Technology Category
- Analytics & Modeling - Natural Language Processing (NLP)
- Cybersecurity & Privacy - Identity & Authentication Management
Applicable Industries
- Education
- Transportation
Applicable Functions
- Human Resources
- Quality Assurance
Use Cases
- Inventory Management
- Speech Recognition
Services
- Testing & Certification
- Training
About The Customer
The company is a talent management and training software company that helps organizations recruit, train, and manage their people. They handle a variety of requests, including product bug-related queries and account services. The majority of calls are inbound, but they also handle outbound calls for support case triage and follow-up. The company works with hundreds of the world’s largest companies—from Walgreens and Starwood Hotels & Resorts to Deutsche Post DHL and Western. Founded in 1999 and headquartered in California, the company is committed to delivering a great customer satisfaction score (CSAT) while meeting and exceeding its customer retention KPIs.
The Challenge
The company, a leading provider of talent management and training software, was faced with a significant challenge at the beginning of 2020. Following a major acquisition, the company needed to double its support team from 80 to 160 agents. This required preparing to train new agents on their process and service offering. However, before onboarding these agents, they needed to ensure their quality process was effective in identifying which agents required the most assistance quickly and at scale. The company's previous manual process was consistently seeing coaching drop on their list of support priorities due to support backlogs. This resulted in agents not knowing where they needed to improve or how support leads were making decisions. The company needed to create a more data-driven, fast, and scalable QA process to meet and exceed its customer retention KPIs while delivering a great CSAT.
The Solution
The company turned to Observe.AI to streamline their process and use AI to better identify opportunities for improved performance. With Observe.AI, 100% of their voice interactions were automatically analyzed and recorded. They also customized its dashboard to help the team understand how their actions were impacting major KPIs, including dead air, process adherence, and more. The company also leveraged the AI-driven ‘Moments’ feature in Observe.AI to pinpoint key areas of interest in conversation that should be analyzed on a deeper level, such as moments of strong negative or positive customer sentiment. This enabled the company to know who needed coaching and which top performers should be celebrated. Through its tonality-based sentiment detection, Observe.AI’s platform identifies patterns like speech volume, word use, speech rate, and more that can be indicative of a good or bad Customer Experience (CX).
Operational Impact
Quantitative Benefit
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