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we are creating models that aim to filter new leads from our current customer base.

We started to create propensity models that calculate a percentage for each customer for a certain product group. I was wondering what are best practices to benchmark such a model and to show the success of a propensity model in advance.

Thx for your replies!

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I'm assuming that the "propensity model" predicts a customer's likelihood to take some desired action.

If it's possible at your organization, then a good way to measure the model's effectiveness is an A/B test. Select a set of leads to act as a control group using a baseline selection method. Ideally, the baseline method would be whatever is currently in-place at your organization. Then allow your model to select a set of leads as the experimental group. Compare conversion rates (or whatever else you care about) between the control group and the experimental group.

If A/B testing isn't viable, then you can usually evaluate your model using the traditional techniques. You can split your customer base into train/test sets. Train your model on the training set, then made propensity predictions for the test set. Compare the propensity estimates with actual conversion rates.

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