As financial institutions explore AI-driven technologies, understanding the return on investment (ROI) becomes essential. Artificial intelligence (AI) can streamline operations, enhance customer experience, and reduce costs, but to truly assess its impact, organizations need to evaluate specific metrics. In this blog, we’ll cover the essential metrics for measuring the ROI of AI in financial services, with an emphasis on improvements in First Call Resolution (FCR) rate, operational cost reduction, Average Handle Time (AHT), and other key indicators.
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First Call Resolution (FCR) Rate: Significant Improvement
First Call Resolution (FCR) Rate measures the percentage of customer issues resolved during the first contact with support agents. A higher FCR rate indicates efficient service, reducing follow-up calls and escalations. Improving FCR enhances customer satisfaction and operational efficiency by lowering agent workload and resource costs, making it a crucial metric for evaluating customer service effectiveness.
80% of expected increase in FCR rates can be achieved by financial institutions, including banks, NBFCs, and insurance providers, when transitioning from traditional manual processes.
Standard observed across clients leveraging Gnani.ai’s Automate365 solution shows notable improvements in First Call Resolution (FCR) Rate. This reflects positive outcomes, and by using this AI solution, you can expect similar improvements in efficiency and customer satisfaction.
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Reduction in Operational Expenditure (OpEx): Significant Decrease
Conversational AI offers financial institutions a powerful avenue for optimizing operational costs. By automating conversations, streamlining workflows, and empowering customers with self-service options, AI frees up valuable resources that can be redirected towards strategic initiatives.
70% savings in operational expenditure (OpEx) can be anticipated by integrating conversational AI. While the exact figures may vary depending on specific use cases and deployment scale, this benchmark has been observed across various clients leveraging Gnani.ai’s Automate365 solution, highlighting the significant potential of AI in driving cost efficiency.
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Reduction in Average Handle Time (AHT): Noticeable Decrease
Average Handle Time (AHT) measures the total time agents spend on customer interactions, with lower AHT reflecting quicker resolutions and team efficiency. Conversational AI improves AHT by automating repetitive tasks, providing real-time guidance, and suggesting responses, which enhances efficiency in both calls and chats, enabling faster, more effective customer support.
60% expected reduction in Average Handle Time (AHT) has been observed by organizations using conversational AI in customer support, leading to enhanced efficiency, quicker resolutions, and increased customer satisfaction.
This insight is based on the experiences of our customers, who have seen improved efficiency and reduced agent workloads with the help of Gnani.ai’s Automate365 solution. By using this conversational AI, you can also expect similar results.
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Reduction in Hold Time: Noticeable Decrease
Hold time refers to the duration customers spend waiting to connect with a support agent or receive a solution. It’s a critical factor in customer satisfaction, as prolonged waits can lead to frustration and negatively impact customer loyalty. Lower hold times are essential for enhancing the customer experience, as they create a sense of prompt, attentive service.
30% decrease in hold time is what clients can expect when utilizing AI solutions, leading to quicker resolutions and enhanced customer satisfaction. Conversational AI automates tasks, provides real-time insights, and uses intelligent routing to connect customers faster.
The results we share are based on the experiences of customers who have seen these improvements using conversational AI solutions like Gnani.ai’s Automate365. With the use of this technology, similar gains in efficiency and performance can be expected.
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Increase in Customer Experience (CSAT Score): Marked Improvement
Customer Experience (CSAT) Score is a metric used to measure customer satisfaction with a company’s products, services, or interactions. It reflects how well a business meets customer expectations, with higher scores indicating positive experiences and stronger customer loyalty. Improving CSAT is essential for businesses to foster lasting relationships and maintain growth.
30% improvement in customer experience has been reported by clients utilizing AI solutions. Conversational AI enhances this experience by enabling personalized interactions, faster service, and consistent support. It anticipates customer needs and resolves issues efficiently, leading to improved satisfaction and loyalty.
We base this on the results observed by our customers, who have achieved similar success with platforms like Gnani.ai’s Automate365. Expect to experience comparable improvements in efficiency and performance by leveraging this conversational AI.
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Improvement in Agent Performance: Noticeable Improvement
Agent performance refers to how effectively customer support agents handle customer interactions, resolve issues, and meet performance metrics such as response times, resolution times, and customer satisfaction. High agent performance is crucial for delivering quality service, maintaining customer loyalty, and improving overall business efficiency.
20% improvement in agent performance can be expected when leveraging AI solutions. In the competitive customer service landscape, enhancing agent performance is crucial. Conversational AI provides agents with real-time insights, automates tasks, and streamlines workflows, enabling them to focus on complex issues. Many organizations using AI solutions have seen increased efficiency and service quality as a result.
This performance standard has been achieved by numerous clients implementing platforms like Gnani.ai’s Automate365. By integrating this conversational AI, your team could achieve comparable outcomes.
Conclusion
AI-driven solutions like Gnani.ai’s Automate365 deliver significant ROI for financial institutions by enhancing key metrics such as First Call Resolution (FCR), reducing operational costs, and improving customer experience (CSAT) and agent performance. With improvements like up to 80% in FCR, a 70% reduction in operational costs, and a 30% boost in customer satisfaction, conversational AI streamlines operations, reduces costs, and enhances service quality. By adopting AI, financial institutions can improve efficiency, customer loyalty, and achieve long-term growth in a competitive market.