Insurance companies have launched new models that promise customers better benefits and prices by sharing their data. Insurance companies say that this makes their business more fair because the pricing can be tailored (e.g., with healthier life habits, you get lower prices). But this is only part of the story. Many companies also use that data to prioritize “good” customers at the expense of the rest of the customer base. That may not be such a good idea.
A couple of weeks ago, I attended a leading insurance business conference in London. People from insurance companies presented their innovations: nowadays, it is especially trendy to use more data to tailor products and pricing to customers.
One insurance company I talked to has done a lot of work giving wearables to their customers, asking them to share health and wearables data, and rewarding customers for healthier life habits. They have a three-level tier system depending on your life habits, and told me that their top-tier customers are much healthier and die less than similar people in the same age groups. The company representative wanted to demonstrate that the program encourages people to live healthier and has a real impact on their lives.
But again, it is only part of the full story. To get into this program, you must already have healthy habits (e.g., no smoking, regular exercise) and pass a doctor’s exam. So the program filters healthier people, with the healthiest ones naturally falling into the top tier. This means that it is not really the program that is helping you to live longer and healthier. Rather, it’s used to identify those people who are a good fit to the program.
Using data and analytics in insurance services
This kind of program is just one example of how you can use customer data in two different ways: (1) to deliver better services to all customers, or (2) to deliver better services only to those customers that are good for your business.
The second option is much easier to do, especially, if you are the first actor to harness significant analytics in a big market. For the first insurance companies offering services using health and wearables data, it’s a feasible option and offers opportunities to improve profitability.
But that also means that such companies only care about good customers. All insurance companies would naturally prefer to sell their products only to healthy, exercising, good drivers that have a well-protected home and no dangerous hobbies. Perhaps this is also the case in some business sectors – e.g., finance institutions would prefer only good low-risk customers.
But this is not practical in reality, if only because the base of “good” customers isn’t that big. The broader customer base is where the growth is, especially the large under-served markets. Most customers don’t live very healthily, have car accidents and live in riskier neighborhoods. And in the case of insurance, in many cases it’s required. If you drive a car, legally you have to get insurance coverage for it. If you’re an employer, you may be required to provide health insurance for all employees – including the not so healthy ones.
Ethical considerations of data
One aspect of this is segmenting the market and focusing on certain segments. This can be thought of as a simple business decision, but in many sectors it’s is also a question of ethics and equality. The insurance business is one example. All kinds of people should be able to get insurance for a reasonable price. Similarly, all people should have access to basic financial services, like opening a bank account.
There are also ethical considerations in using customer data. In some markets, there are limits on which data you can utilize in your business. For example, insurance companies refrain from using genetic information to decide who can get insurance and how much to charge them for it.
But these are decisions that are outside the control of an individual. This raises two important aspects regarding the use of data:
- People should really be able to understand how their data is used, for which purpose it is collected and have control over the use
- Businesses should put more effort into utilizing data to make better and fairer offerings for many kinds of customers and focus on how to improve each customer relationship.
Sustainable business models
People can (and do) argue that it should be up to the business to make their own decision on which segment to offer better service to, and that the goal of the business is to be profitable, not to be fair to everyone. And anyway, people pay premiums for better service all the time.
This is true as such. On the other hand, who says that fairness isn’t a good business model? Businesses that use data to choose which customers to serve better can also miss out on a lot of opportunities in the long run with a broader customer base that’s more loyal if the service quality is better. Also, fairer business models are typically more sustainable. Unfair models usually only last for as long as it takes to make quick money off the premium customers.
It should also be really crucial for individuals to be able to understand how their data is utilized, because they may not even know that it’s because of their data that they’re not getting good offers or good service. We can say that in some cases, some companies can actually use it against them. Maybe they are not accepted as “real” customers, or get much worse offers. This understanding requires much more concrete information than typical ultra-long data usage T&Cs that don’t offer any practical information to customers.
Customer wants to see the value
To achieve a more equal relationship where customers really understand how the data is used, the value to them in sharing that data, and the ability to stop sharing data requires giving customers much more practical control over their own data. For now, most of the data collection is quite invisible once the customers have signed the T&C.
Better and fairer relationships based on the data are better for consumers and businesses in the longer run. It also forces companies to think properly about offering higher value and better services if a customer shares data with them.
These things are hard to explain in T&Cs, but people learn by trial and error. If they share data with someone, and don’t feel it has any value – or suspect that it is being used against – they won’t want to share it with that business in the future. If someone really offers tangible value for customers that share some of their data or data analytics results, people will be willing to do it.