insights - are you even listening

Are you even listening?

Properly understanding peoples' needs is the route to long-term success, it’s too much of a risk not to.

Gailynn Nicks, Chief Product Officer

6 mins

As a product developer, I have spent decades across countless industries distilling down commercial problems before building back up to solutions. Almost always, this process begins and ends with understanding people. Truly, deeply understanding people. When we talk about understanding brands, what we really mean is understanding how people make decisions and choose brands. When we talk about successful advertising or content, what we really mean is how and why people remember and engage with that content.

All imagery AI generated.

Successful brands, ads and content all have one thing in common. They fulfil needs in a more desirable, effective or efficient way than their competitors do. Those needs are many, and the context in which people make their choices can vary considerably. I recall working with a mobile phone network to understand why people choose PAYG vs all-in contract payment options. It turned out that, in both cases, the need was to control expenditure. People with fixed incomes and outgoings used an all-in option to minimise variation, while people with variable income used PAYG to ensure that outgoings could be synched with income. Completely different processes and products serving an identical need.

Be it insurance, mobile networks, energy or other similar services, people rely on businesses to help them meet their needs (peace of mind, connection, warmth). People strive for simple outcomes even in sectors that are a minefield of regulation, process, pricing and risk. This presents a natural barrier to people engaging with certain sectors in the meeting of their needs, even when the cost of not doing so can be significant.

Insurance is risk management. But of what kind?

As a business it is about aggregation. The sum of all the potential bad outcomes, weighed against the sum of all the accumulated provision in anticipation of those outcomes. It is easy to forget that both sides of the equation can be changed significantly by small adjustments in individual actions or contributions. Understanding that requires us to deconstruct how people interact with risk itself. How humans engage with and manage risk is complex, involving not only cognitive brain function, but also the impact of deeply held values and beliefs.

If insurance is all about risk management, how do we decide if protection against a risk is worth it? We calculate risks based not only on our own experiences, but on how risks are framed. How is a risk communicated to us? How much do we believe or trust those communicating? How we interpret communication around risk is heavily influenced by our own values and beliefs. However much we like the idea that we objectively calculate and manage our own risks, in general we constantly compare ourselves with others: are we more or less likely than them to experience a problem? How much will we lose compared to them? How often?

Insurance is pooled risk. At its core, it’s a team sport. We attach a financial risk assessment to specific dangers so that those who, by random chance, fall victim to the danger are supported by the fortunate remaining team members.

Insurance is a form of economic “commons”. For a relatively small contribution we have access to a resource that, should the need arise, will compensate us for our bad luck. So, if as an industry we want to create change at the macro level, we must properly understand how to create change at the micro, or individual level.

How much we, as individuals, interact with risk changes the success or otherwise of the pool, but this is usually not obvious nor is the pool relatable for most. How much will our own behaviour change if we choose to share the risk with others vs. if we take that risk alone? To what extent do we think we take more risk or are better able to manage risk than others? Regulation allows discrimination based on higher risk where this can be proven based on a reliable source, but this is a fine line and begs the question of where the boundaries of the “pool” can or should lie.

Further, insurance is a sector now characterised by inherent distrust between buyer and seller. Insurers see some people acting fraudulently, people see insurers not paying out when claims are not clearcut. There is truth to both. Natural bias in our own favour forms part of the conflict, but asymmetry of information between insurer and insured is the root cause of many issues. Adverse selection, moral hazard and protection bias all play their role. How are these impacted by digitisation? The exponential increase in information available to insurers, the incremental use of big data models, IoT and AI is changing the balance of asymmetry of information.

This should create opportunities to maximise fair exchange of value, but that requires a lot of commitment from a lot of stakeholders. It feels easier to maintain the adversarial nature of the relationship but, while this might seem expedient in the short-term, there is huge regulatory and ethical risk in insurers simply taking the gain from that change. We can already see how the outcomes of insurer-centered algorithms dealing with fraud, pricing and offers of cover are ending up in court¹.

So, what is the answer?

There are many examples where a brand taking the initially harder path of ‘customer first’ has delivered competitive advantage and, sometimes, changed the trajectory of an entire sector. Orange (now EE) was the first mobile phone network provider to change from charging per three minutes to charging by the second for calls. At the time the industry issued dire warnings of revenue collapse, but consumers have a way of wanting more of what delivers fair value.

AI generated image of brain and cognitive function

Given the depth and diversity of risks we face today, and how much more risk averse societies are, it seems unlikely that an insurer thinking customer first will find themselves unable to find and profitably insure risks. Insurers should be thinking more carefully about using data to offer new and better cover (think of embedded insurance and micro mitigation behaviours), rather than thinking about how to create greater asymmetry of information.

Transparency goes a long way, both in revealing how individual data might be used to police fraud and in enabling people to mitigate their own risk. A number of technologies from the simple use of photos through to blockchain can facilitate this in contracts, claims etc. It is also possible to provide far more nuanced levels of variables like excess, exclusions etc. to fairly price the right risk for individuals. Resisting the urge to either exploit consumer weaknesses or judge them more harshly may be harder in the short-term, but comes with far greater long-term benefits.

So, in insurance, my focus returns to understanding people and defining the real problem of how individuals calculate and want to manage risk in their own, and others, behaviour. This provides the gateway to engaging people with the purpose of insurance in such a way that all can financially benefit. By participating fully in the “commons” or concept of pooled risk, consumers have less incentive to cheat the pool, and can also engage with more nuanced offers and active participation.

This way we can better leverage the myriad of data available to build a more ethical, less wasteful, approach to the future of pricing and risk in personal insurance.

We’re Lovethorn. We’re revolutionsing risk.

Reference

1 https://www.insurancejournal.com/news/national/2022/12/16/699788.htm

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