Revolutionizing Customer Support in Real Estate: A Case Study on AI-Driven Transformation
The customer is a Spanish tech company that provides an app with three essential components: businesses that list their products on the app, consumers who order what they need, and couriers who pick up and deliver orders in under 30 minutes. They specialize in delivering products to your doorstep, offering on-demand services through their app to connect users with local stores and restaurants.
The Challenge: Limited QA Coverage in a High-Volume Environment
The company collaborates with external partners who provide customer service on behalf of their company in about 95% of cases, particularly focusing on written channels like chat and email. They were facing challenges related to the quality assurance (QA) of their customer interactions. QA evaluations were conducted for only a small percentage of interactions through these third-party vendors who assess sample cases to create scorecards based on the agent’s performance. Unfortunately, this traditional approach limited their QA coverage to a mere 2% of the millions of monthly interactions.
Moreover, their current manual QA process, which assesses agent behavior based on very limited factors such as politeness, was seen as another bottleneck for accurately understanding the effectiveness of their QA program to ensure service quality.. Although some reports exist, they are generated through spreadsheets linked to internal databases used to store customer conversations, offering limited new insights.
Objectives: Scaling QA and Automating Processes
Contact center leaders recognized the need for improvement and the potential of AI. They sought AI-focused initiatives to scale QA coverage, enhance understanding of customer conversations, and streamline reporting for business growth.
They were particularly interested in sentiment analysis, exploring the possibility of understanding both agent and customer sentiment. The goal is to find a solution that can automatically analyze customer interactions and the completion of scorecards.
The Next Level? Aiming for Comprehensive Insights
The business expectations were clear – they wanted to capture specific areas of opportunity that traditional QA processes with limited sample sizes couldn’t reach. The aim was to bring value to their QA managers by automating routine tasks and allowing them to focus on more strategic activities, such as value analysis and coaching. They were also eager to dive into customer intelligence and analytics to make data-driven decisions.
Level AI’s Solution: A Revolution in Contact Center QA
The customer found the perfect solution in Level AI, which used generative AI technology to automatically analyze 100% of conversations against their contact center scorecard with near-human accuracy. In addition, the solution offered customer intelligence and in-depth analytics, enabling efficient insights into customer interactions. The transition to Level AI allowed their managers and auditors to have a more tailored approach to selecting impactful interactions & streamlining the QA process while reducing the need for manual intervention.
The company’s envisioned future is brought to reality with Level AI by enabling them to segment performance data by regions, markets, channels, and more, facilitating comprehensive analysis through customer dashboards and strategic decision-making.
By leveraging Level AI’s generative AI capabilities, they have been able to enhance the quality of their customer service and drive informed decision-making based on comprehensive analytics. Level AI continues to be at the forefront of AI-driven solutions, helping businesses across various industries enhance their operations and achieve their goals.
Are you ready to transform your contact center’s QA process? Contact Level AI today to explore the possibilities!
