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Consumer electronics leader detects, triages, and fixes emerging issues at launch
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Real Time Analytics
- Application Infrastructure & Middleware - Data Visualization
Applicable Industries
- Consumer Goods
- Electronics
Applicable Functions
- Quality Assurance
Use Cases
- Predictive Maintenance
Services
- Data Science Services
- System Integration
The Challenge
About to launch its first wearable technology product, this leading global consumer electronics brand sought to reliably monitor customer feedback for emerging issues. Given the company’s scale and the launch’s publicity, problems needed immediate detection and resolution. Most solutions the company reviewed required the painful process of manually building and categorizing inflexible lists of keywords or important terms. An effective solution would: Accurately identify, categorize, and label underlying intents in feedback; Quickly uncover and track emerging issues to monitor effectiveness of fixes; Offer support in multiple languages, including English, Spanish, Chinese, French, and Russian.
About The Customer
The customer is a leading global consumer electronics brand, renowned for its innovative products and large-scale operations. The company is about to launch its first wearable technology product, which is expected to attract significant attention and customer feedback. Given the high profile of the launch, the company needs to ensure that any emerging issues are detected and resolved promptly to maintain its reputation for quality and customer satisfaction. The company operates on a global scale, necessitating support in multiple languages to cater to its diverse customer base. The brand is known for its commitment to innovation and excellence, making it imperative to have a robust system in place for monitoring and addressing customer feedback.
The Solution
The brand used Luminoso CompassTM to classify, label, and analyze its incoming support tickets – ideal for acting on high volumes of streaming text data. Without keyword-matching or ontology-building, the solution was set up in under 10 minutes. After launch, incoming support tickets were pulled in and analyzed in near real time. As Luminoso assigned categories to incoming tickets, the team monitored which problems were mentioned most frequently, and quickly identified emerging issues. The team found unexpected insights. 'Cracked' and 'scratched' screen complaints, labeled as 'Repairs and Physical Damage', stemmed from damage in the shipping process, not user behavior. The group also learned that early customers were complaining about a 'sticking' or 'unresponsive' dial, an actual defect for some buyers, but also a counterintuitive design feature for others.
Operational Impact
Quantitative Benefit
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