Who remembers the television game show The Price Is Right in which contestants guessed the price of various items to win prizes? Corny as the format was it serves to illustrate a vital element of business success – you have to get your pricing model right.
The fundamentals of pricing lie in risk management. It’s all about understanding the risks a particular line or portfolio faces, measuring the potential impact and then considering how this might affect your capital position. For insurers, this then has to be translated into the premium.
While an insurer can usually get the price right it doesn’t always look that way to consumers. Sadly, there is little correlation today between the cost of manufacturing an insurance policy and the price the market is willing to pay.
The price of products has never been more visible. In the world of financial services the rise of aggregators has facilitated consumers’ ability to shop around and compare prices like never before.
Consumer research carried out by Mintel last year revealed that more than 26 million individuals now shop around when policies are due for renewal to find better deals.
Perhaps the financial services community hasn’t helped itself. In the benign economic environment of the dec- ade leading up to the credit crunch it was all too easy to lose sight of applying the fundamental disciplines of risk management to pricing models.
In the seemingly endless boom time businesses got caught up in the drive to win their share of customers’ increasing spending power. This led to rate reductions across many lines of business to win customers.
As a result, many financial products have become commoditised. This is particularly true of personal line insurance, and as a result combined operating ratios now often exceed 100%.
So if consumers are now entirely driven by price – as the evidence suggests – what can be done to achieve the right price to ensure a business can continue to drive volume without losing sight of the need to generate profit? Well, it all goes back to getting the pricing model right.
Traditionally, insurance pricing models have tended to look backwards to review behaviour of risk in the past to predict the future.
Taking the creditor insurance market as an example, rates have traditionally been set based on historical data sets. But in the present downturn the behaviour of consumers has changed. They are showing a greater propensity to buy as they become exposed to the risk of unemployment.
The behaviour of mortgage brokers has also changed. Once reliant on proc fees from mortgages they had to seek alternative revenue streams when the bottom dropped out of the housing market and so turned to selling general insurance products including accident, sickness and unemployment cover. As a result of these two previously unknown factors claims frequency has experienced a far greater increase than historical data dictates it should.
Nobody – even among the august ranks of world economists – expected this recession to dig so deep or last so long. It wasn’t that pricing models ignored the possibility of an economic downturn, rather that they lacked sophistication in terms of capturing and measuring potential risk.
As a result of changing buying patterns among consumers and mortgage brokers selling an increasing number of ASU deals, claims frequency has seen a far greater increase than historic data dictates it should
So there is now a need to look forward as well as back. We should not ignore facts based on historical data but it is essential to look for correlation of risk also and acknowledge the compound effect this could have on a portfolio of products in the future.
The importance of conducting sensitivity analyses can’t be stressed enough. It’s clearly important to project claims volumes based on risk scenarios identified and how this could affect return on investment. But it is equally important to think about the effect if these scenarios were 10% greater or 10% less than projected.
It’s not just a question of setting benchmarks and forgetting about them. Think of it as continually moving timeline with performance against these benchmarks being measured constantly to correct any imbalances.
Combining historic data with a clear view of what the future risks could be and the impact they could have together with constant measurement will lead to a more robust and sustainable model.
This model will be better placed to flex according to market conditions and a business’s own appetite for risk.
I think you will agree that’s is a better outcome, not just for insurers but also for the distributors they work with and their customers.
Building sustainable businesses
But what about the lack of correlation between the appropriate price according to risk and the price the customer is willing to pay? What can insurers do to avoid falling into the commodity trap?
To justify the margin we need to strike a balance with distributors and find ways to differentiate propositions.
To do this, we have to stop thinking like insurers and start thinking like solutions providers, developing activities that can be charged separately but that add value to both distributors and customers. In my opinion, this means showing we can boost distributors’ top lines, help them manage risk and increase the lifetime value of customers.
As I said at the beginning of this piece pricing is the key to the success of any business. But while the cover products we provide must be affordable for consumers there’s no getting away from the fact that they also have to be profitable.
If we fail to build accurate pricing models based on a sound risk management framework we will fail to build sustainable businesses, in turn failing our partners and their customers.