Being able to target those customers that are interested in buying policies from an insurance company and that are more likely to buy the product can lead to an increase on the conversion rate. This quote conversion dataset representing the activity of a large number of customers who are interested in buying policies from an insurance company
This dataset (from "Homesite Quote Conversion" Kaggle competition) contains historical data of 67,504 users and the outcome. Each QuoteNumber corresponds to a potential customer and the target variable QuoteConversion_Flag indicates whether the customer purchased a policy. For this competition, the organizers have anonymized the information in the columns. There are 56 explanatory variables, and they include specific coverage information, sales information, personal information, property information, and geographic information.
The objective is to train a ML model that returns the probability of a customer to accept the offered product. This is a binary classification task, therefore F1-score is a good metric to evaluate the performance of this dataset as itweights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously.
Although this dataset can make a huge difference on the insurance business' performance, it has some problems that complicate its usage. Luckily, Synthesized can solve these problems in a fast and intuitive way.
This dataset is available in "Homesite Quote Conversion" Kaggle competition.