Fortune 300 Car Dealership Enhances QA Efficiency with Observe.AI
Technology Category
- Platform as a Service (PaaS) - Application Development Platforms
- Sensors - Autonomous Driving Sensors
Applicable Industries
- Automotive
- Education
Applicable Functions
- Quality Assurance
Use Cases
- Time Sensitive Networking
- Visual Quality Detection
Services
- Testing & Certification
- Training
About The Customer
The customer is an international, Fortune 300 automotive retailer, founded in 1997 and headquartered in Texas. The company owns and operates 186 automotive dealerships, 242 franchises, and 49 collision centers in the United States, United Kingdom, and Brazil. The company's QA team was tasked with reviewing a high volume of calls and providing feedback to agents serving over 238 locations. The team was reduced from six to four analysts due to an unexpected workforce reduction, increasing the workload for each analyst.
The Challenge
The company, a Fortune 300 international automotive retailer, faced a significant challenge when it had to transition to remote work and simultaneously deal with an unexpected workforce reduction. The QA team was tasked with reviewing the same number of calls within the same timeframe but with fewer analysts. The existing QA process was slow and tedious, with analysts spending an average of one hour monitoring and evaluating a single call, collecting feedback, and sharing relevant recordings. This process involved juggling multiple spreadsheets and systems. The team was reduced from six to four analysts, each of whom was responsible for completing more call reviews and evaluations, as well as providing feedback to agents serving over 238 locations. The company needed a more efficient way to run its QA process, avoid overtime costs, and ensure that its agents received enough detailed feedback to deliver a positive customer experience.
The Solution
The company adopted Observe.AI's contact center AI platform to evaluate more calls and agents in less time. The platform expedited the process of finding the best calls to review and quickly surfaced highly accurate transcripts and interactions within those calls for deeper review. Analysts could access an audio player and scorecard in a single view and provide coaching tips to agents directly with a time-stamped transcript. This made the evaluation process more relevant and contextual for agents. The company also used the 'Moments' feature in Observe.AI to pinpoint key areas of interest in conversation for deeper analysis. This feature identified patterns like speech volume, speech rate, word use, and more that could indicate a good or bad customer experience. The AI platform made the process more accurate, data-driven, and transparent, enabling the company to build trust and transparency on its team while also better celebrating top performers to motivate them.
Operational Impact
Quantitative Benefit
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