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Tapping AI to Improve Policyholder Experience

By Chris Small, VP of Product – Roadside Managed Services, Agero and

Chris Richard, VP, Contact Center Operations, Agero

 

Artificial intelligence and machine learning are increasingly moving from the lab or back-office to having a strong impact on the daily front-line interactions of consumers and companies, in particular by allowing businesses to better anticipate and respond to customer needs. The latest example in the insurance industry comes in the form of policyholder interaction with roadside assistance programs.  Agero, one of the leading providers of roadside protection delivered through insurance policies, provides this technology through its Command Center managed service, which leverages machine learning to proactively combat the impact of traffic, weather and other factors that cause unexpected delays in delivering driver assistance. A number of companies, including four of the top six auto insurance providers, have already adopted the technology to cover millions of U.S. motorists.

A Key Policyholder Touchpoint

Roadside assistance is a key touchpoint for many insurers – beyond filing a claim, it might be one of the few opportunities they have to interact with a policyholder. Data shows that a positive experience can dramatically improve customer sentiment, leading to increases in Net Promoter Scores (NPS) and directly impacting policy renewals. Of course, the converse is also true, with bad experiences having the potential for a long-lasting negative impact. According to PwC, “one in three consumers (32%) say they will walk away from a brand they love after just one bad experience.” As a result, it’s critical to place significant emphasis on mitigating the threat a poor customer experience can have against an organization and the relationship between the insured and the insurer. If handled at all, this process usually takes the form of post-event escalation to customer service staff to resolve issues and ensure exceptional follow-up care.

The challenge in implementing this and similar types of escalation is that it happens after the fact – after a bad experience has already occurred, putting the brand on the defensive in winning the customer back. Traditionally, that decision is handled by either a manual or a rules-based process that identifies and escalates particular service interactions for additional attention – in the case of roadside assistance, that means delayed service delivery once the tow provider was already late – and the policyholder was likely already unhappy. While it is still effective in correcting dissatisfaction, it is just that: corrective, not preventative. Simply put, the care is given after the injury has already occurred, as opposed to a preventive check-up that identifies and begins treatment on an impending health risk.

AI and Proactive Customer Care

AI can change this reactive escalation scenario to preemptively improve customer engagement. For roadside assistance, by collecting data in the form of millions of breakdown event records, it becomes possible to move to a more proactive stance on policyholder experience. This includes analysis of both current and past events to identify, assess and weight the variables that can lead to delayed service, and the development of data models to predict impending issues. With added machine learning, the analytics can be trained and refined.

From here, it then becomes possible to identify the patterns that indicate an unexpected delay in service delivery is likely. But knowing what might cause a delay is only half the battle. With AI, that knowledge can be applied in real time to preemptively identify and escalate particular events based on how they are trending. Specifically, the AI can query and assess data in real time from external sources and internal systems, apply its predictive models and assign events as needed to the escalation team.

Most importantly, the tool can identify and escalate those at-risk cases early, before an arrival time is missed and often before the problem even becomes visible to motorists. When problems do occur, highly trained case managers can use this to proactively engage both service provider and motorist, instead of reacting after the damage is already done.

In that sense AI acts as not only a force multiplier – allowing a specially trained group to engage those targets that most need their services – it also enables a service (prediction) that was otherwise impossible given the speed and volume at which these events unfold.

And by anticipating policyholder needs, the AI actually creates an opportunity to delight a customer who now, rightly, recognizes that their event has been proactively singled out for additional attention. Similarly, it gives the organization the ability to directly and personally express that they care about the policyholder’s safety and satisfaction. Policyholders and vehicle owners respond in kind with compliments that frequently include the sentiment that the insurer has earned a customer for life.

Breakdowns are a hugely stressful event for policyholders, and everyone wants help to arrive as quickly as possible. Predicting when and where potential problems will occur during delivery of that assistance – and quickly recovering when they do – is critical to ensuring the best customer experience for motorists on behalf of our insurance clients.

That marriage of the predictive nature of artificial intelligence with the personal hands-on attention provided by trained case managers offers the ability to both anticipate and respond to the needs of policyholders. While it is always possible to give customers special attention when needed (which is what escalation is all about), AI’s ability to help insurers consistently get out in front of policyholder need promises to be a real game changer in proactively managing customer experience.

 

 

 

 

 


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