Insurance is transactional. It's a grudge purchase with plenty of pain points.
As an industry, we have become much better at customer service. But deep customer relationships or client-centricity is a stretch.
And now the industry is on edge. Downward pressure on premiums worldwide. Fragmentation of products and channels. Tech designed to eliminate – yes, eliminate the intermediary.
It's a fun world we live in!
The Chartered Insurance Institute (UK) notes that 40 percent of brokers think their jobs are at risk due to tech. But tech can enable a rich future for UMA's and brokers – if we focus on a simple concept. How do we use tech to anticipate customer needs?
Viewed with curiosity, this might be the best opportunity that specialist and commercial insurers (especially UMA's and intermediaries) have to build competitive advantage.
Yes. Insurance products are becoming commoditised and the trend to encouraging clients to self-serve is necessary. But at the same time, the business operating environment and risk exposures – even for SMEs - are more complex.
The solution is summed up in US-based insurer Allstate's 2017 annual report, where the company revealed a multi-year effort to hone the expertise of its agents to act as "trusted advisors" to their clients.
So, in the near future, a customer could ask their digital assistant at home – a SIRI equivalent – to update their home and SME insurance at the best price.
Or the broker could pre-empt this – using a combination of data-based insights, risk assessments and relationship-based knowledge to predict need. It's up to the broker to know what matters to that customer – example: new risks like having more assets, possible water leaks or fire risks determined by connected devices (IoT) that feed information back to brokers, changes in succession planning and even, family structure.
Insurers will use big data for overall trends, product development and value chain re-configuration.
But the thing about big data is that it can help decisions at various levels – UMA's and brokers can capitalise by using more granular data.
The more you know the better. This is not just about identifying current risks or even, future risks (which predictive analytics will do). But knowledge about the clients' business culture, macro environment and other intangibles allow you to deliver better service and help the client feel secure.
Daniel Burrus is a strategic advisor to leading global insurance companies and author on tech innovation. He says that risk will not disappear – but "risk is shifting." For example, with autonomous cars, some risk liability can shift from driver to the manufacturer or the distributors of smart technologies.
There will always be a risk landscape. Intermediaries need to understand this new landscape. Identify where and how to create valued advice – not just product knowledge.
Clients may interact using apps, chatbots and even, avatars that can assess risk at mega-speed. But AI can't understand variances in context (social or political) or understand emotion (which is a critical differentiator in buying insurance products).
To quote a commentator on the future of chatbots: 'Natural Language Processing (NLP) is necessary for AI to properly understand context and there is some progress in algorithms designed to understand how a customer is currently feeling. But just because a chatbot can read that a human is upset, doesn't mean it can respond in a way that will help that customer feel better.'
So, for the foreseeable future, it will be the machine augmenting the human expert.
This is where predictive analytics combined with closer collaboration with the risk carrier is good for everyone.
Predictive analysis is literally the analysis of data to predict future events. It can aid in product development and business planning.
Data (even relatively limited data) and predictive analysis may just be the key to unlocking that elusive grail – deep customer relationships. You retain the customer because the customer trusts you! Now there's a change for the insurance industry. Probably one we can only whisper about at this stage. But a new world of tech-enabled and data-rich customer service is within reach.
The bottom line is that without data-centricity, we have no game. With it – challenging or not – there is the possibility of leverage.
The only question now is how do you access that data (and maybe befriend that algorithm)?
"The quicker you let go of old cheese, the sooner you find new cheese."
Spencer Johnson, Who Moved My Cheese?