Data-Driven Business Transformation in Banking

This case study focuses on a bank's efforts to improve customer experience and operational efficiency through data-driven business transformation. The bank, which employs around 28,000 individuals and operates 1,000 contact center agents, faced significant challenges in managing customer interactions across multiple channels. With over 60,000 phone conversations and 3,000 to 5,000 non-voice interactions daily, the bank struggled to effectively analyze and understand these disparate data points, leading to poor decision-making based on inaccurate data. Specific operational issues included an unfavorable cost per contact, lagging self-service initiatives, slow resolution times, and reduced agent productivity. The bank aimed to leverage advanced conversation intelligence tools, specifically Call Journey Ci, to address these challenges. By implementing AI and machine learning capabilities, the bank sought to gain deeper insights into customer dissatisfaction, root causes of contact, and agent performance. This initiative aimed to enhance decision-making and ultimately improve both customer and employee experiences.