An epic example of model deployment failure is from Netflix Prize Competition. In a short story, it was an open competition. Participants had to build a collaborative filtering algorithm to predict user rating for films. The winners received grand prize of US$1,000,000. In the end, the complete model never got deployed.
Not just Netflix, such dramatic events occurs in most of the companies. Recently, I have been talking with C-Suite professionals of many leading analytics companies. The biggest concern I hear about is 50% of the predictive models don’t get implemented.
Would you want to build a model which doesn’t gets used in real world ? It’s like baking a cake which you’ve tasted and found wonderful but would never be eaten by anyone.
In this article, I have listed down all the possible reasons which you should keep in mind while building models. In my career, I’ve faced such situations many a times. Hence, I think my experience could help you in overcoming such situations.
1. High amount of false positive : This might seem a bit technical. Here, it’s important to understand what is false positive?In a classification model, assume that we want to predict whether the customer is a responder (one who give answers) or a non-responder (one who doesn’t).
Imagine that you predict that a person X will be a responder but in reality he does not respond. Person X in this case is called a false positive. So how does this effect in real world ? I knew you would ask this question.
Let’s take an example. For instance, you have given the responsibility to build a retention campaign for 1000 customers. Out of these 1000, 100 customers will actually attrite (leave). You create an amazing model which has a 4X lift on top decile (10 equal large subsections).
This means, out of your top 100 predicted customers, 40 customers will actually attrite. So, you recommend the business to target all these 100 customers with a fascinating shopping offer which can stop them from attriting. But, here’s the challenge.
The challenge is that for every dollar you spend on each of these customers, only $0.4 get used to stop attrition. Rest $0.6 just go to false positive customers who were really not in a mood of attrition. This calculation will some times make these models less likely to be implemented as a result of negative P&L (Profit & Loss).
2. Low understanding of underlying models with business : Lately, there has been a rising requirement of using machine learning algorithms and more complex techniques for model building. In other words, companies are drifting away from using traditional models techniques.
Undoubtedly, using ML techniques lead to an incremental power of prediction, but the businesses are still not very receptive to such black box techniques. In my experience, this leads to a lot longer lead time for a predictive strategy to get implemented. And as most of the applications in business are highly dynamic, the model become more and more redundant with higher lead time.
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