Case study Data science
Reclamation minimization

Predict customer reclamations with 80% accuracy

Predictive insights establish future performance and measure potential customer claims, avoiding critical losses and increased business profitability

Fortune telling with accuracy

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.

Our tools of wizardry

The analysis addresses the following questions:

  • Measurements, diagnostics, and repair of signal processing and radio frequency modules are currently time-consuming and expensive. Is it possible to cut costs by building a decision aid for diagnostic technicians that will shorten repair times?
  • What are the patterns in diagnostic measurement results that provide predictive insight into potential faults and future claim returns from customers?

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.


Classifier built which predicts future customer claims.

Process improvement recommendations provided.

Measurements optimally sequenced to reduce assembly time required.

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