Corporate Transactions
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Full Credential Description
The client, a commercial bank, faced significant challenges in analyzing the effectiveness of its sales agents. Traditional performance analysis methods relied heavily on supervisors listening to sampled calls and using surface-level metrics, which were often subjective and generic. This approach was slow, difficult to scale, and failed to provide a comprehensive view of agent performance, leading to issues such as resentment among staff and high turnover rates. To address these issues, EY implemented a tailored solution that utilized advanced machine learning and natural language processing (NLP) techniques. The goal was to create a system that provided greater visibility into agents' activities, including outreach, conversations, and follow-ups, while uncovering behaviors that drive successful outcomes. EY curated a robust, fact-based comparative data set and operationalized insights from unstructured call data, linking it with structured data from customer relationship management platforms and performance metrics. The results were impressive. The bank experienced an uplift in opportunities of over 50% as a result of the insights generated. The implementation of an interactive dashboard allowed agents and team leaders to access real-time performance data, enabling targeted coaching and quantifiable improvements over time. This data-driven approach eliminated bias in performance measurement, strengthened the links between performance and incentives, and improved retention rates. Additionally, the enhanced visibility into agent activities facilitated better follow-up strategies and allowed for potential automation of workflows, ultimately leading to richer customer interactions and more tailored solutions. Overall, the project not only transformed the bank's sales operations but also provided a scalable framework that could be applied across various business functions, enhancing overall organizational effectiveness.
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