What is all the fuss? This topic was the focus of a recent panel discussion at an event in London, England. The buzz it generated was fascinating to me.
What is dynamic pricing? In simple terms, it is the use of various data points, weighing these factors as prices are set - such as the customer's income, buying habits, or the popularity of an item or service on a given day. Prices are aligned with the desire of a consumer to buy - ideally at the moment the customer wants it. For this to work, the company must gather data on its customers' use of the product or service in real-time, process the data and then set prices that adjust, also in real-time.
Dynamic pricing is… well, dynamic. By its nature the data available evolves and changes very quickly. The necessary expertise and tools to produce a pricing approach that effectively manages this to maximise pricing and profitability also evolves at the same rapid pace.
This type of pricing can be very attractive to consumers. The travel industry is a good example – particularly the cost of flights, which vary day to day, and even minute to minute, based on demand, seasonal patterns, and load factors. We all have used this, and have seen the impacts on the price for this year’s holiday by buying at just the right, or the wrong, time.
For insurance, a topical use of data to produce products and pricing which are highly tuned to the end consumer and their needs would be Pay-as-You-Drive, or telematics-based insurance. These products have become increasingly attractive to certain types of consumers – particularly younger high-risk drivers. The cost of entry for an insurer for these types of products remains quite high. Based on an interview with a major UK insurer who specialises in this space, this is a driving influence on how widespread this type of product can become – at least until the factory installed telematics units from some auto manufacturers are more widely available.
Some examples of other types of data which might be used in dynamic pricing are:
Personal credit histories – some insurers have developed pricing approaches which rely on this type of data to drive rating and terms for premium clients;
Company buying patterns and payment histories - various agencies provide data sets which will reveal a commercial enterprise’s buying patterns, payment patterns, and even provide management assessments;
Leveraging variable location data – if a risk is mobile, where it is taken can have a significant impact on the exposure;
Quote patterns – for example, assessing the mix of business on a given day through a channel (such as aggregators) and applying factors based on demand for certain products or against certain risk profiles.
And this all overlays the very valuable information the insurer holds on the client – and which is the most compelling data to use for managing pricing, and underwriting, to maximise retention.
What is all the fuss? Clearly insurers that can be agile in using variable data can improve underwriting quality, granular pricing of risk, and maximise the client relationship. As the UK personal lines market shows no signs of cooling any time soon, utilising all available data and tools to ensure that pricing and segmentation of risk is optimised is the best way to ensure portfolio profitability and retention.