Article | July 15, 2022
Artificial intelligence (AI) has changed the insurance industry – and customer service is no exception. One of the most common forms of AI are the use of chatbots, which Forbes defines as “software functionality that is designed to receive conversational input through text of voice and then generate a response that is also in natural language.” In other words, instead of interacting with a human, you’re “chatting” with a bot that’s programmed to understand your questions and direct you to the right place.
Article | August 9, 2022
With the major impact of the COVID-19 outbreak, contractors appreciate the need for insurance coverage even more. You may be safely covered by Force Majeure and pandemic clauses in your policies. However, you may still be wondering how to deal with the associated costs related to the COVID-19 outbreak risks. In this article, let’s look at some of the steps you can take to handle your insurance position during the pandemic.
Article | July 15, 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 13, 2022
In the insurance industry, artificial intelligence (AI) has become a buzzword. Nonetheless, despite the fact that we are still in the early stages of AI implementation, the industry has made significant progress.
The Need for AI in Insurance
Insurance is a long-established and highly regulated industry. Perhaps as a result, insurance companies have been slower to adopt technological change than other industries. Insurance is still dominated by manual, paper-based processes that are time-consuming and necessitate human intervention. Even today, customers must deal with time-consuming paperwork and bureaucracy when filing a claim or enrolling in a new insurance policy. Customers may also pay more for insurance if policies are not tailored to their specific needs. Insurance is not always a pleasant customer experience in an age when most of our daily activities are online, digitized, and convenient.
Having said that, we are beginning to see a global push by insurance companies to enhance their technological capabilities in order to do business faster, cheaper, and more securely. There have been several notable examples of insurers investing heavily in Artificial Intelligence solutions in recent years.
If AI technology is fully applied to the insurance industry, McKinsey estimates a potential annual value of up to $1.1 trillion.
How are insurers implementing AI?
There are numerous examples of insurers around the world using AI to improve both their bottom line and the customer experience. There are also a slew of start-ups offering AI solutions to insurers and customers. I'll discuss a few interesting cases here.
The Future of Artificial Intelligence in Insurance
AI has the potential to transform customers' insurance experiences from frustrating and bureaucratic to quick, on-demand, and more affordable. Customized insurance products will attract more customers at lower costs. If insurers apply AI technology to the mountain of data at their disposal, we will soon see more flexible insurance, such as on-demand pay-as-you-go insurance and premiums that adjust automatically in response to accidents, customer health, and so on.
Insurance will become more personalized as insurers use AI technology to better understand what their customers require. By accelerating workflows, insurers will be able to save money. They will also discover new revenue streams as artificial intelligence-driven analysis uncovers new business and cross-selling opportunities.