In a real insurance market, when you win some policies, you get some feedback.
From tonight onward you will be receiving this market-level feedback in your weekly email!
Note: remember that your model plays in thousands of markets each week (see evaluation metric).
1 Plot and KPIs
1.1 Plot: Profit Vs Market share
In this plot you see your profits and market shares plotted against each other for each market you participated in.
So what are the blue, yellow, and red dots?
We put all of your markets into 20 groups, from your most profitable (group 1) to your least profitable (group 5). The blue, yellow, and red dots represent sample markets from groups 1, 10 and, 20. In other words:
Blue dot. Randomly selected market from your 5% most profitable markets.
Red dot. Randomly selected market from your 5% least profitable markets.
Yellow dot. Randomly selected market from the middle 5% of your markets.
Note that these 3 groupings are quantiles and so each contains the same number of markets.
1.2 The KPIs next to the plot
For a description of how the KPI are computed please see this post.
2 The tables
You will see 6 tables below the plot that look like:
2.1 Table rows
First let’s understand what the rows mean:
- Policies won most often/sometimes/rarely: xx - yy%. Since your model plays in many of markets for the same set of policies, we can compute how frequently you win a specific policy. In other words, if you win the policy PLXXXXX in 20 out of 100 markets, then we can say you win this policy 20% of the time. We call this the conversion rate. We then split your policies based on their conversion rate into up to 3 groups of equal size from most often won to rarely won. The conversion rate range is represented by xx - yy.
- Policies won in sample profitable/average/losing market. These represent contracts won in one of the three sample markets represented by dots on your plot (see the section on plots).
2.2 Table columns
These represent summary statistics about the policies that you have won.
- For continuous columns: the mean value is displayed.
For categorical columns: the most common category is displayed. In parenthesis we also show the proportion of that label within the contracts represented by that row. So for example, if you see
M (55%)for the gender column on the
Policies won in sample profitable marketrow. That means that of the policies you won in your sample profitable market, the most common label was male, making up 55% of the contracts that you won.
2.3 Data within the tables
Dataset summary tables
The first 4 tables are related to the dataset features:
- Policy summaries
- Driver summaries
- Vehicle summaries
- Location summaries
Financial summary tables
The final two tables are related to the financial performance of your model.
Financials by conversion rate summaries. This table has 5 columns:
- Claim frequency. Fraction of contracts that made a claim
- Premiums. Mean value of premiums you have won.
- Conversion rate. The mean value of the conversion rate.
- Profit per policy. This is the mean value of profit per policy weighted by the conversion rate. To give you an idea of how profitable each segment is.
Profit margin. This is the mean value of your profit margin calculated as (
predicted expected claim) /
- Financials in sample markets summaries. This table includes detailed information about the 3 sample markets and should be relatively self-explanatory.
If you have any questions or comments please mention them here.
Good luck in the markets!