Insurance Technology
Article | July 14, 2022
Americans consider boosting the economy a top policy priority over dealing with COVID-19 as the coronavirus outbreak enters its third year.
The decrease in the percentage mentioning the pandemic has been particularly sharp: from 78% last year to 60% this year, dealing with the coronavirus is now seen as a top policy priority. This comes at a time when Americans see various issues as lower priorities than they did a year ago.
Republicans and Democrats disagree on the significance of the majority of policy priorities, but for 11 of the 18 issues covered by the survey, the partisan divide has grown significantly. This includes double-digit increases in partisan differences on addressing issues like immigration, the political system, improving the job market, and the criminal justice system.
Changing Public Priorities: The Economy, Coronavirus, Jobs
The percentage of Americans, particularly Democrats, who see the economy as a significant policy issue has decreased, despite the fact that it still ranks first on the public's list of priorities. From 75% a year ago to 63% now, the percentage of Democrats and independents leaning toward the Democratic Party who believe that improving the economy should be a key priority has decreased.
Republicans and GOP learners, meanwhile, have seen almost no change in their opinions (85%top priority then, 82%today).
Democrats are also less inclined than they were in January of last year, before President Joe Biden's inauguration, to rank addressing the employment situation as their top priority. 71% of Democrats said jobs should be a primary priority a year ago; today, only around 50%of Democrats agree (49%). The Republicans' slide has been more subdued (from 63% to 55%).
As a matter of policy, solving the issues of the poor has lost priority. Democrats continue to prioritise this policy area significantly more than Republicans, although Republicans are now less likely than Democrats to see dealing with the issues low-income families confront as a key concern (25%now vs. 35%then; 58%now vs. 68%then).
Additionally, there has been a reduction in the public's opinion that strengthening the political system ought to be a major priority for policy, mostly due to Republican efforts. The proportions of voters in each party who said that reforming the political system should be a high priority were essentially the same as they were the previous year (64%of Democrats and 60%of Republicans). Now, only 40% of Republicans and 61% of Democrats believe that this should be a high priority.
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Insurance Technology
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
Marketing
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
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
Lead segmentation
Sales funnel optimization
There is enormous value in optimizing productive data by focusing on prospects likely to become loyal customers.
Operations
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.
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Insurance Technology
Article | August 9, 2022
It is common knowledge that there is virtually no industry that has been immune to the effects of COVID-19. The global pandemic has caused massive shifts in individual and industrial behavior and will continue to do so in the months, if not years, to come. The P&C industry, like many others, is reeling from the effects of the virus. Amidst all these events, it is important to assess how the insurtech industry is going to be affected by COVID-19.
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Insurance Technology
Article | July 14, 2022
The world is changing at a rapid pace, and no industry is immune to the need to evolve, upgrade, and innovate. The effects of mass digitization, artificial intelligence, machine learning, climate change, and the rise of financial-based cybercrime are all being felt in the business world. At the same time, consumer expectations have shifted dramatically, thanks in large part to companies like Netflix and Amazon, which have the technology and business models to provide the instant access to products and services that today's consumers have come to expect. When these changes are considered, it becomes clear that no industry, not even one as traditional, robust, and stable as the insurance industry, can afford to stand still.
Trend 1: CARE-Based Distribution Channels
Insurance companies are engaged in a "digital arms race," rushing to equip their distribution channels with digital tools to improve customer experiences. While CARE is the core experience that most insurance companies strive to provide in both distribution and sales, few achieve it consistently.
Trend 2: Quicker Payouts
Pay cycle time is fast becoming one of the most important differentiators between insurance companies. The winners of the future will use insurance technology to help them resolve claims quickly, at the touch of a button.
To this end, companies are adopting AI-enabled tools to automate both estimation and inspection. Telematics insurance solutions are expected to provide greater levels of contextual information that will support the smoother, faster, and more comprehensive settlement of claims.
Trend 3: The Rise of Usage-Based Models
As the pandemic made consumers aware of the waste involved in paying for insurance on cars that sit unused in driveways, interest in usage-based insurance products skyrocketed in 2021. As the nature of work changes and many people's daily commutes become obsolete, winning insurance companies will offer products that are more in line with how their customers live today. Telematics devices will allow insurers to offer products based on how and how far users drive.
Trend 4: Intelligent Automation
For a long time, the insurance industry has been experimenting with automation. The first phase was robotic process automation (RPA), which was viewed as a way to speed up processes and reduce costs without requiring significant changes to the underlying applications. While this was effective at capturing low-hanging fruit—those ubiquitous repetitive steps that were an unnecessary feature of so many insurance processes—it never really attacked productivity and core functions that required automation.
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