How many clients currently have full insurance cover? What sex are they, how old are they and where are they? What type of policy have they chosen and through which channels did they purchase it? Who are the best potential buyers of a specific cross-selling campaign? What are the characteristics of the most and least profitable clients? What is the geographical distribution of the premiums, in relation to the potential of the territory? Which clients seem less inclined to renew a policy? Which clients have the most accidents? And who is trying to cheat the Company?
To answer these and other questions we need to transform the mass of data in management systems - whose aim is the operative management of policies and accidents - into a structured group of synthesis information. With the new group, we can make the best marketing and fraud prevention decisions. From the comparison of the characteristics and different forms of our clients' insurance cover, we can draw out important conclusions for cross-selling campaigns and the development of new products. Similarly, by analysing the client’s historical behaviour in the Company, we can understand, in probability terms, which clients are highest risk and which ones are potentially fraudulent.