Price optimisation can boost profits by half

It is well known that the competitive pressure induced by web-based aggregators has driven down first-year commissions in personal lines insurance.

This has spurred some brokers to optimise pricing structures – which has led to a boost in profits and volumes. The benefits can be dramatic: creating a simple though well-developed algorithm can increase customer value by up to half, depending on current pricing, the diversity of business and elasticity of customers.

Also key to profitability is product holding, payment type, renewal rates, cancellation rates, acquisition channel and claims ratio.

The value of customers will vary enormously. It is not untypical for a third of customers to create all of the profits, another third to have little effect and the remainder to be loss-making. In one company I worked, about 20% of customers contributed virtually all the profits.

The customer’s lifetime value must be modelled with consideration to cost categories. The biggest error is to model gross margin, which results in under-pricing.

Price elasticity must be measured by how customers are grouped. Customers should be defined by channel and stage in life cycle, as well as by parameters relating to what and how they are transacting with you.

Measuring price elasticities can be complex. You need to understand the effect of price on volume, and it is often difficult to disentangle this from other factors.

Be careful not to use sophisticated so-called price optimisation software that is based on conversion rates rather than price elasticity. This results in underpricing in areas of poor performance (and vice versa) without volume returns.

Price optimisation should lead to important decisions. A good model should be versatile, transparent and flexible. But over-simple models do not provide all the information to make good decisions. They may, for instance, show the effect on the coming 12 months but not on the lifetime of the customer. Over-complex models can be more dangerous; they can be opaque and lead to over-confidence. Pricing then becomes an act of faith in the model.

More often than not, 90% of the potential benefits can be achieved with simple solutions.

Behavioural economist Richard Thaler described the “winners’ curse” – where the winner in an auction offers the most money but ultimately lose out because they have over-valued the item.

Equally, when underwriters find they are writing too much of a certain type of business they increase prices, and rightly so. In a competitive environment, it is more than likely that the underwriter has under-estimated the risk.

The industry standard price optimisation software has been developed against this backdrop.

However, while the underwriter is right to reduce prices if they find they are getting too little business, the broker is not. This can lead to over-investing in consumers they will never attract or hold.

Accordingly, some brokers are using what appears to very sophisticated price optimisation tools but to the wrong effect. IT

Tim Ham is managing director of Pearson Ham, a price optimisation consultancy