SENTIENT Case Study

Automotive service provider uses historical repair order data to improve customer service

Customer

Automotive dealership that runs multiple service centers

Industry

Automotive Manufacturing

Function

Maintenance & Service

Problem

The invoice for repair orders is created by multiple technicians working on the same vehicle independently. Technicians use different language structures and make grammatical errors when they enter repair order and warranty data. Company wanted to use this unstructured data to gain useful insights that would lead to faster diagnosis of failures or faults, and to deliver faster and better customer service. The company had data from millions of such repair orders from which they wanted to find patterns. For example, finding the most common type of repairs done on specific models of cars or trucks.

Solution

Natural language processing (NLP) techniques were used to make sense of service order data coming from repair shops. The NLP model was trained using the service order data so as to be able to extract and understand the data. Next, a combination of unsupervised learning techniques and clustering techniques were used to obtain correlations and actionable insights. For example, the NLP model will tell what the most common sensor or part replacement for a particular model of a sedan is.

Result

Faster diagnosis for repair, improved customer satisfaction

More Use Cases

For the Manufacturing Industry

Automotive

Automotive service and repair provider achieves faster diagnosis and improved customer satisfaction

Food

Fruit producer ensures optimal quality of products during transportation and delivery

Electrical

Electrical equipment manufacturer saves $9.32 Million in Supply Chain Management

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Contact

Call : +966 54 312 3734

Email : info@unitx.tech

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