Home Insurance:
understanding buyer behaviour

To understand customer behaviour, the research environment needed to simulate the real-life buying process as closely as possible."

Our client, a major insurance company, needed to understand why the number of customers who chose an ‘upgraded’ home insurance product option once they had clicked through to buy from a Price Comparison Website (PCW), was failing to meet levels of expectation.

The company had several internal hypotheses that needed to be tested, including, pricing, cover options and whether there was in fact a behavioural economics issue in that customers were only being presented with a basic and upgraded level of cover rather than three options.

The Challenge

To understand customer behaviour, the research environment needed to simulate the real-life buying process as closely as possible.

We needed the research participants to be presented with the product variants, options and pricing that closely reflect the real-life scenarios.

A dynamic choice-based conjoint model was needed to generate realistic customer premiums based on property size and other risk factors. The model also needed to work with several product variants e.g. contents only cover and combined policies as well as a two-tier and a three-tier product.

Our approach

Conjoint model:
built

Consumers:
surveyed

Propensity to upgrade:
maximised

A choice-based conjoint model was embedded within a 15-minute online survey among 500 home insurance consumers who had used a PCW and switched insurer recently.

Screens were designed to resemble initially a PCW/aggregator site and then our client’s website.

The outcome

Immediate tactical recommendations were made to amend both the product features itself (in terms of cover, sum insured and excess) and enhance website layout to bring greater clarity to choices available, maximising propensity for customers to ‘upgrade’ from the core product to the more comprehensive product.

In addition, the conjoint simulation model provided our client with a powerful tool to use in future NPD.



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