How a Fortune 100 Technology Manufacturer Reduced Support Engineers’ Search Efforts by 70%
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- Cortical.io Support Intelligence
Tech Stack
- Unsupervised Machine Learning
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Machine Learning
Applicable Industries
- Electronics
Services
- Data Science Services
About The Customer
The customer is a Fortune 100 Technology Manufacturer. They are a large-scale company with a complex networking environment. Their support engineers handle a multitude of support cases that often refer to complicated technical issues. The company's internal documentation uses specific terminology, which often differs from the language used by their customers. This discrepancy in language use further complicates the resolution of support cases. The company had previously attempted to reduce the time to find meaningful results with other search-based solutions, but these attempts failed to improve the support team’s productivity.
The Challenge
The support cases handled by the company’s support engineers were difficult and time-consuming to resolve because they referred to complicated technical issues in a complex networking environment. The fact that customers often used different terminology than what is used in the company internal documentation made the task even more difficult. Efficient handling of support cases was predicated on finding a solution from past cases instead of troubleshooting the issue from scratch. Attempts to reduce the time to find meaningful results with other search-based solutions failed, as they were not able to quickly and consistently identify similar support cases and did not improve the support team’s productivity.
The Solution
Cortical.io Support Intelligence was rapidly trained in an unsupervised machine-learning approach using support cases. The solution’s patented technology overcame the problems of language ambiguity and vocabulary mismatch by analyzing not just keywords but also the meaning of whole support cases, including customers’ written requests, engineers’ notes, email exchanges, and the meaning of sections of text from support documents, long or short. This allowed the solution to quickly provide the support engineers with the most applicable documents to the support case. As new material became available, it was ingested, indexed and automatically became searchable. Based on support engineer feedback, the system also continuously learned to assess the quality of the applicable documents.
Operational Impact
Quantitative Benefit
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