insurance products, or specific lines such as auto, home, or commercial, and whether you’re undertaking a proof of concept (PoC) or pilot, or a full-blown commercial rollout.
Part of any good plan is to also identify potential failure points, challenges, and mitigation strategies. As a regulated industry, insurance is well- versed in preparing for these eventualities and defining recovery plans and responsibilities.
Choose the Right Tools
Resist the temptation to “throw good money after bad” in the belief that legacy systems can be rehabilitated or updated to meet the needs of today’s insurance business. AI and ML are necessities, not “nice to haves” in today’s world.
Don’t overlook the need for your new generation of pricing intelligence software to integrate with existing systems, such as rating and policy administration.
Take advantage of integrated underwriting tools to reduce time-consuming handoffs across the organization and bring together these two critical functions.
To allow for the best combination of tools across the organization and with outside partners, select tools that support application programming interfaces (APIs) to integrate data sources and tools, ensuring seamless data flows, and real-time updates.
Design the solution to automate data collection, model execution, and pricing adjustments to reduce manual intervention and improve efficiency and profitability.
Build Out the Data Collection and Management Infrastructure
The modern insurer is sitting on a wealth of invaluable data and has access to external sources to supplement the data found in-house.
Start with internal data such as historical data on premiums, claims, customer demographics, policy details, and underwriting policies and decision-making.
Look to external sources as needed for complementary data on market trends, competitor pricing, economic indicators, weather and climate patterns, and regulatory trends.
Specific to auto insurance, integrate telematics data to track driving behavior.
For home insurance, use Internet of Things (IoT) data for monitoring property conditions and to predict future claims exposure, and to compare those predictions to historical claims.
Perform Thorough Testing Before Deployment
Validate your new and proposed predictive models using historical data, and back-test to ensure accuracy and reliability.
Make sure the new models are predictive, unbiased, and fair.
Conduct a series of pilot tests with a subset of products and/or customer segments to evaluate the performance of the pricing strategy, as well as its ability to be modified and updated over time.
Move to full-scale deployment only after rigorous internal and external testing across the entire defined scope.
Engage in Continuous Monitoring and Adjustment
Continuously monitor your pricing intelligence system’s performance in real time, including evolving customer and prospect feedback and how the system reacts to changing market conditions.
Ensure your vendor has set up and is constantly monitoring alert systems to flag any anomalies and guaranteeing overall system health and performance.
Monitor for Ongoing Regulatory Compliance and Ethical Considerations
It goes without saying that insurers must design and test all pricing strategies to ensure that they comply with all relevant regulations and industry standards, and that they consider ethical considerations such as fairness, transparency, and anti-discrimination in pricing.
Pay attention to issues revolving around model explainability, both internally and externally.
By following these steps, insurers can successfully implement pricing intelligence systems that enhance their customer acquisition and customer experiences, improve competitiveness, and drive increased profitability.
Are you ready to leverage pricing intelligence in your organization? Check out Earnix Price-ItTM and take advantage of real-time pricing and rating and embrace the power of Intelligent InsurOps.