Looking ahead to a new decade, insurance companies have much to consider for 2023 and beyond. Many novel challenges lie in wait, especially concerning the use of technology in marketing for insurance companies.
As more insurance professionals adopt tech solutions for running their businesses, they can leverage innovations to get more value from their marketing. Companies unable to take advantage of tech in their marketing risk falling behind.
With data analytics, insurance providers and agencies can enjoy a potent tool for actionable insights and measurable results. Many carriers have seen the impact of advanced insurance data analytics on their underwriting processes.
Using predictive analytics in the insurance industry helps carriers model catastrophe events for property insurance or build algorithms for rating auto insurance drivers. Data science in insurance is necessary for insurance companies that need to calculate risk and gain insights into critical business decisions.
The same value-added that data analytics brought to the carriers’ ability to underwrite and serve customers can also be utilized in marketing. There are myriad ways that data science and insurance predictive analytics can help companies optimize their insurance sales funnel — delivering a better return on investment and uncovering new revenue streams.
Companies that understand and implement advanced analytics in insurance marketing will put themselves in a position for long-term success.
Introduction to Data Analytics in Insurance Sector
The insurance industry uses analytics to manage risk while providing coverage to its clients.
Leading carriers and agencies use data and advanced analytics in insurance to reshape risk assessments, enhance the customer experience, and boost productivity and decision-making across the auto, health, or life insurance underwriting process and flow. On top of that, insurance companies frequently apply the very same findings to loss prevention.
Carriers routinely access previously unfathomable volumes of third-party data from various subject areas, including environmental data sources, industry-specific data, geolocation data, etc. They have developed agile capabilities for obtaining, testing, maintaining, using, and reusing data in their sales frameworks and models.
In addition, carriers have created advanced tech stacks allowing for cost-effective model development and ongoing revisions. Insurance industry analytics use the most up-to-date methods to implement predictive, descriptive, and prescriptive models.
Naturally, the characteristics of insurance underwriting data and the excellence of insurance underwriting data science vary by segment. That is why insurance professionals often depend on segment-specific data and their understanding of inherent risks to inform the most impactful use cases.
What Are the Challenges of the Insurance Industry?
First and foremost, many lead generation challenges exist for insurance agents and companies, including staying ahead of the competition, bringing in high-intent insurance leads, building rapport, and delivering elevated customer experience.
According to Deloitte research, the insurance industry is still on its way to maturity in data management and insurance analytics. In the recent online survey, Deloitte asked carriers to describe the maturity level of their data analytics in insurance using one of three phases: Explorers, Adopters, and Pioneers.
Source: Deloitte
The results indicate that only 6% of insurance companies use advanced analytics for insurance that influences the majority of decisions and encourages innovation across their operations.
But when it comes to applying data analytics, insurance professionals still have to upgrade their capabilities, such as prioritizing analytics in insurance underwriting and pricing and harnessing the power of data analytics in insurance claims processing.
With the application of granular insights derived from insurance data, insurance companies have a better chance of upgrading their maturity level in lead distribution, customer segmentation, and overall marketing efforts.
Implementing Insurance Data Analytics
Department sizes, cost estimates, and other aspects vary across companies, impacting how well they can perform their jobs. That emphasizes the importance of having a solid insurance and analytics platform as part of a marketing automation solution.
Such solutions often level the playing field for insurance providers and agencies in fraud prevention by enabling fraud analytics that empowers companies to begin safeguarding themselves accurately and thoroughly.
There’s been an increasingly prevalent presence of solutions designed to aid data analytics for insurance companies. For instance, it is common for businesses to use predictive analytics in health insurance.
Many healthcare applications and insurance clients benefit from predictive analytics in healthcare as it results in more personalized medical services, earlier interventions, and lower hospital costs.
Physicians, healthcare institutions, and carriers use predictive analytics in insurance to predict the likelihood of specific health conditions developing in their patients, including cardiac problems, diabetes, or CVD.
There’s no denying that companies offering health insurance were early supporters and adopters of this technology due to its ability to determine whether patients require interventions. Insurance analytics software is one of the ways to deliver excellent customer experience, improve data acquisition strategy in insurance, prevent diseases, and enhance clients’ health outcomes.
How Is Sales and Marketing Data Analytics Used in Insurance?
Customer Acquisition Cost
Customer Acquisition Cost, or CAC, is an important metric and part of analytics for insurance companies to focus on. As an average of your sales and marketing spend divided by the number of new customers you acquire, your insurance customer acquisition cost informs you how much you are paying to get your new policyholders.
Companies equipped with customer acquisition data analytics can make the most of the insurance data captured at the three traditional and predictable points of contact: buying a policy, submitting a claim, and renewing a policy. Leveraging insurance customer analytics in all three instances can be incredibly beneficial.
Companies use customer analytics in insurance to align their operations with the occurrences and events in their clients’ lifetimes to create and offer an excellent customer experience. For instance, when a client gets married, companies can offer to extend coverage and include a spouse.
Deloitte’s assessment of U.S. life insurance statutory filings showed that the number of policies sold has decreased by 27% over the last 20 years. At the same time, the U.S. adult population has increased by 25% over the same period. In other words, there was a 42% decrease in the number of policies sold per capita.
What does it mean? The overall success of the insurance industry will likely be determined by the company’s ability and decision to adopt life insurance analytics and innovative solutions to provide customers with more meaningful experiences, relevant services, and solutions.
Data analysis in the insurance industry helps marketers and insurers get a clear view of their CAC, showing how much they spend on organic lead generation efforts and the costs from third-party vendors. A performance summary can show how different lead generation and acquisition efforts fare, helping inform decisions about where firms should spend their resources.
Lead Scoring
Data analytics can also play a role in the scoring of leads. With lead scoring and analytics in insurance underwriting, marketers and insurance professionals can determine how close they are to underwriting a policy for a new consumer. This process works by ascribing specific values to different insurance data points for a lead — these values add up to create a “score” for the lead.
You can use these lead scores to create marketing-qualified leads and sales-qualified leads. That informs your marketing and sales teams when a lead is closest to conversion.
Insurance Predictive Modeling
One of the most effective insurance machine learning use cases is predictive modeling. By using predictive analytics software for insurance, businesses can leverage existing insurance customer data to create models that will offer predictive outcomes for future marketing initiatives.
For example, a carrier can see the cost/benefit and insurance data analysis for accepting third-party leads over the weekend or during off-peak hours. Insurance companies can determine their lead generation and acquisition strategies using their first-party data.
Other revelations from insurance predictive analytics might include highlighting seasonal upticks from consumers or how spending can change across different demographics and locations.
Phonexa’s predictive analytics for insurance offers companies more insight and control, removes the guesswork from marketing campaigns, and enables carriers, agencies, and individual agents to get a more informed view of when, where, and why the demand for their products or services requires their attention.
Predictive modeling in insurance remains one of the essential tools due to its ability to sort through a wealth of insurance data and use different variables to generate predictive outcomes.
The data analytics benefits of predictive modeling are not just a way for insurance carriers to keep an edge over the competition; they also provide clear pathways for increased profitability and long-term growth.
Improving Insurance Marketing With Analytics
Insurance providers frequently classify and quantify insurance data as a short-term expenditure managed for specific purposes or projects. Different maturity stages in data management and insurance analytics imply different levels of return from insurance marketing efforts and analytics as a strategic asset.
Insurance industry data analytics allow carriers to improve their risk and cost control, making it easier to transition from Explorer to Adopter, and later to the Pioneer stage. Based on Deloitte’s three-stage maturity model, let’s take a closer look at how insurance companies can gain ground and leverage insurance data insights.
Source: Deloitte
As you can see, Explorers typically look to effectively assess risks and cut costs through improved insurance claims analytics and fraud detection techniques. On the other hand, Adopters tend to focus on customized products and market scoping, resulting in significant revenue growth, service enhancement, and expanded insurance analytics market share.
Finally, Pioneers tend to sustain competitive advantage by continually investing in the application of data science in the insurance industry, making insurance analytics part of their organization’s culture.
Each insurance company must create its strategy to best leverage its first-party data using data analytics. Lead management and insurance analytics software can help businesses with tools for data analytics to face their marketing challenges and optimize their lead acquisition.
Here is a roadmap for insurers who want to take advantage of the benefits of capturing and analyzing insurance industry data.
1. Insurance Analytics Tools & Software
Carriers consistently improve business models and develop disruptive offerings that yield non-risk revenue. As the data-as-a-business approach becomes increasingly popular and proves its effectiveness, insurance professionals personalize and extend their offerings through unique insurance data insights.
While arguments for harnessing the power of insurance data and analytics are compelling, many insurance execs keep wondering where to start.
The first stage for any business looking to extract more value from its data is to ensure they capture as much first-party data as possible. Industry professionals would want to look into call analytics solutions for insurance, predictive analytics insurance software, and lead tracking software for web leads.
Tracking your inbound leads will give you a wealth of insurance data that can be utilized for better understanding your customer journeys and be the first building block for an optimized marketing approach.
2. Reporting & Analytics
Once you have set up the technology solutions to capture your insurance data, the next step is managing and analyzing that data. Businesses must determine the metrics most important for insights into target audiences. Once these metrics are identified, you can utilize dynamic real-time reports, analytics and data to spot trends and identify opportunities to earn more for your marketing and lead acquisition spending.
3. Optimization & Predictive Modeling in Insurance
The final stage is putting your results into action. Businesses can implement changes and see how well the reality matches the prediction using predictive modeling in the insurance industry. Besides, using the predictive outcomes as a guide can help carriers and agencies lower their risk on new marketing initiatives.
Insurance companies must continually capture, analyze, and optimize to build long-term success strategies. The changes in consumer behavior or other trends will be noticeable in the data, which is why insurance data remains vital for carriers and insurance professionals. It ensures that you have strategies that reflect the current state of consumer activity.
Ultimately, companies using data analytics manage to keep their business plugged in and can continually attract and acquire waves of new policyholders.
The Use of Business Analytics in Insurance
The use of business analytics in insurance is no longer optional; it’s essential. With increased competition, stronger regulations, and decreasing customer loyalty, insurers can’t afford to stick with the usual manual processes. They need a better grasp of data in insurance to understand their business performance and risk exposure, ensure more effective claim management, and improve customer satisfaction.
Source: DAMCO
With business analytics in insurance, companies can transform big data into decision-making insights, retain and grow their customer base, get a complete view of clients’ previous interactions with the brand, and ensure deeper customer engagement.
On top of that, BA facilitates data analytics in insurance underwriting and encourages underwriters to focus on tasks that require personal judgment and thoughtful decision-making while allowing systems to handle manual tasks.
How Does Insurance Industry Data Analytics Benefit Users?
Deloitte’s 2021 Insurance Outlook report states that one of the top priorities for insurance industry leaders is creating innovative and unique customer experiences. Rapid digitization, ramped up by the COVID-19 pandemic, has fundamentally changed how customers behave and what they want.
Assessing the need for insurtech data analytics typically begins with thoroughly examining the company’s current data setup.
The use of analytics in insurance entails assisting businesses in aggregating, integrating, validating, and compiling data to generate insights for increasing revenue and operating excellence. Carriers and agencies can use these insights to enhance the customer experience, recalibrate product development strategies, and improve customer service.
One of the most significant benefits insurance data analytics and modeling can offer clients is improved customer satisfaction and experience.
Companies can meet customer expectations with insurance data analytics and modeling because it can accurately predict the demands of potential customers by analyzing data and identifying trends in insurance analytics.
On top of that, the ability of carriers to digitally capture prospective clients’ feedback, store it, and provide actionable insights on their behavior and lifestyle habits results in a streamlined buying experience and improved customer satisfaction.
However, many insurance companies struggle to reap the full range of benefits of data and analytics in insurance.
One of the most effective approaches is to develop enterprise-level strategies for implementing data analytics software for insurance. That, in turn, will facilitate sales techniques, improve capital management tactics, and provide an excellent customer experience.
Big Data Analytics for Insurance and Risk Assessment
Big data has been a crucial component of innovation in the insurance industry, allowing marketers and insurance professionals to capture and interpret insurance data more efficiently and quickly.
With big data, insurance businesses can leverage more accurate targeting, search for anomalies, and fight fraud.
The principal goals of insurance companies when it comes to data management are to use captured information to develop and design new insurance products, understand customer spending habits, and predict their behavior and lifestyle.
Big data is a continuous and immediate information vehicle, allowing businesses to assess possible risks. Let’s examine in more detail how it can help with risk assessment:
| Approach | Explanation |
| #1. The ease of access to a patient’s medical information | Fitness trackers and other wearable devices can help healthcare professionals and insurance companies understand what medical conditions may affect their client’s health and longevity. |
| #2. Evaluating financial stability and spending habits | The use of predictive analytics in insurance and the ability to track and analyze customer spending habits helps predict customer behavior and lifestyle. |
| #3. Natural catastrophe event data | Access to such data gives a better understanding of the areas and magnitudes of future events, such as flood vulnerability or hazard mapping. |
| #4. Data from internet-connected devices | Such devices as smoke and heat detectors, intelligent leak detectors, or water usage monitors help to monitor insured properties for risk holistically and prevent incidents. |
Risk and Insurance Data Analysis Techniques
Risk analytics in insurance has much to do with categorizing, understanding, and managing various risks. The categories should correspond to the company’s objectives and risk management goals.
Risk analysis can offer tailored insights that inform risk management strategies and connect analytics and insurance experience for in-depth business intelligence. For that reason, companies often build risk programs to help reduce the possible cost of risk.
Here are four steps that help with risk and insurance data analysis:
- Determine risk exposures and financial factors that impact specific approaches to risk management.
- Determine the reasons for critical exposures to possible losses.
- Quantify risk and impact on experienced losses.
- Recalibrate risk management strategies, structures of current insurance programs, and capital allocations.
It’s not uncommon for insurance companies to partner with marketing data analytics companies to work through all required steps and dive deeply into specific areas where companies might need help.
Keep Up With Insurance Analytics Trends
It’s no secret that the insurance industry is highly competitive. Therefore, one of the principal goals for every carrier and agency is to stay current on insurance technology and stay ahead of the game.
That includes implementing call tracking, lead tracking and distribution, and data analytics and insurance software like Phonexa’s marketing automation suite, which streamlines the collection of client data without the hassle of constant back-and-forth communication.
Companies can build data set relationships and ensure deeper granularity in risk profiles and exposures by keeping abreast with the latest insurance analytics trends and making the most of analytics in life insurance and other niches.
With advanced insurance and data science, companies can successfully segment users, model behaviors, adjust prices on policies, and identify opportunities for business growth.
Schedule a consultation to learn more about how Phonexa’s all-in-one marketing automation suite can help you bring in more organic insurance leads and leverage insurance data analytics and modeling.
Frequently Asked Questions
What is insurance analytics?
Insurance analytics is the systematic process of gathering, assessing, and extracting essential insights from various insurance data sources to successfully manage risks and provide the best healthcare, auto, life, property, and casualty insurance contracts, among others.
How do I generate organic insurance leads?
Insurance companies using data analytics start by developing models that tell what their ideal leads look like and where they can find people who fit that profile.
Implementing solutions like predictive analytics for insurance companies can help them define future actions throughout the lead generation process, including spotting new high-quality leads and marketing to them more effectively.
Carriers and agencies typically use the following approaches to generate insurance leads organically:
- Ask for referrals from the existing client base.
- Build a list of positive feedback from clients on popular review sites.
- Invest in content marketing to add value to your offerings and help existing and prospective clients learn more about your brand.
- Make the most of online and offline networking to create an organic lead generation pool.
On top of that, there are insurance data companies that help carriers curate insurance data sets. Besides selling leads to insurance companies, data providers help organizations build quality business portfolios and maximize their impact.
What are the primary customer experience metrics for insurance?
Insurance companies looking to see where they stand in terms of person-to-person customer interaction and customer experience must monitor the following metrics:
- Average time to coverage: Customers are no strangers to long wait times when purchasing insurance policies. But since consumers typically have little patience for lengthy periods between application, purchase, and coverage, it’s crucial to provide a product as quickly as possible. Otherwise, they will move on to one of your competitors.
- Claim processing rate: Customers generally look for a prompt and accurate resolution. For that reason, it is crucial to track how long it takes to process an insurance claim from the initial notice of loss to the payout or denial. With the appropriate solution for claims analytics in insurance, businesses can also detect specific anomalies, manage and analyze claims data, and, as a result, improve performance.
- Claim denial rate: Businesses must compare their claim denial rate to the industry average. A significant difference will mean that you deny more claims than the average. As a result, your customers will be dissatisfied because their insurance doesn’t serve them as well as expected. But remember that denying fewer claims than the average implies raising your costs and passing them on to the consumer through higher premiums.
- Referral rate: Referrals are the most accurate indicators of whether or not your clients are satisfied with your service and their experience. It’s crucial to keep track of the number of effective referrals that resulted in conversion.
Why is it crucial to use data analytics in the insurance sector?
Insurance companies typically rely on historical data to evaluate the probability of possible risks. The increasing use of data science in the insurance industry explains the gradually enhancing capabilities of carriers to collect insurance data across all the touchpoints.
Insurance data providers and specifically designed insurance data & analytics solutions assist industry experts in gaining the insights required to personalize their offerings, improve business operations, make data-backed predictions, and drive more value across the insurance value chain.
Here’s how carriers, agencies, and individual agents can benefit from using data analytics in the insurance sector:
- Create offerings based on behavioral patterns and trends
- Deliver elevated customer experience
- Leverage actionable insights for more targeted campaigns
- Enhance insurance data acquisition and risk management strategies
- Reduce the possibility of insurance fraud
How are analytics and BI used in insurance?
Business intelligence serves as the insurance industry’s backbone, assisting leading carriers and agencies in increasing productivity, lowering operational costs, relieving employees of labor-intensive manual processes, and generating business value.
With advanced insurance analytics and BI technologies, companies can gain a comprehensive view of critical insurance data that would otherwise remain hidden in excel spreadsheets. Implementing BI and insurance analytics solutions eventually results in more informed decision-making and client-centered strategies.
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