Problem Statement

In our journey at Squadstack, we encountered a significant challenge in maintaining exceptional customer interactions during telemarketing calls. We realized the need to evaluate callers based on various parameters to identify and flag undesirable interactions, this would become a robust solution and will help streamline the evaluation process and provide targeted training interventions to telemarketing executives.

Impact

The impact of poor caller interactions on our business performance and customer satisfaction became apparent. We understood that inaccurate or inadequate evaluations could lead to dissatisfied customers and damage our brand’s reputation. Addressing these challenges became crucial for us to deliver exceptional customer experiences and maintain a competitive edge in the industry.

Value

Recognizing the practicality and business impact of implementing a comprehensive ML-based solution for caller evaluation and training, we sought to leverage advanced ML techniques. Our goal was to improve the accuracy and speed of evaluations, allowing us to swiftly identify bad callers and take appropriate action. This approach would enable us to provide targeted training interventions and enhance the overall quality of interactions.

Outline for the talk

• The problem with traditional quality monitoring solutions for telemarketing
• Proposed architecture
• Deep dive into ML architecture or various quality evaluation parameters
• Feedback Loop
• How the architecture plays a crucial role in sync with the state of the art ML models.
• Impact generated
• Closing with future work