In his book "Predictably Irrational," Behavioral Economist Dan Ariely reveals the curious interconnectedness between two forces. First, that people's choices are influenced by their emotional or "hot" states, and are often irrational. Second, that this irrationality is oddly systemic, and forms patterns — suggesting that, if studied closely over an extended period of time, they could be predicted. In the insurance world, events that trigger a claim are examples of hot emotional states for a claimant — meaning ones in which they may be predisposed to act irrationally. Combine this interesting insight with the growing belief that the nature of the claim experience can either make or break a customer's relationship with an insurer, and all of a sudden, it seems to makes sense to preemptively understand how people react to a claim incident, and then get ahead of them to service them accordingly so that they are not just satisfied, but pleased every step of the way.
The Customer Satisfaction/Loyalty Connection
The case is simple. Research conducted separately by J.D. Power and Associates and Diamond establishes that customers who have a positive claim experience are significantly more likely to remain with an insurer for a longer time and become "evangelists" so to speak in their circle of influence. Over time, this creates tremendous value for the insurer. Yet, when it comes to tightening purse strings in tougher times, claims is the area that insurers target shrinking the most. A recent study by Arthur J. Gallagher & Co. revealed that "it now takes insurers significantly longer to settle large claims than in the past," leading to frustrated clients and tremendous inefficiencies in the claim process. This Catch-22 situation will persist, as pressure on reducing losses is critical to insurers' survival.
So, the real-world claim challenge lies in concurrently minimizing losses while growing customer satisfaction. But is this really possible? The answer is "definitely." Somehow, these two factors are perceived to be starkly opposite forces that cannot coexist. However, the fact is that many insurers may have missed an opportunity to manage losses proactively in tandem with the pulse of their customers.
While losses and customer satisfaction seem like awkward bedfellows, the only reasonable way to develop a true win-win between the two is to create such incredibly smart intelligence around customer behavior at the lowest possible level of granularity that not only can you, the insurer, significantly reduce losses, but also create very happy customers.
Develop intelligence to predict customer behavior. There are four aspects to building an intelligent, responsive information environment in which it is possible to reduce losses while efficiently managing claims and improving customer satisfaction.
Assess claim processes through ethnographic analysis. Look within your own claim organization to identify potential root causes of claim dissatisfaction. Carry out ethnographic observations of the process by immersing yourself in each stage and watching how a claim is handled. For instance, a leading P&C insurer engaged its entire claim organization in creating an unusual way of understanding value chain linkages. The insurer drew very detailed, lifelike knowledge maps. This visualization process helped the company develop mental maps of clear causalities, roles, and dependencies. In addition, it structured a claims university to continually generate proactive ways of understanding the claimant. As a result, the insurer's claim department has won several customer service awards and has the lowest loss ratios in the top ten: more than 5 percent lower than the next best performer.
Leverage behavioral economics to peel back unexplored drivers. The real power of relating to people lies in reaching the deepest part that often unwittingly motivates their behavior. For instance, because an accident is a hot irrational state that sets off unconscious responses of which claimants may be unaware at a surface level, insurers should focus on these patterns. Insurers should study how they can predict customer reactions to claim-related triggers and how to customize services accordingly.
This concept can be extended further to the advantage of insurers, where simple human motivational triggers could induce desirable behaviors to thereby reduce claim payouts from the outset. A behavioral economics experiment at MIT found that a simple reminder of moral code caused cheating in exams to plummet. Taking the lead from this handy tip, a leading personal lines insurer simply, yet cleverly shifted the "promise of truth" signature of the claim form — from the bottom to the top of the form. The insurer then witnessed a dramatic drop of more than half in claim fraud.
Yet another simple method to boost customer satisfaction is to play on the inherent attractiveness of anything labeled "free." In the claim space, offering risk control or loss advice gratis can increase satisfaction and perceived value while equipping the insurer with access to rich, unique customer data. Additionally, the cash benefits to insurers can be huge. For example, based on a relationship built from a casual conversation during a loss-prevention advisory visit to a steel mill, the security officer called the insurer's claim department when he heard an unusual rhythm in the machinery. In the end, this prevented a large-scale loss that could have cost the insurer $2.4 million in losses.
Granular Segmentation and Predictive Modeling
A customer segmentation model that leverages all of the above insights as key predictive variables can create a granular understanding of specific customer needs from a claim experience. In addition, this level of segmentation could help optimize service delivery — from the exact desired value to the specific customer segment at the lowest possible cost to the insurer, as shown in Figure 1 below. When engineered effectively, this could lead to incredible internal efficiencies for the insurer — thus talent is focused where needed, and the company can cater investments to specific improvements in operations — while still ensuring that customers are getting exactly what they need.
Real-Time
Preventative Diagnostics
Real-time information could be fed to claims/risk prevention functions so that early warnings of an event could trigger action to reduce or even prevent it from occurring. For example, inexpensive devices in a house could detect early symptoms of water damage or mold in walls.
Furthermore, insurers can save millions in catastrophe-related expenses by reacting to early triggers from real-time devices placed along river beds rising to precarious levels; or by installing system monitoring programs in nuclear power plants that detect very early breakages in processes that prompt immediate alerts. These investments help cultivate a pool of deeply entrenched customers by providing savings to them from avoidance of business disruption, penalties, lawsuits and other damages.
One personal lines insurer applied real-time diagnostics to deal with claim fraud. The insurer created a rigorous analytics engine for "search word" patterns online, to understand the occurrence of falsified behavior based on the nature of search patterns traced to certain geographies, and accordingly to preempt it.
Weaving these factors together not only brings the insurer closer to the claimant but also helps the company profit internally. Insurers must ask themselves if the claimants are merely irrational, or if they are in a position to predict irrationality. They must then use that knowledge to design the right experience for customers and the right results in terms of profitability.
Punita Gandhi, principal, Anand S. Rao, partner, and Jamie Yoder, insurance practice managing partner, are part of PwC's Diamond Advisory Services. They may be reached at www.diamondconsultants.com.
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