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Has anyone worked on Price Elasticity model at customer level? In Insurance Industry, insurance policies from a insurer that have been given a renewal offer in the period of 2 years are taken. Target Variable: (Binary) whether a customer renew an existing insurance contract or policy? I have read somewhere they generally use the treatment - the rate change: percentage change in premium from the current to the new rate, categorized into ordered values. My question - How price elasticity would be calculated give it's a logistic regression model (binary)? How can model help in finding the customers which are more sensitive toward policy premium changes?

Any help would be highly appreciated!

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Do something along the following lines:

  1. Estimate a logistic model in the form Pr(renew) ~ price + all other interesting variables. You most likely want to include various interaction effects, something like price*income, price*gender etc
  2. Now predict the acceptance rate by customer groups by increasing the price by, say, 10%. How much does the acceptance rate fall?

One can come out with an equation based on the estimated coefficients, but in this way it is perhaps easier to understand.

For the model to be correctly identified, there must be a large amount of randomness inside the groups, who gets offered which price. Similar people must face different price. If you rise/lower the price offer for all group members at the same time then it may not work.

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  • $\begingroup$ What's the coefficient way? $\endgroup$ Jun 16, 2021 at 17:58
  • $\begingroup$ You mean a single formula, based on the estimated coefficients? This will be messy, I am afraid. Why do you need that? $\endgroup$
    – Ott Toomet
    Jun 19, 2021 at 2:37

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