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Looking ahead to a new decade, insurance companies have much to consider for 2020 and beyond. Many novel challenges lie in wait, especially concerning the use of technology. As more insurance companies adopt tech solutions for how they run their business, they are also in a position to leverage new innovations to get more value from their marketing. In fact, companies that aren't able to take advantage of tech in their marketing risk falling behind.

Data analytics has become an especially powerful tool for insurers. Many insurance carriers have seen the impact that advanced data analytics has on their underwriting processes. Using predictive analytics, insurance carriers can model for catastrophe events for property insurance or build algorithms for rating auto insurance drivers. Data science is a necessity for insurance companies who need to calculate risk and gain insights on key 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 predictive analytics can help companies optimize their sales funnel-delivering a better return-on-investment and uncovering new revenue opportunities.

Insurance companies that understand and implement advanced data analytics in their marketing will put themselves in a position for long-term success.

How Is Marketing Data Analytics Used?
Customer Acquisition Cost
Customer Acquisition Cost, or CAC, is an important metric 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 CAC tells you how much you are spending to get your new policyholders.

Data analytics can help insurers get a clear view of their CAC, showing both how much they're spending for organic lead generation efforts or the costs from third-party vendors. A performance summary can show how different lead generation and acquisition efforts fare, helping inform decisions over where firms should spend their resources.
Lead Scoring
Data analytics can also play a role in the scoring of leads. With lead scoring, insurers can determine how close they are to underwriting a policy for a new consumer. This process works by ascribing specific values to different 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. This informs your marketing and sales teams when a lead is closest to conversion.
Predictive Modeling
One of the most effective tools insurance carriers can utilize is predictive modeling. By using predictive modeling, insurers can leverage existing data to create models that will offer predictive outcomes of future marketing initiatives.

For example, a carrier can see the cost/benefit analysis for accepting third-party leads over the weekend or during off-peak hours. By utilizing their own first-party data, insurance companies can make determinations over their strategies for lead generation and acquisition. Other revelations from predictive analytics might include highlighting seasonal upticks from consumers or how spending can change across different demographics and locations.

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 Data Analytics

Each insurance company must create their own strategy to best leverage their first-party data using data analytics. Lead management 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 data analytics.
1. Data Capture
The first stage for any business looking to extract more value from their data is to ensure that they are capturing as much first-party data as available. Insurance companies would want to look into call tracking software for inbound calls and lead tracking software for web leads.

Tracking your inbound leads will give you a wealth of 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 data, the next step is management and analysis of that data. Businesses must determine what metrics are going to be most important for insights into target audiences. Once these metrics are identified, you can utilize dynamic real-time reports and data analytics to spot trends and identify opportunities to earn more for your marketing and lead acquisition spend.
3. Optimization
The final stage is putting your results into action. Using predictive modeling, implement changes and see how well the reality matches the prediction. By using the predictive outcomes as a guide, you can lower your risk on new marketing initiatives.

In order to build a strategy for long-term success, insurance companies must continually capture, analyze, and optimize. The changes in consumer behavior or other trends will be noticeable in the data, which is why data remains so vital for insurers. It ensures that you have strategies in place that reflect the current state of consumer activity. By using data analytics to keep your business plugged in, you can continually attract and acquire waves of new policyholders.

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How Insurance Companies Can Find Success with Data Analytics - Phonexa

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Looking ahead to a new decade, insurance companies have much to consider for 2020 and beyond. Many novel challenges lie in wait, especially concerning the use of technology. As more insurance companies adopt tech solutions for how they run their business, they are also in a position to leverage new innovations to get more value from their marketing.

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Insurance: Data analytics can make insurance firms smarter

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Technology revolutions of the last few decades and falling cost of technology create new opportunities for insurers to harness the data.

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Advanced Analytics For Insurance Industry: 6 Major Use Cases | Blog

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The insurance industry is a high-risk sector. Using advanced analytics Navigate through tricky claims procedures, pricing and promotion, mitigating risks...

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Traditionally, the insurance industry relied on historical data to assess the likelihood of potential risks occurring in the future. Now, as digitization and the usage of digital technologies increase across all business processes, data is being collected at every touchpoint of the customer journey.

Today's insurers have access to an enormous volume of data, including real-time data, that can be used to supplement historical data. When paired, the two forms of information can give them a deeper understanding of the customer experience and potential risks. Applying analytics to this data is helping insurers get the insights they need to personalize products and services, improve operations, make faster and more strategic business decisions, and drive more value across the insurance value chain.

Let's explore the top five use cases of insurance data analytics:

1. Design products based on customer behavior

As organizations look to grow their business in the digital age, putting customers first is no longer a differentiator, but an industry must-have. With the volume of customer data readily available - either through public domains or collected from points of interactions with the customer - companies are leveraging this data to understand the customers' needs and preferences.

Data science is utilized to synthesize a large amount of data and generate actionable insights. For example, Natural Language Processing (also known as text analytics) can build data engines that go through multiple social media platforms and review websites. After collecting information from various channels and touchpoints, these tools can develop their own digital content to study and comprehend what an insurer's customers are talking about in real time.

This insight can then be applied to create innovative products and services, develop an easy-to-use interface for customer-facing processes, or provide customized communications - all from a customer-first perspective.

2. Develop an enhanced customer experience

Outside of providing personalized products and services, insurers are also increasingly expected to be able to deliver high-quality customer service.

Just some of the leading factors in determining when a service experience was "good" include a fast response time, personalized recommendations, and cross-channel consistency.

As consumers continue to conduct the majority of their interactions with brands via digital channels, they've come to expect 24/7 customer support. With an efficient analysis of customer buying and behavioral data, insurance companies can respond to customer queries instantaneously using chatbots. Apart from answering customer queries, chatbots can provide contextually relevant insights, educate customers on process flows, help compare policies, and suggest suitable insurance coverages. Conversational chatbots are powered by data analytics to provide personalized experiences, new product quotes, and can even be programmed to address a customer's insurance claims.

For example, a typical auto claims experience requires a customer to call their insurer's call center for assistance. This process can often result in a long wait time as the caller is bounced through several service reps before eventually getting the support needed. Instead, using advanced data analytics, insurance providers can improve this process by immediately obtaining the customer's profile, accessing the GPS locational data, and providing a personalized - and frictionless - claims experience.

Timely and efficient services will help customers feel valued, thereby increasing long-term brand loyalty.

3. Generate actionable insights for targeted marketing

With the rise of digital media, various new marketing tactics such as emails, texts, social media, and in-app engagements are being used to attract and engage both existing and potential customers. This targeted strategy not only helps insurers stay competitive, but can also offer a financial incentive.

Some benefits that can be unlocked by targeted marketing efforts include:

Longer-lasting customer relationships: Customers are far less likely to switch brands if you continuously provide them with relevant and valuable information.

A lower cost to acquire new customers: When ads are tailored specifically to the consumer, the length of time it takes to convert a potential customer is reduced.

Higher engagement levels: By offering innately valuable messaging to the target audience, insurance providers will be able to connect more effectively with both existing and potential customers.

Advanced analytics can help insurers streamline their marketing efforts and ensure that the messaging is more proactive, personalized, and targeted. One way to achieve this is by consolidating customers' search data. By organizing this massive volume of information, it becomes easier to analyze buying behaviors, send customized messages, and promote suitable products to the target market in the appropriate channels. Insurance marketers can also leverage customer lifetime value (CLV) data to segment their existing customer base and better understand the real-time performance of campaigns.

By applying data science, insurers will be able to use a more targeted approach, increasing the effectiveness of their marketing campaigns while also creating a more positive perception of their brand.

4. Improve your risk management strategy

Another powerful application of insurance data analytics is the ability to perform an optimized risk assessment of each application before a policy is ever issued.

Now more than ever, insurance risk managers have improved access to internal and external data as well as analytics that allow them to conduct comprehensive risk assessments. For example, a customer's risk profile can now be measured for a defined timeframe by analyzing historical data collected from credit agencies, third-party vendors, and also by observing social media.

Previously, underwriters were relying on specific, predefined guidelines, basic statistical models such as profiling and scoring models, and their intuitions for evaluating the risk of insurance policies. With the availability of predictive analytical models, underwriters can now make data-driven predictions about a customer's risk profile with higher accuracy and apply these insights to premium pricing accordingly.

5. Minimize the risk of insurance fraud

Any organization that exchanges money with customers, service providers, or vendors has to consider its risk of exposure to fraud and abuse. However, due to the massive size of the insurance market, there is a significantly higher rate of fraud compared to other industries. In fact, estimates from the FBI put the total cost of insurance fraud at more than $40 billion per year. This impact isn't only felt by insurers, however; the average U.S. family can expect to pay between $400 and $700 per year in the form of increased premiums.

To better prevent fraud while also safeguarding the interests of legitimate customers, insurers can leverage data science and predictive modeling tools to analyze real-time customer data with a higher degree of accuracy.

By using predictive analytics and studying historical data and customer behaviors, data professionals can identify the likelihood of a false insurance claim and detect patterns of suspicious behaviors, automatically triggering the appropriate response based on predefined conditions. Compared to traditional human-led fraud detection, AI and machine learning algorithms can uncover correlations and patterns of fraudulent behavior more quickly and with less of a chance for error.

Addressing fraud through the use of data analytics and minimizing fraudulent claims can save an insurer a significant amount in pay-outs, enabling the company to extend the benefit of competitive insurance premiums to all customers.

The future of insurance

Data analytics is empowering modern insurance professionals, giving them the business intelligence needed to understand their customers and build better products and services in order to meet customer needs. As market demands continue to evolve, business leaders will need to make strategic data investments and have a nimble IT infrastructure to respond quickly. Doing so will allow insurers to harness each and every byte of data to gain deeper insights and drive profitable business results.

Ready to leverage data analytics for your insurance company but need guidance on how to get started? Check out our data services page to learn how we can help you tap into the power of data analytics.

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Insurance Data Analytics | ValueMomentum

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Traditionally, the insurance industry relied on historical data to assess the likelihood of potential risks occurring in the future. Now, as digitization and the usage of digital technologies increase across all business processes, data is being collected at every touchpoint of the customer journey.

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According to Willis Towers Watson, more than two-thirds of insurers credit predictive analytics with reducing issues and underwriting expenses, and 60% say the resulting data has helped increase sales and profitability.

That figure is expected to grow significantly over the next year, as the inherent value of predictive analytics in insurance is showing itself in myriad applications.

Predictive analytics tools can now collect data from a variety of sources - both internal and external - to better understand and predict the behavior of insureds. Property and casualty insurance companies are collecting data from telematics, agent interactions, customer interactions, smart homes, and even social media to better understand and manage their relationships, claims, and underwriting.

Another closely related tool is predictive modeling in insurance, such as using "what-if" modeling, which allows insurers to prepare for the underwriting workload, produce data for filings, and evaluate the impact of a change on an insurer's book of business. The COVID-19 crisis has shown insurers that the ability to predict change is invaluable, and "what-if" modeling is a great tool for carriers that know they need to make changes but want to ensure they are doing it accurately. The right predictive modeling in insurance software can help define and deliver rate changes and new products more efficiently.

Using the plethora of data now available, here are 11 ways predictive analytics in P&C insurance will change the game in 2021.

Pricing & Risk Selection

This isn't exactly a new use for predictive analytics in insurance, but pricing and risk selection will see improvement thanks to better data insights in 2021. Given the increased variety and sophistication of data sources, information collected by insurers will be more actionable.

Why do these data sets help predictive analytics improve pricing and risk selection? Because they are largely comprised of firsthand information. Data and feedback collected from social media, smart devices, and interactions between claims specialists and customers is straight from the source. Data that isn't harvested through outside channels (such as the typical demographic material used in the past, like criminal records, credit history, etc.) is more direct, and can provide valuable insights for P&C insurers.

But just how much data are insurers collecting from IoT-enabled devices? Some reports estimate it's approximately 10 MB of data per household, per day, and that figure is expected to increase.

Identifying Customers at Risk of Cancellation

Predictive analytics in P&C insurance is going to help carriers identify many customers who require unique attention - for example, those likely to cancel or lower coverage. More advanced data insights will help insurers identify customers who may be unhappy with their coverage or their carrier.

Having this knowledge in hand will put carriers ahead of the game and allow them to reach out and provide personalized attention to alleviate potential issues. Without predictive analytics, insurers could miss credible warning signs and lose valuable time that could be used to remedy any issues.

Identifying Risk of Fraud

P&C insurance companies are always battling various instances of fraud, and oftentimes aren't as successful as they would like. The Coalition of Insurance Fraud estimates that $80 billion is lost annually from fraudulent claims in the United States alone. Additionally, fraud makes up 5-10% of claims costs for insurers in the United States and Canada.

Using predictive analytics, carriers can identify and prevent potential fraud before it happens, or retroactively pursue corrective measures. Many insurers turn to social media for signs of fraudulent behavior, using data gathered after a claim is settled to monitor insureds' online activity for red flags.

Insurers are also relying on insurance predictive modeling for fraud detection. "Where humans fail, big data and predictive modeling can identify mismatches between the insured party, third parties involved in the claim (e.g. repair shops) and even the insured party's social medial accounts and online activity," according to SmartDataCollective.

Triaging Claims

Customers are always looking for fast, personalized service. In the P&C insurance industry, that can sometimes present a challenge. But with good predictive analytics systems, carriers will be able to prioritize certain claims to save time, money, and resources - not to mention retain business and increase customer satisfaction.

Predictive analytics tools can anticipate an insured's needs, alleviating their concerns and improving their relationship with their carrier. It can also contribute to tighter management of budgets by employing forecasted data regarding claims, giving insurers a strategic advantage.

Focusing on Customer Loyalty

Brand loyalty is important, no matter the product, and now insurers can use predictive analytics to focus on the history and behavior of loyal customers and anticipate what their needs may be. How important is brand loyalty? About half of customers have left a company for a competitor that better suited their needs. Also, this data can help insurers modify their current process or products based on the information.

Identifying Outlier Claims

Predictive analytics in insurance can help identify claims that unexpectedly become high-cost losses - often referred to as outlier claims. With proper analytics tools, P&C insurers can review previous claims for similarities - and send alerts to claims specialists - automatically. Advanced notice of potential losses or related complications can help insurers cut down on these outlier claims.

Predictive analytics for outlier claims doesn't have to come into play only after a claim has been filed, either; insurance companies can also use lessons learned from outlier claim data preemptively to create plans for handling similar claims in the future.
Download our eBook to learn how AI and advanced analytics enhance claims decisions
Transforming the Claims Process

With predictive analytics, insurers can use data to determine events, information, or other factors that could affect the outcome of claims. This can streamline the process - which traditionally took weeks and even months - and help the claims department mitigate risks. This also allows insurers to analyze their claims processes based on historical data and make informed decisions to enhance efficiency.

Advancements in artificial intelligence and other analytical tools have also become increasingly important in the claims process, and are transforming how carriers do business.

Data Management & Modeling

Data is one of the most valuable assets an insurer can have, and predictive analytics has been helping businesses make the most of that data. From forecasting customer behavior to supporting underwriting processes, predictive analytics and data have been working together to provide valuable insights to insurers for years now.

However, making the most of your data is only possible with excellent data management and modeling capabilities. If data is scattered across disparate systems and there isn't a strategic plan in place, all of that data is wasted. With data management solutions, predictive analytics tools can build a robust customer profile, provide cross-sell and upsell opportunities, or even forecast potential customer profitability. And with insurance data models, insurers can deliver on-demand services to their customers via the cloud, using the data-driven insights gathered from their data management platforms.

Identifying Potential Markets

Predictive analytics in insurance can help insurers identify and target potential markets. Data can reveal behavior patterns and common demographics and characteristics, so insurers know where to target their marketing efforts.

Since there are 3.2 billion people on social media around the world, these platforms have become increasingly important when it comes to identifying potential markets. It's also influenced customer service: about 60% of Americans say that social media has made the customer service process easier when it comes to obtaining answers and resolving problems.

Gain a 360-Degree View of Customers

TechTarget defines the 360-degree view of a customer as "the idea that companies can get a complete view of customers by aggregating data from the various touch points that a customer may use to contact a company to purchase products and receive service and support."

Using predictive analytics, insurers can quickly and accurately consolidate data and generate new insights that paint a more complete picture of a customer. What are their buying habits? What is their risk profile? How apt are they to buy new or expanded coverage? Before predictive analytics, insurers could estimate or take guesses at these questions, but now they are able to accurately and effectively service customers, which ultimately results in happier customers and increased revenues.

Providing a Personalized Experience

Many consumers value a customized experience - even when it comes to shopping for insurance. Predictive analytics in insurance provides the capability to comb through IoT-enabled data to understand the needs, desires, and advice of their customers.

Going forward, more and more insurers will use predictive analytics to help forecast events and gain actionable insights into all aspects of their businesses. Doing so provides a competitive advantage that saves time, money, and resources, while helping carriers more effectively plan for a future defined by change. After all, data is only a strategic asset when you can actually put it to work.

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2021 Trends for Predictive Analytics in Insurance

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According to Willis Towers Watson, more than two-thirds of insurers credit predictive analytics with reducing issues and underwriting expenses, and 60% say the resulting data has helped increase sales and profitability. That figure is expected to grow significantly over the next year, as the inherent value of predictive analytics in insurance is showing itself in myriad applications.

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03 Mar 2022

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Use Cases & Benefits of Data Analytics in Insurance Industry

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Home > Blog > Insurance Analytics Use Cases & Benefits - A Business Perspective 03 Mar 2022 Our specialists can help leverage data analytics for your insurance business to harness each byte to drive profitable results Partner with us to craft & implement your analytics strategy that offers greater visibility into your business operations We use cookies We use cookies to improve your experience.