EXPERTISE / INSURANCE

The Insurance sector has given us many challenges we have been able to solve
challenge
1

Insurance: the cost of attracting customers

A renowned brand in the insurance sector wanted to quantify the impact generated by advertising on user searches.

Their objectives:

  • Quantify the advertising investment required for a customer to make a contact via phone, ask for a quote and visit the web.
  • Establish a methodology which would help them to accurately measure the number of leads generated by each TV spot.

Solution:

Econometric models were developed for each of the contact channels, in order to quantify the contribution of the advertising key drivers (offline and online) on each of the channels.

We applied Huff models in order to determine the probability that a lead is associated with one or more TV spots depending on the time elapsed between broadcast.

Actions carried out:

Using the econometric models, each media was analyzed in detail to determine their effectiveness in generating contacts.

From the probabilities obtained with the Huff models we assigned the leads to TV spots and we calculated the cost of obtaining a lead.

Results:

We quantified the contacts generated by advertising, distinguishing between campaign type, media and format used in each media.

We managed to assign the leads generated by each TV spot and thereby calculate the return on investment generated by spot.

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challenge
2

Insurance: What is the optimal premium I can offer to maximize my gains?

An insurance company came to us with the need to find the optimal premium to offer to potential customers, based on their characteristics.

The challenge was to find the optimal price with a view to maximizing company profits.

Solution:

With the classification of customers according to risk of default and considering the relationship between customers who take out and pay for the insurance, an optimization model was built.

Actions carried out:

First, the contracting process was analyzed to define the profile of people most likely to take out this insurance.

Through this analysis a relationship between customer behavior and the risk of default was detected

Various tests were performed grouping the default risk variable in different categories in order to find that one with which maximizing the gain could be achieved.

Results:

Thanks to the optimization model, we found the optimal allocation of premiums according to the risk of default so that the gain in euros was maximized.

In addition, we obtain the optimal strategic recommendation: offer higher prices to customers with greater default risk, being profiles which are less influenced by price.

Optimal premium

Optimal premiu
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