As the need for digitization gets pervasive, many insurance firms want to be more present in their customers’ lives. They are working in this direction through medical-advice mobile apps, tracking drivers’ braking behaviors, and equipping factory workers with wearable devices, among other things. It is undeniable that, especially in these times, consumer satisfaction is a critical factor in a company’s success. Customer happiness ultimately reflects how a consumer feels about interacting with a brand.
Customer experience and service are critical for any insurance company as they provide insights into trends that allow them to make more informed decisions. Within the realm of insurance, customer behavior analysis provides insurers with information about their customers’ behavior and practices. In this digital age, putting the customer first through automation and digitization helps put your brand at the forefront of customer engagement and recall.
According to a Celent 2021 poll, improving customer experience is a top priority (63%) for insurers. It aids a company’s growth by delving deeply into customer engagement with its market-provided products and services. Analyzing customer behavior enables customer acquisition, which leads to profitable lifetime potential.
Customer segmentation plays a huge role
Market segmentation is critical in the insurance industry to improve customer acquisition. Its typically done by tailoring services to the preferences of specific customer types and demographics. All this is to ensure better engagement for a broader range of customer groups. The goal is to provide more reliable insights to insurers so they can create relevant offers that provide high-value experiences, as well as to identify risks or opportunities and generate timely effective responses.
A customer-centric segmentation needs companies to look at the lifestyle of the customer and not categorize them based on parameters such as age, gender, color, and income to create a customer profile.
Advantages of using predictive analysis
Predictive analysis is the process of making predictions and identifying patterns using various methods, including AI, statistics, machine learning, and data mining. These analytics can increase customer satisfaction, reduce claims costs, and improve key KPI performance, and customer attrition.
It can help in risk and fraud detection by implementing transparency to improve the accuracy of the risk model. Claim management is another area where these analytics can help by identifying and sending automated alerts of potentially high-cost losses to alert claim handlers.
Usage of insurance data analytics
Data and analytics prove crucial in getting accurate insights into whether or not a customer is a retention risk. Also, what actions should be taken to mitigate that risk. Analytics help firms predict potential leads, design customer behavior-based products, and deliver enhanced customer experiences. They help create actionable insights for higher engagement levels and targeted marketing. Insurance data analytics helps gain deeper insights, make strategic data investments, and record engagement behavior on the insurer’s platform. All this while optimizing each interaction with every policyholder.
Behavior tracking mechanisms
These are real-time device trackers that monitor and collect individual-level user behavior data. This data is used by insurers to provide customized price discounts, products, and services. It is only possible if users give the manufacturer permission. In car insurance, the company employs a telematics program that provides feedback on driving behavior, and in life and health insurance. It employs a fitness tracker.
Once the data is received, it can automatically help clients to manage their premiums. They can even offer customers health coaching services and generate policies based on the user’s car age, model, mileage, life, etc., by integrating AI and ML technologies.
Importance of sentiment analysis
Sentiment analysis in insurance uses Natural language processing (NLP), Machine Learning (ML), and deep text analytics. These technologies help exactly understand the end customer’s emotional patterns in written or spoken text in real time. For any firm eager to improve operations assimilating customer feedback is instrumental. This analysis is received as unstructured data from different channels like voice messages, emails, surveys, call center logs, and more. They are then acted upon to improve retention rate, identify trends, and cross-sell or upsell opportunities.
Collecting qualitative data
This data enables the companies to see behind the numbers. Knowing the customer’s perspective and leveraging this data helps uncover what drives their behavior and their journey map on a particular platform. This can highlight some areas to refine the results and double-check the possible trends and new models. By analyzing past customer behaviors in insurance, like purchase history, one can identify habits and align strategies to match their behavior journey.
Key benefits of incorporating customer behavior analysis in insurance
● Using data to ensure a personalized experience, by embedding AI technology and meeting customer requirements on the right channels. To personalize the services, the firm requires data, and 75% of customers to agree to share their data to get cheaper premiums and personalized offerings, according to BusinessWire. It increases the customer lifetime value (CLV) by understanding the value of customer sentiment.
● This can help to increase conversion rates and customer retention once the company knows the buying and purchasing behavior of the customer. Attention to detail should be given to products or services that can solve customer queries as fast as possible. It can help to attract new customers but also retain existing ones. This enables more precise target strategies to attain the right customers for the company’s marketing.
● Spotting new market trends or predictions, ahead of rivals, and being a step ahead to invest in a new offering. Knowing evolving market trends can help brands identify and fill gaps by analyzing customer behavior. This can not only help finalize an ideal production strategy but also reduce costs.
● Customer behavior analysis in insurance involves understanding how customers differentiate themselves from each other. This can help bring tangible improvements to your customer service. Better service leads to more sales which results in the growth of the company at different levels. 80% of customers mentioned the experience a company provides is as important as its product/services.
Customers in this era of the digital revolution are expecting fast-paced results in this increasingly competitive marketplace. Insurtech startups are disrupting the current insurance model with innovative ideas and wider use of technology infrastructure. While making strategic decisions on risk selection and pricing strategies, behavioral analytics with automation can be a critical differentiator.
Given the abundance of digital opportunities, the focus should be to increase the efficiency of internal process models and eliminate fraud detection. It’s wise to use data from insurance companies to support customer-centric models for a seamless experience. To wrap it up, we expect this industry to grow on the back of innovation and technological advancements in the coming future.
Netscribes offers competitive intelligence and market research to help insurance companies stay ahead in the face of new market entrants and changing consumer expectations. To learn how we can support your business, get in touch.
Based on insights by Chahat Khatter, Manager, Research and Information, BFSI.