Predictive insights establish future performance and measure potential customer reclaims, 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 that was able to predict future customer reclaim with approximately 80% accuracy.
The analysis addresses the following questions:
The methods used for the extensive analysis were the following:
1) Exploratory Data Analysis
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 weak ones that accumulate into one strong predictor.
Based on the existing data and metadata we were able to:
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