Data science for Pet Insurers

Value vs price in the UK market

  1. Market pressures?


The current UK pet insurance market is completely price driven, and largely immune to seasonality apart from the transient ?puppy peak? in January. Incumbent insurers have a single-minded focus on acquiring customers who own puppies younger than one year, in order to put them in line for receiving their first claim.??


The impact on future quotes for the same pet, once they have put through their first claim, means that switching another providers is very unlikely for price reasons. The customer is locked-in.?


This makes it very difficult for new insurance providers who want to bring innovation to the market, since consumers have been taught by experience to only buy on price rather than value.?


Further to that, comparing on feature sets is often difficult as policy restrictions and wording are typically hard for the customer to navigate. Many just give up once they?ve found a price that?s comparable to last year?s. The cost in time and effort of continuing to search for a better premium outweighs the cost savings or convenience from finding a cheaper or more suitable policy. Information asymmetry here is unfortunately working against the customer.?


?2. Market trend


We?ve seen that for a new insurer, growing market share to compete on annual or lifetime policies is only possible through intelligently directed, acquisition marketing, or the application of data enrichment within pricing models, to allow competitive pricing in certain segments, where market share can be grown at a realistic CPA (cost per acquisition).?


3. Aggregators bring more change


One of the most obvious risks to pet insurers is the increasingly dominant position of aggregagators (price comparison websites) who increasingly own the relationship and therefore own the customer?s policy renewal date too.This is the position the insurer wants to be in, instead they must deal with an intermediary who charges increasingly high fees for supplying steady referral volume, which represents a considerable risk to the insurer?s business model.


4. Conditions for growth?


In brief, the ideal application of data science to solve the above problems comes in two parts.

Firstly, a data enrichment-powered dynamic pricing programme to identify segments of the highest-risk customers and thereby offering better rates to the rest of the customer base.

Secondly, the application of data science to marketing attribution, to allow sharp brand marketing ROI measurement, intelligently targeted acquisition marketing, and accurate predictive churn modelling.



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