Predictive insights establish future performance and measure potential customer claims, avoiding critical losses and increased business profitability.
Customer reclaims stemming from shipping out items is a significant factor for all manufacturers. Therefore, minimizing the probability of customer reclaims is of major importance.
Based on test and reclaim data, we created a machine learning model able to predict future customer reclaims with approximately 80% accuracy.
The analysis addresses the following questions:
Our predictive models were suspiciously powerful with nearly 80% precision. A reverse causality case was uncovered: received claim cases undergo different measurement procedures. By removing the measurements that were done after a claim date, the picture became more realistic.
There are no obvious predictors of claims, rather hundreds of subtle ones that accumulate to form one strong predictor.
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