Abstract:

Eagle Star, one of the leading manufacturers of home insurance in the UK sought to develop a comprehensive understanding of the diversity of needs and benefits that consumers associate with their property insurance and to quantify what people were prepared to pay for their insurance. Moreover, we wanted to develop a predictive model that would help us understand the choices that consumers may make, in spending a discretionary income, given a new product design competing in an overpopulated marketplace. Our major problem was the lack of an effective research tool for the task. We chose the SIMALTO technique; it had all the fundamental attributes for the job, but needed some re-engineering. To the usual "attributes" and "levels" we added both "cost” and "saving for each component of the trade-off matrix. We then gave consumers incremental budgets to spend on their product that added a further component of "willingness to pay". Presented as a game it overcame the barriers to researching insurance and succeeded in generating dynamic, real data on product cover coupled with spending preferences and behaviour: and put the fun back into research! The process enabled the 'product attribute mix' and 'willingness to pay' to be optimised for particular segments of the market and different product concepts. A unique feature of this approach was the ability to simulate budgets to suit the individual's personal financial situation. Consumers' flexibility in being able to choose between spending their discretionary income and pocketing any savings was a particular innovation. 'Cash in hand' was identified as a clear indicator of risk aversion. The research highlighted the need for a radical repositioning of property insurance communication within the market. This was coupled with an opportunity to create the perception of product differentiation and competitive advantage through targeted communication. By correlating research survey data with client database records a key objective in delivering actionable findings to internal customers was achieved. This gave us a competitive edge in understanding how the consumer operates in the property insurance marketplace. In addition, the predictive and interactive model gave us an overview to the optimum product concepts for key markets and avoided the wastage of expense on abortive product designs. Finally, we succeeded in adding a new dimension to a unique tool: a new dimension to future financial product innovation research.