How Predictive Analytics Software Can Help Transform the Insurance Industry

Below is the continuation of thetranscript of a Webinar hosted by InetSoft on the topic of Data Analytics in the Insurance Industry. The presenter is Natalie Chan, principal at TFI Consulting.

Well, good morning everybody my name is Natalie Chan. I'm delighted to be here today to talk to you about how predictive analytics can help transform the insurance industry. My background is in mathematical modeling and statistics, I have over 15 years experience of working in areas where these skills are applied in a practical setting.

I'm extremely passionate about using data to try to support evidence based decision making, and I've been working with organizations across a wide variety of sectors insurance, retail, banking and telcos to basically build and execute analytical strategies to ensure the success implementation of data driven and analytics.

I'm here today to talk to you about how predictive analytics can improve your customer's experience, increasing customer satisfaction and reduce cost. You might wonder why I want to focus on the customer experience. Well most of the analytics employed in the insurance industry is focused on identifying or reducing fraud, estimating and managing risk, and on improving customer retention.

However, reports from the insurance industry consistency highlight that the quality of customer experience remains the biggest factor driving customers to remain loyal or to switch to another insurance provider. We should focus on how to improve the quality of the customer experience rather than focusing solely on fraud.

Here on the slide before you I am presenting a report which essentially states that those companies who excel in delivering a quality customer journey tend to win in the marketplace. Those with higher journey satisfaction scores of between an 8.8 and 9.2 reflect a much greater revenue growth compared to those with scores of between 6 and 8 present. I think it speaks for itself in that particular case.

Essentially we're talking about predictive analytics today because it works. It drives return on investment, and it's effective when measured across different areas of insurance organization. On the screen before you here you will see some examples of success stories associated with the use of predictive analytics in the insurance industry. For example, there in the bottom left you can see that Infinity Insurance increased separation recovery by 12 million dollars. In fact they actually also manage to increase their fast track rates from 2% to 22%.

What is predictive analytics? Very often in business we can ask ourselves the following questions, we can kind of consider what happened, why did it happen, what will happen and what should I do next. We would essentially consider answering the questions of what happened and why did it happened to be business intelligence. Many of you are probably doing some of that already. We would then consider answering the questions of what will happen, or what should I do next, to constitute advanced analytics. This is where leading insurance companies have a focus essentially.

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This might sound a bit similar to the reference earlier to the fourth stage of analytics. What we have here is a view from a data implementation perspective. What does the predictive analytics insurance company looks like? The predictive analytics insurance company ensures that analytics is driven at an enterprise level. In this way predictive analytics can be embedded across the organization in a variety of ways. Now, earlier it was mentioned that the analytic insurers use analytics throughout the organization, so essentially that's what we're talking about.

For any one customer we can obtain data from a variety of sources, online, telephone, face-to-face among others. This information can be descriptive information which might include demographic information or claim history. It can be attitudinal information in the form of complaints or surveys, or it can be some sort of external data like weather data or social media data. There can be a variety of sources of data and then each data source can essentially be mashed up to give a 360 degree view of the customer. Using predictive analytics we can enhance a range of engagements from the claims process to customer retention, to supplier analysis among others.

Predictive analytics is revolutionizing the insurance industry by providing valuable insights into customer behavior, preferences, and risks. When applied effectively, predictive analytics can significantly enhance customer retention strategies, ultimately leading to improved profitability and long-term business success. Here's how predictive analytics can benefit customer retention in the insurance industry:

1. Identifying At-Risk Customers: Predictive analytics models can analyze vast amounts of customer data to identify patterns and trends indicative of customers at risk of churn. By examining factors such as policy usage, claims history, demographic information, and interactions with the company, insurers can pinpoint customers who are likely to switch to competitors. Early identification allows insurers to proactively intervene with targeted retention efforts, such as personalized offers, proactive customer service, or policy adjustments, to prevent attrition.

2. Personalized Offerings and Recommendations: Predictive analytics enables insurers to create highly personalized offerings and recommendations tailored to individual customer needs and preferences. By leveraging data on past interactions, policy coverage, life events, and market trends, insurers can anticipate customers' evolving needs and offer relevant products or services at the right time. Personalization enhances customer satisfaction and loyalty by demonstrating a deep understanding of their unique circumstances and providing solutions that meet their specific requirements.

3. Risk Segmentation and Pricing Optimization: Predictive analytics allows insurers to segment their customer base more effectively based on risk profiles and behavior patterns. By categorizing customers into risk segments using advanced algorithms, insurers can adjust pricing, coverage options, and underwriting criteria to align with the risk associated with each segment. This ensures that customers receive fair premiums based on their individual risk levels, reducing the likelihood of price dissatisfaction and increasing retention rates.

4. Proactive Risk Management and Loss Prevention: Predictive analytics can help insurers anticipate and mitigate risks before they escalate into costly claims or customer dissatisfaction. By analyzing historical claims data, market trends, and external risk factors, insurers can identify emerging risks or patterns indicative of potential losses. Proactive risk management initiatives, such as targeted loss prevention programs, safety recommendations, or policy endorsements, demonstrate the insurer's commitment to protecting customers' assets and reducing their exposure to risk, thereby fostering loyalty and trust.

5. Optimizing Customer Interactions and Communication Channels: Predictive analytics enables insurers to optimize their customer interactions and communication channels based on individual preferences and behaviors. By analyzing data on past interactions, communication history, channel preferences, and response patterns, insurers can personalize their outreach efforts to engage customers effectively. Whether through email, phone, mobile app, or social media, insurers can tailor their communication strategies to resonate with customers and strengthen the relationship, ultimately increasing retention and lifetime value.

6. Enhanced Claims Experience and Resolution: Predictive analytics can improve the claims experience by streamlining the claims process, accelerating resolution times, and enhancing customer satisfaction. By analyzing claims data, customer feedback, and operational metrics, insurers can identify bottlenecks, inefficiencies, and pain points in the claims handling process. Predictive models can predict claim complexity, estimate claim settlement times, and allocate resources more efficiently to prioritize high-value claims or customers in distress. By delivering a seamless and transparent claims experience, insurers can build trust and loyalty with their customers, driving higher retention rates.

7. Anticipating Life Events and Policy Lifecycle Management: Predictive analytics enables insurers to anticipate significant life events or policy lifecycle milestones that may impact customer behavior and retention. By analyzing data on life stage changes, policy renewal patterns, and customer life events, insurers can proactively engage with customers to provide relevant guidance, support, and coverage options. Whether it's a marriage, birth, home purchase, or retirement, insurers can offer timely advice and solutions to address evolving insurance needs, strengthening the bond with customers and increasing retention over time.