Predicting customer claims with 80% accuracy

The challenge: costly product returns and hidden signals in diagnostic data

A global manufacturer of complex electronic equipment partnered with Proekspert to better understand and reduce costly customer claims.

Customer claims caused by faulty products were a major cost driver for the client. Each return triggered logistics, diagnostics, and repair work while also risking customer trust.

Large volumes of diagnostic measurements were already collected during production testing. However, identifying which measurements actually signaled potential failures was extremely difficult. Engineers relied on complex testing sequences and experience-based troubleshooting, but many weak signals remained hidden inside the data.

The client wanted to determine whether existing diagnostic measurement data could be used to predict future customer claims and improve testing and repair processes.

Our role: machine learning models for claim prediction

Proekspert partnered with the client to analyze production measurement data and build predictive models capable of identifying products likely to generate customer claims.

The project began with exploratory data analysis to uncover patterns in diagnostic measurements. Proekspert then trained multiple machine learning models using historical production and claim data.

Key responsibilities and deliverables:

• Exploratory analysis of diagnostic measurement datasets
• Data processing and modeling using Microsoft R Server
• Machine learning model development and evaluation
• Testing multiple modeling approaches including Random Forest, XGBoost, and neural networks
• Identification of predictive signals within large measurement datasets
• Development of predictive models forecasting customer claims

The resulting models were able to predict potential customer claims with approximately 80% accuracy based on production measurement data.

The outcome: deeper insight into product reliability

The analysis revealed that product failures rarely depend on a single diagnostic measurement. Instead, hundreds of small signals combine to create a reliable prediction of future claims.

By analyzing these signals together, the predictive models can identify products with a higher probability of failure before they reach customers.

The insights also revealed opportunities to improve testing workflows, showing which diagnostic measurements contribute meaningful predictive value and which tests can be simplified.

Impact for the client’s organization

The predictive models provide measurable business value:

• Early detection of potential product failures. Machine learning models predict future customer claims with around 80% accuracy based on production measurement data. 
• Improved testing and repair decisions. 
  Engineers can focus on the diagnostic tests that provide the most meaningful signals instead of relying on exhaustive measurement sequences. 
• More efficient quality assurance processes. Optimizing measurement sequences reduces testing time while maintaining reliable defect detection. 

Technologies

R, H2O machine learning framework, Random Forest, XGBoost, Deep Neural Networks, Microsoft R Server, SpectX log parsing, exploratory data analysis.