Articles - Health insurers utilising analytics for profitable growth

 

Analytics can significantly enhance your new product and proposition design process. Take lower-cost entry-level plans that offer access to primary care services, such as virtual GPs or physiotherapy benefits. These can provide a gateway for new private health insurance customers in markets where public health systems’ primary care services are stretched. While entry-level products typically have tighter margins in absolute dollar terms, by analyzing the business mix and utilization compared to expectations when pricing, you can protect profits while capturing new customers.

 

 

From a product perspective, modeling can drive evidence-based decisions on enriching benefits with access to cutting-edge diagnostics and treatments. Analytical insight lets you evaluate whether access to the latest technologies will drive customer benefit at point of claim or simply drive up prices through a higher-cost way to achieve a similar outcome when compared to more traditional diagnosis and treatment approaches.

 

Modeling can also show whether new technologies are likely to have significant health benefits that outweigh the additional costs. Let’s say analysis shows a new technology is likely to be high cost relative to the incremental benefits. Positioning products as supplementary high-value riders that provide access to the latest cutting-edge medical care in areas such as precision cancer treatments can enable you to protect the core book from rising medical inflation while still enabling access to new treatments.

 

When it comes to pricing, there are several analytical levers you can use to enhance performance, such as better granularity of analysis, advanced modeling methods and more sophisticated retail pricing approaches.

 

You can deploy machine learning techniques such as gradient boosting machines (GBMs), which identify granular non-linear drivers of risk, for detailed risk-cost modeling and informing new pricing factors to deliver competitive advantage. Even for community-rated schemes and in jurisdictions where there are restrictions on the individual pricing factors permitted, having a deeply granular risk-cost model can give your critical portfolio-level insights. With a clearer view of what’s driving claims experience, you can set the community rating at a level that’s appropriate and sustainable.

 

Pricing optimization, where permitted, is also a valuable additional layer on top of risk-cost pricing to maximize profitability and competitiveness. Price testing can help you assess the real-world impact on sales of different price points or downgrade strategies. By testing or flexing different price points, you can analyze customers’ price elasticity to forecast the impact on sales.

 

Techniques like scenario testing and sensitivity analysis can explore the pricing impact of cost containment measures, such as changes in outpatient limits or excess levels from both technical and customer behavioral perspectives. You can visualize the impact of product and benefit changes on key metrics, such as new business, retention and profits.

 

And when regulatory or taxation changes trigger significant upward pressure on rates, modeling the effects on pricing and the corresponding impact on retention helps establish proactive product downgrades or other mitigating strategies to retain customers.

 

Analytics can inform more sophisticated market segmentation approaches. For example, machine learning techniques can identify previously undetected non-linear market segments with high sales value. You could also uncover certain brokers or sales agents within key distributors with high penetration within different regions for key age groups. Guided by machine learning analytics, you can uncover the true value of these granular segments and continue to target them as key contributors to new business.

 

By checking the profitability of these segments against expectations, you can also avoid rapid new business expansion in loss-making segments.

 

While the lag between policy sales and claims can make monitoring the claims performance of these segments challenging, you can get early-warning indicators of poor performance using analytics. By overlaying predictive monitoring onto portfolio factors such as underwriting and demographic mix, as well as other data sources such as pre-authorizations data, areas of concern can be flagged early.

 

Analytics can also proactively suggest products directly to customers and brokers using data-driven insights on what will appeal to certain customer demographics more than others, from anti-obesity drugs and maternity care to precision cancer care benefits.

 

Identifying or recommending products more likely to match customers’ deep health needs in this way can strengthen sales strategies and boost revenue.

 

With the wealth of opportunities available for analytics to create ROI across the insurance value chain, prioritization based on which initiatives will have the highest impact for your organization is critical.