Article | July 19, 2022
A quick Google Trends search on data reveals that data analytics, data and analytics, data analysis, and predictive analytics have steadily grown in popularity among businesses across industries.
These terms peaked when business leaders searched for ways to increase ROI and reduce business costs and tech-based investments. The insurance industry is amongst the industries actively leveraging data analytics.
The rising importance of analytics in insurance has made CMOS take note too. As agility became more important in the insurance industry, more than 85% of global businesses shifted to a data-driven model.
The purpose of taking you back is to emphasize that, as a CMO, now you need to churn accurate data and turn it into relevant information. This is a necessary model to practice to make the right decisions or will improve the decision-making process.
Without data analytics, you are deciding in a void, and that’s not considered good practice. Forrester reports that 41% of insurance companies faced challenges in extracting data and making decisions based on it in 2020.
Take a look at how and what you can do with insurance analytics to cater to better insights into your decision-making process and, finally, ROI generation.
Bring Data to These Key Levels of Departments
Analytics in insurance raises the bar in terms of marketing. As you know, marketing results frequently fluctuate, making data insights challenging to capture. CMOS who base their decisions solely on outcomes usually loses sight of making sound decisions due to unstructured data.
Therefore, it is essential to have an aligned platform for data analysis in insurance. To begin with, marketers must understand the various types of data analytics available. Most insurance marketers employ descriptive, predictive, and prescriptive analytics, among others. This will assist them in strategizing based on continuous data insights from various sources for any given initiative.
Sales leaders can also improve how they spend their time by using data analytics to create more accurate sales forecasts. However, the question is, how will they do it efficiently?
CRM software is the answer and solution to them. The software performs best because of its analytical capabilities in combination with data visualization, particularly predictive functions. It generates enormous amounts of data on customer interactions, which can then be used to inform decisions. You can assemble relevant data and use it to make some decisions, such as:
Acquisition and management of leads
Sales funnel optimization
There is enormous value in optimizing productive data by focusing on prospects likely to become loyal customers.
Utilizing data analytics in insurance boosts insurance operations. Small changes help to align a wide range of core processes. You can access data obtained from operations, observe key aspects of the overall processes, and make appropriate decisions. A targeted, timely, and data-driven approach will help you make decisions about these key functions, which can lead to business growth in the long run.
Bain's research in 2019 reports that seventy insurers were polled. They say data analytics will reach 58% in the marketing funnel and 45% in business operations.
Begin with Overcoming Barriers to your Decision-Making Process
Use Data to Identify Customer Patterns
Information from data can identify patterns. As mentioned above in the sales section, CRM's predictive modelling and the popular Google Analytics' descriptive overview are the two best platforms for identifying customer patterns.
What is the best way to get pertinent data? Data mining is the answer to it. Do you want to know about it? Then read data mining for pattern evaluation now!
As a CMO, you're probably aware that behavioral patterns are highly predictable and can sometimes result in unsatisfactory outcomes. This occurs when you are unable to obtain relevant data. And you end up performing ineffective marketing activities. To assist you in overcoming it, an AI-enabled platform can reduce the level of effort and provide the necessary data to study your customers' patterns in real-time. This is how you will notice a significant increase in sales.
According to research by McKinsey and Company, automation saves 43% of insurance employees’ time.
Segmenting Sales Plans
Following the establishment of your customers' patterns, segmenting the insurance sales plan is a necessary step. In this process, analytics provide detailed information about customers, allowing you to make decisions about sales functionalities. This will undoubtedly reduce the time, energy, and effort you previously spent.
Accurate customer segmentation and sales forecasting can also help tailor marketing efforts, improve the sales funnel, and keep sales strategies in check.
When Media 7 contacted Vishal Srivastava, Vice President (Model Validation) at Citi, here’s what he said about data segmentation through data analytics.
CMOs must ensure that adequate data quality checks have been performed, The goal is to ensure a scientific approach to data segmentation, sampling methodology, and data outliers, which can significantly impact revenue forecasts.”
Pricing & Savings
Analytics in insurance marketing can help CMOs make cost-cutting decisions and become more cost-effective in marketing efforts. It can set price ranges based on historical, current, and predictive performance. Also, analytics will help you figure out how to price things in the future, which will be good for ROI.
Keep Improving with Data to Stay Abreast with The Decision-Making Process
Better data organization in your business boosts productivity."
Warren Buffett, an American business magnate, investor, and philanthropist.
This phase is best suited to the current business environment. Implementing data analytics in insurance now will open up tremendous opportunities in the future. To make the most of them, you, as a CMO, must stick to a data-driven model for marketing actions.
Aside from that, it appears that the data analytics you select for your business must be capable of informing and driving performance. Performances ranging from risk assessment to sales forecasting and a plethora of actionable insights assist businesses in thriving.
Frequently Asked Question
How are data analytics used in insurance companies?
Data analytics empowers insurers to optimize each function and also assess risks. It also identifies trustworthy customers, which further boosts engagement.
What does data analytics mean in insurance?
Data analytics empowers insurance professionals by providing them with the business intelligence to understand their customers better, build better products and services, and thus, boost business growth.
How are insurance companies using data?
Insurers can use data to gain insights from customers’ profiles. They can review their history, behavioral pattern, and marketing needs to develop strategies and provide marketing services.
Core Insurance, Risk Management
Article | September 22, 2022
As the COVID-19 pandemic continues, we are learning to live with it and mitigate its risks. While older adults have suffered disproportionately from the health impacts of COVID, they have also suffered from the effects of efforts to control its spread.
Infection rates rose in recent months, and many long-term care facilities again closed their doors to visitors. This left many families separated from elderly and disabled loved ones during the holiday period.
Article | July 15, 2022
Despite economic pressures on reinsurers and cedants, nearly all buyers were able to secure coverage during the reinsurance renewal period. However, attachment levels and the cost of ceding risk were higher than most buyers desired, and supply constraints in some lines and territories caused stress not seen in years. As a result, according to Gallagher Re's latest 1st View renewals report, the reinsurance market has maintained its firming trend.
Despite mostly positive H1 2022 results, the combination of inflation and rising interest rates has caused reinsurers to adjust their balance sheets and reserves while also taking into account how a recessionary environment may increase claims frequency.
These economic factors, combined with sustained loss levels, allowed reinsurers to maintain upward pricing pressure as they sought to reduce their appetite for volatility.
Key Contributions to Understanding:
Natural disaster capacity decreased overall as reinsurers continued to shift away from low-level layers, which differed by country and region.
Reinsurers were seen assessing cedants' inflation-related actions and applying carefully calculated loadings to relevant treaties.
The Russian invasion of Ukraine increased interest in cyber and war contract provisions.
Long-tail casualty placements remained popular among reinsurers, but there was more debate about ceding commissions than in recent renewals.
Higher ILS risk transfer prices have attracted net new capital, but this has not resulted in market softening.
The inflation discussions have been detailed and technical, with reinsurers eager to challenge cedants' model outputs. Most reinsurers are assessing reserve adequacy as interest rates rise, in addition to their concerns about primary rate adequacy in the new inflationary environment.
They are experiencing effects simultaneously on the asset and liability sides, which has strengthened their resolve to maintain the pricing momentum of the previous two years.
Article | March 29, 2022
As AI becomes more deeply integrated into the industry, carriers must position themselves to respond to the changing business landscape. Insurance executives are expected to understand the factors driving this shift and how AI in insurance will impact claims, distribution, underwriting, and pricing. They can start to learn the skills and talent they need, embrace new technology in the insurance industry, and build the culture and perspective they need to be successful in the future insurance market with this grip.
While there are four types of levers that might help with productivity efforts—functional excellence, structural simplification, business transformation, and enterprise agility—insurers typically focus on the first two. Those levers are the foundation of efficient and effective operations, it isn't easy to leapfrog them. Traditional industry barriers are dissolving while technology advances and customer expectations vary dramatically. Ecosystems, which are groups of services that work together in a single integrated experience, are becoming more common across industries. Platforms that connect offerings from different industries are also becoming more common.
In an interview with Media 7, Darcy Shapiro, COO of Americas at Cover Genius, talked about the changing expectations of consumers in the insurance industry.
“Consumers expect brands to provide the same high-quality day-to-day experiences directly within the digital platforms they use most. Insurance should be no different.”
Darcy Shapiro, COO of Americas at Cover Genius
The Increasing Acceptance of Parametric Insurance
In contrast to traditional policies, which are paid based on actual loss incurrence, metric insurance has been around for a while, providing payouts when a specific event exceeds an agreed-upon threshold. Previously being used specifically for natural disaster coverage and supplied to countries and large corporations, parametric insurance is making a comeback today. Advancements in sensor technology, data analytics, and Artificial Intelligence (AI in insurance) create broader information indexes on various levels, which opens up parametric risk applications in novel ways.
A reinsurance company recently introduced a parametric water-level insurance product to shield businesses from the financial consequences of high or low river water levels. The program considers measured water levels at specific river gauges and agrees to pay a fixed amount for each day that the index remains below a predetermined threshold value. Other new-generation parametric solutions include terrorism protection for cities and airports, protection for retailers when transit strikes cut down on pedestrian traffic, and help for hotels when there are outbreaks.
The advantages of parametric insurance include faster delivery and avoiding lengthy claims investigations. Furthermore, since parametric products have less uncertainty than traditional insurance, premiums can be significantly lower. In terms of technology, parametric insurance is best suited to blockchain technology, with smart contracts that pay out automatically when certain parameters are met.
A Flood of Data from Connected Devices
Fitness bands, home assistants, smartwatches, and other smart devices are rapidly becoming a part of our daily lives. In addition, smart clothing and medical devices will soon join the fray.
Sensor-equipped equipment has long been common in industrial settings, but the number of connected consumer products is expected to skyrocket in the coming years. Existing gadgets (such as automobiles, fitness trackers, home assistants, smartphones, and smartwatches) will continue to grow. In contrast, new and expanding categories (such as clothing, eyewear, home appliances, medical devices, and shoes) will join them. According to analysts, interconnected devices will reach one trillion by 2025.
The data generated by these devices will result in a flood of new data that carriers can use to understand their customers better, resulting in new product categories, more customized pricing, and an increase in real-time service delivery.
The insurance industry can mine the data generated by these smart devices to better understand their customers’ preferences. This information can also assist insurers in developing new and more personalized product categories.
The Rise of the Insurance Ecosystem
According to McKinsey, insurance ecosystems will generate 30% of global revenue by 2025.
With an expanding array of data sources and a data-driven culture, many insurers will soon be able to plug into and exploit data from complementing firms. These agreements are evolving to involve traditional insurers as well as technology companies. For example, an insurance firm in Europe teamed up with a smart-home technology vendor to improve its home insurance. The latter's technology can detect smoke and carbon monoxide, preventing losses. In addition, a global initiative of a major reinsurance company is developing an ecosystem for InsurTech start-ups and digital distributors. Recent McKinsey research also shows that the insurance business has been having a hard time making efficiency gains for a long time.
Moreover, the operating expense disparity between the best and worst performers in P & C and life has widened over the last decade. Functional excellence, structural simplicity, business transformation, and enterprise agility are four productivity levers that insurers often focus on. Those levers are essential to efficient and productive operations. Ecosystems, which are groups of services that work together, are formed across industries and platforms that connect offerings from different sectors.
Insurers may use ecosystems to integrate their products into seamless client experiences. Ecosystems are essential in today's interconnected world, whether you want to build direct relationships with customers or work with companies that act as the customer interface.
Advancements in Cognitive Technology
Cognition is a critical component of AI in insurance. AI cognitive technologies mimic how the human brain functions. In addition, new technology may make it easier to process huge amounts of data, especially from active insurance products that are linked to specific people.
Carriers can constantly learn and adapt to the world thanks to cognitive technologies. As a result, it can enable insurance companies to introduce new product categories and engagement techniques and respond in real-time to changing underlying risks. In addition, convolutional neural networks and other deep learning technologies, which are currently used primarily for image, audio, and unstructured text processing, will be used in various applications in the future of insurance industry.