Article | July 15, 2022
Do you know what the UK insurance industry is going through? A disruption that calls for complete metamorphosis. Not so different from what the whole world is going through at the moment. Crafting one-size-fits-all products and expecting them to sell like hotcakes is a huge misconception. Customers want products to be as personalised as possible. Pay per mile insurance or lower car insurance premiums for safe drivers are some examples. In the current global crisis, personalised life insurance would look like factoring in the unique health/ living conditions of the person and then providing insurance options.
Article | July 19, 2022
Insurtech is advancing, and the significance of an effective policy management system cannot be underrated. Policy management professionals understand the payoff it offers to an organization. On the other hand, a policy management system that just isn’t a good fit can prove to be a lot more expensive than previously budgeted.
So what is it actually costing you? Is your policy management software updated, or are you still using an old version? Do you know how much it is hampering your financial productivity? Even then, often, an outdated system may not be affecting your process significantly but damaging it in other intangible ways that are just as crucial to business success. Analyze your current system for the following:
Financial Implications of the Current System
Manual processes for policy creation and management make up the costliest part of running a policy management system. Paper-based solutions incur high costs that can be easily avoided by using digital systems that use automation extensively. With thousands of policies and compliance procedures for your team to manage, costs can add up quickly, especially with printing and distribution costs.
In addition to these expenses, manual processes are also responsible for policies being misplaced or lost. It may also result in a large fine for noncompliance if some policies are accessible to unauthorized employees.
Organized policy management procedures are critical for high operational efficiencies. Policy management systems that require manual supervision can prove to be expensive over the long run as they require employees to monitor them constantly. However, automated policy management systems enable policy teams to optimize their resources better and direct team members to speed up other more crucial processes.
Furthermore, modern policy management systems don’t need constant monitoring and require only a one time set-up. This enables teams to allocate resources where they are urgently needed.
If you have an outdated policy management system, chances are it takes a lot more micro-managing than it needs to. Businesses must be able to optimize their resources better but with old and outdated systems, it ends up cutting into the productivity and performance on an everyday basis.
In addition, it puts undue stress on employees to keep up with compliance norms and changing regulations and policies. Policy management often requires various employees to pitch in with their inputs, and using an old system that doesn’t offer the option to collaborate can take away a huge chunk of productivity daily.
What’s the Bottom Line?
Automated policy management systems can undoubtedly save you a lot of time and resources. If you’re facing sky-high costs just to maintain your policy management system, it might be time for a rethink. From automating the lifecycle of policies and procedures to streamlining the management of policies by your agents, consolidating a policy management process with software is one of the best insurtech trends to look out for in 2023. It is probably what your organization needs to move the needle.
Article | July 14, 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.
Article | July 12, 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.